NUST Institutions Library Catalogue Search for 'an:"119524"'

Detecting Musculoskeletal Co-contraction for Ankle Rehabilitation through Variational Mode Decomposition in sEMG /

By Yasmeen, Sania . . 56p. 30cm.

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Comparative Analysis of UNET-Based and Transformer-Based Medical Image Segmentation Models on Lungs X-rays /

By Ghafoor, Muhammad Fazeel . . 66p. 30cm.

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Comparison And Correlation of Hamstring Reinforcing Exercise With Sprint In terms of Muscles Activity And Force Production /

By Habib, Adnan . . 44p. 30cm.

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Optimizing Feature Reduction and Selection Techniques for Surface Electromyography /

By Khan, Affaf . . 65p. 30cm.

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EMG Feature Reduction Technique For Optimal Accuracies /

By Abbas, Usman . . 94p. 30cm.

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Mobile Tablet Base Therapies for Cognitive Rehabilitation of Stroke Patients /

By Shad, Amna. . 66p. ; 30cm..

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Enhancing Gait Symmetry: The Impact of Prolonged Sitting and the Efficacy of Flexibility Exercises /

By Khan, Habib Ullah. . 62p. , Due to advancement in technology, we have seen a shift in the modern work environment. A notable surge has been seen in the percentage of people with jobs requiring prolonged sitting. There is a strong link between sedentary behavior and health risks down the road. Gait of a person is also linked to sedentary behavior which is believed to be significantly affected by prolonged inactivity. Gait asymmetry can be a very good indicator of overall physical health and that is why it used extensively to assess the physical health of subjects. Due to prolonged sitting, tightening of certain muscles, reduction in core strength and reduction in joint flexibility can lead to various Gait abnormalities. Our study is designed to compare the Gait asymmetry of physically active individuals with those of inactive individuals. We took into account various gait related features to find out the gait asymmetry between the right and left limb. We then also assessed the impact of exercise on gait symmetry and introduce flexibility training to the Sedentary group. Sedentary group performed Flexibility Training for 4 weeks every day. The exercises were performed thrice every day for 30 minutes. Results were substantial in supporting the link between sedentary behavior and increased gait asymmetry. Results also showed improvements in gait symmetry using flexibility training exercises. 30cm.

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Comparison of a Human Portable Blood Glucose Meter and Automated Chemistry Analyzer for Measurement of Blood Glucose Concentrations in Human Diabetic Patients /

By Asad-Ur-Rehman. . 46p. , Diabetes is the most prevalent chronic metabolic disorder in Pakistan which is characterized by an increased Blood Glucose level. An accurate diagnosis of diabetes is very essential for its early detection and treatment. Almost 90\% of the cases are of Type II Diabetes and the patients are required to continuously monitor their Blood Glucose level to maintain a near-normal range of Blood sugar. A practical way to monitor sugar levels is using a portable Glucometer at home which is faster and more convenient for diabetes patients. The purpose of this study is to compare Blood Glucose levels measured using Oxidase Method and a Portable Glucometer with reference Hexokinase Method in Type II Diabetes patients to determine if Glucometers can be used as a home-based selfmonitoring device. A cross-sectional study was conducted with a total of 150 Type II Diabetes patients. Blood samples were collected from the capillary of fingers for a portable Glucometer and from veins for Hexokinase and Oxidase Plasma methods after overnight fasting of 8 hours. Independent t-test and Pearson Correlation were used to check the significance of our results. There was no statistically significant difference in Blood Glucose readings obtained from Oxidase Method (128.31 + 61.4, p = 0.947) and Glucometer (122.53 + 59.6, p = 0.370) with Reference Hexokinase Method (128.78 + 61.2). A statistically significant positive correlation was observed in both methods with reference Hexokinase Method. Glucometer readings positively correlate with Hexokinase, showing that Glucometer can be used as a home-based self-monitoring Blood Glucose Device. 30cm.

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Effect of load and size on wear depth per cycle in Total Knee Replacement (TKR) /

By Ahmed, Hajira. . 97p. , The sole objective of this research thesis is to evaluate the effect of size and load on the wear per cycle in prosthetic knee joints. Prosthetic knee joints are abundantly used in the medical field to cater the deteriorating natural knees and help maintain a better standard of living. The prosthetic knees come in seven universal sizes and each one acts differently under same load conditions. Initially, seven different models developed on SolidWorks constituting the femoral and disk part. The parts were mated so that the femoral component can slide on the disk. Then, the wear analysis was carried out using Abaqus. Firstly, materials were assigned to the two components i.e., Cobalt Chromium Molybdenum Alloy (CoCrMo) to the femoral component and Ultra High Molecular Weight Polyethylene Disk (UHMWPE) to the polyethylene disk. Meshing was done and boundary conditions were defined to execute the whole analysis. The use of this method resulted in the calculation of contact stresses on the area of contact between the two parts. The contact stresses were calculated at 700N, 950N and 1200N for each size. The contact stresses were maximum at the edges due to small area concentration and minimum at the center attributing to the large area. Moreover, the maximum contact stress was utilized to evaluate the wear depth by utilizing Archard’s wear law. The wear depth was calculated for one gait cycle. The wear depths (𝑊d) for size 1 against loads of 700 N, 950 N and 1200 N were 26.131 nm/cycle, 31.706 nm/cycle and 37.253 nm/cycle respectively. The 𝑊d for size 5 against loads of 700 N, 950 N and 1200 N were 140.27 nm/cycle, 156.14 nm/cycle and 171.45 nm/cycle respectively. The 𝑊d for size 6 against loads of 700 N, 950 N and 1200 N were 23.060 nm/cycle, 28.193 nm/cycle and 33.307 nm/cycle respectively. Similarly, the wear depth was calculated for the remaining sizes. The results indicate that the maximum wear occurs in size 5 and the minimum wear occurs in size 6. The incorporation of Cobalt Chromium Molybdenum Alloy (CoCrMo) and Ultra High Molecular Weight Polyethylene Disk (UHMWPE) has greatly reduced the wear debris per cycle and allowed the knee joint to work properly which are quite evident from the results. 30cm.

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Design of Passive Controlled Iot Based Smart Cpm for Lower Limb /

By Shahnaz, Iram . . 42p. , Muscle strength and range of motion in the knee joint are impacted following total knee replacement and anterior cruciate ligament surgery. As a result, the goal of this research is to design a rehabilitative device to improve knee joint muscle strength and range of motion. The global population of people with mobility disorders is growing, which increases the demand for rehabilitation and physiotherapy devices. Automated assistive tools and devices have shown to be extremely effective and necessary in the treatment of physical injuries and impairments. Physiotherapists might use android technology to remotely set the degrees of flexion/extension, the pace of movement, and duration. The Android application will also save the patient's exercise history and can convey the patient's input about his condition's progress to the physiotherapist. The normal active knee range of motion is as follows: Knee Flexion ROM: 135 deg (completely bent), Knee Extension ROM: 0 degrees (totally straight). External Knee Rotation ROM: 30-40 deg, Internal Knee Rotation ROM: 10deg Normal passive knee ROM is as follows: Passive Knee Flexion ROM: up to 150deg, depending on leg size - the limit is the calf pushing towards the back of the thigh, Passive knee extension range of motion (ROM): up to 10deg hyperextension is deemed typical. 30cm.

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Analysis of post-COVID comorbidities in Pakistani population /

By Ansari, Sara. . 61p. , Introduction: Long-COVID, is the collective name given to denote persistence of symptoms for weeks or months in those who have recovered from SARS-COV-2 infection. If the relapsing symptoms are left undetected, can lead to chronic conditions. Still some work needs to be done to determine the correlation of the previous illnesses with the severity of post-COVID symptoms. Method: A questionnaire survey was spread amongst Pakistani population. About 83 COVID-19 survivors were included in the study, who were asked about any occurrence of symptoms at recovery, the experience and duration of the post-viral symptoms. Results: Out of 83 participants, 59(71.7%) experienced relapsing symptoms at 3 weeks from the onset of the viral infection, with 34(44.6%) having to face those symptoms for 3-4 weeks (postacute COVID). Females were significantly more likely to experience fatigue (p=0.014) and severity (p=0.032). The presence of symptoms was not associated with any therapy or activity. The presence of mild symptoms is common after the COVID-19 infection with those already suffering from anxiety, allergies, hypertension, and diabetes. Conclusion: This study highlights the importance of assessing those recovering from mild COVID-19 with acute-fatigue. Moreover, further longitudinal research in this area can help understand the management of chronic situations. 30cm.

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Emg-Based Force Estimation Using Deep Learning Models /

By Nayab, Maham . . 94p. , The estimation of force through electromyography (EMG) assumes paramount importance in diverse domains, including neurorehabilitation, myoelectric control, and neurofeedback systems. The intricate relationship between muscle contraction and force, characterized by linear associations in small muscles with narrow motor units and nonlinear relationships in larger muscles with wider motor units, underscores the complexity of this physiological interplay. Against the backdrop of a global demand for advanced technologies to address limb loss limitations, with an estimated 100 million individuals worldwide in need of prosthetics, there arises an urgent need for sophisticated solutions. Meeting the diverse needs of prosthetic users emphasizes the crucial role of EMG-based force prediction, striving to provide adaptive and personalized solutions for an inclusive and effective approach to limb rehabilitation. This comprehensive study explores the dynamic interplay between surface electromyography (sEMG) and intramuscular electromyography (iEMG) signals and force estimation. Leveraging a diverse set of machine learning and deep learning models, the research aims to predict forces in both healthy individuals and those with trans-radial amputations. Across sEMG and iEMG modalities, deep learning models, including Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and the hybrid LSTM-TCN, consistently exhibit remarkable efficacy. These models, boasting R² values surpassing 0.90 in force prediction, offer promising advancements in refining force estimation through electromyography. Notably, the TCN emerges as an exemplary performer, yielding R² values of 0.98 for able-bodied individuals and 0.87 for amputees in sEMG. Simultaneously, the hybrid TCN-LSTM model maintains strong performance with R² values of 0.98 for able-bodied individuals and 0.85 for amputees in sEMG. The LSTM model also upholds notable performance, showcasing R² values of 0.99 for able-bodied individuals and 0.80 for amputees in sEMG. Beyond unraveling the intricacies of EMG-based force estimation, this study sheds light on the unique challenges posed by amputations, contributing substantively to the ongoing quest for enhanced precision and effectiveness in rehabilitation interventions. 30cm.

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Classification of Activities of Daily Living (ADLs) Based Upper Limb Movements Using Machine Learning & Neural Network Classifiers /

By Anwer, Saba . . 68p. , Real time natural control of assistive, rehabilitation and prosthetic devices has gained significant importance over the last few decades for the people suffering from motor disabilities due to stroke, any spinal cord injury or amputation. Although surface electromyography (s-EMG) has been used as a viable controlling interface for several robotic devices specifically designed for post stroke therapeutic services. But these conventional controlling strategies are not feasible to design the rehabilitation or HMI systems based on simultaneous movements of multiple degrees of freedom (DOF). This paper presents a novel control strategy for HMIs which is based on the coupled use of EMG and inertial sensors. EMG and kinematic data of healthy and stroke subjects for commonly used daily life activities has been recorded. Multiple machine learning models including LDA, QDA, LSVM, QSVM, Fine KNN, Ensembled discriminant, and ensembled KNN have been applied. Besides this a tri-layered neural network classifier has also been implemented. A comparative analysis has been performed for the classification outcomes of all the applied models for EMG, IMU & EMG+IMU data. Overall, the KNN model performed well for all types of datasets with an average accuracy of 98.5% but results clearly demonstrated that average classification accuracies for all the applied models have significantly improved for EMG+IMU data which indicates that sensor fusion based control strategy for prosthesis can achieve higher performance than conventional control systems for each task. This study is an effort to provide a new EMG+IMU based technique for fast, efficient, and reliable control of robotic, rehabilitation and assistive devices for multiple movements with varying DOF. 30cm.

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Post-Discharge symptoms and analysis for COVID-19 patients /

By Shahjehan, Ayesha . . 65p. , Coronavirus was initially recognized as human COVID by researchers in 1965. Different strains of coronavirus appeared in the following years MERS, SARS-1. Thousands of cases were reported in individuals that to led many casualties, in 2019. New strains of coronavirus emerged, known as covid-19that started to spread from Wuhan, China. It was labeled a worldwide epidemic by the World Health Organization (WHO) in March 2020, the first since 2009. Patients with covid-19 experienced gentle to extreme indications like fever, cough, weariness, shortness of breath, migraine, loose bowels, nausea, and vomiting. As SARS cov-2 is a novel infection, initially the infected patients were treated in a single room along with the utilization of antiviral medication, including oseltamivir, ribavirin, and ganciclovir, lopinavir, and ritonavir to decrease the viral burden. Indications during the contamination may not resolve unexpectedly grumble about persistent side effects, even a long time after the disease. The research is based on observing the symptoms of COVID and postCOVID in patients who perform PCR tests at a hospital. Sample of 26 males and 34 females. Services were taken and their symptoms were noted during and after the quarantine. During the assessment of the covid-19 pandemic, it was seen that overall unexpected issues have gotten even after the onset of intensive covid-19. The prolonged aftereffect stays unexplained. The point of this examination is to represent the persistent symptoms in patients who were released from the health center and to explore the related element of danger. The impact of the study is fundamental in investigating the components and potential persistent post-COVID disorder. It presents a system of procedures for prognosis and handling of patients with suspected or affirmed persevering post-COVID conditions. 30cm.

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Classification of Reach and Grasp Motions from EEG Signals using Deep Convolutional Neural Networks (CNN) /

By Sultan, Hajrah . . 73p. , Classification of neural correlates of hand motions from EEG signals recently gained attention of researchers for the development of BCI systems for persons suffered from stroke, spinal cord injury who are not able to do voluntary movements or person with amputated arm or legs. Commonly LDA, SVM and K-NN models are used for the classification of hand motions, CNN and hybrid models are also used but most methods include the complex methods or preprocessing of EEG data and extraction of time or frequency domain features from the preprocessed signals which is a time consuming and lack flexibility because the EEG signals vary from human to human. In this thesis a Deep CNN model for end-to-end0learning of neural corelates for reach and grasp actions is introduced, aiming to increase rate of recognition & balanced classification0accuracy throughout all the subjects. A new model of CNN for movement classification is proposed that can also be used on the edge devices because of its smaller size for the development of BCI systems. In the proposed model separable convolutional blocks are used which reduce the number of parameters and hence the size of model also decreases. The dataset that is used for the testing of model is BNCI Horizon 2020 Reach and Grasp action dataset that is publicly available dataset. The dataset is also tested on 3 machine learning models LDA, SVM and K-NN are used, in which input is given in the form of time domain feature set the average accuracies achieved on these models are 60.77 (±3.80 STD), 66.73 (±2.86 STD), and 79.81 (±3.11 STD) respectively on the unseen dataset. Then the dataset is tasted on proposed Deep learning model along with DeepConvNet and EffNet models. The proposed model achieves the average classification accuracy of 92.44 (±4.13 STD), 92.9 (±4.23 STD) and 81.7(±5.68 STD). The model proposed achieves the same accuracy as DeepConvNet, but the size of proposed model is far smaller than the DeepConvNet model. Results shows that the proposed model shows the improved results with less variation of results within the subjects. Which will become helpful in the creation of real time BCI systems. 30cm.

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Human Placental-Derived Extracellular Matrix Sheets as Scaffolds for Cell Growth in Cornea Transplantation: A Promising Approach in Regenerative medicine /

By Khan, Unaiza Ali . . 121p. , Eye is a sensory organ designed for human vision. Its intricate components work together to make the process of sight possible. The cornea is a critical part of the eye responsible for clear vision, and corneal diseases or injuries can lead to visual impairment or blindness. However, the limited availability of suitable donor tissue poses a significant challenge. There is a significant influence on the quality of life when the visual acuity is reduced. In terms of the overall prevalence of blindness and visual impairment Pakistan ranks third position, following the India and Bangladesh across all age groups, totaling 21.78 million. Placenta-derived extracellular matrix (ECM) sheets have become an effective therapeutic approach due to their rich composition of bioactive molecules, growth factors, and supportive microenvironment for tissue regeneration. The unique composition of placental-derived ECM sheets can provide a favorable microenvironment for the growth of corneal cell and promote the regeneration of corneal tissue. In this study amniotic membrane sheets, have been prepared by decellularizing placental tissue and different characterization techniques have been used for a thorough examination of the human amniotic membrane. Scanning Electron Microscopy (SEM) reveals intricate surface features, while Hematoxylin and Eosin (H&E) staining provides insights into tissue architecture. Fourier Transform Infrared Spectroscopy (FTIR) offers a detailed examination of biochemical composition. Microbial activity testing provides valuable information of the membrane's antimicrobial properties. A p-value < 0.05 in the ANOVA analysis indicated a significant difference in antimicrobial activity among the three bacterial strains. The characterization approaches utilized in this study contribute to a betterxx knowledge of the biological characteristics of the human amniotic membrane, paving the path for advances in regenerative medicine and tissue engineering. In this study a human placental-derived extracellular matrix (ECM) sheets have been used to investigate the integration potential of the ECM sheets with host corneal tissue. The positive outcome was associated with a noticeable reduction in size of corneal defect due to the application of amniotic membrane transplant. The use of AM proved to be essential in reducing notable subjective symptoms like pain, as well as clinical signs such as redness and the size of corneal ulcers. 30cm.

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Decoding of Hand Motion Using State of Art Time Domain, Frequency Domain And Feature Extraction /

By Munawar, Sulaiman . . 69p. , Exoskeletons that are activated by the muscles and brain have been suggested to train the motor skills of stroke victims. Training can incorporate task variety since an exoskeleton allows for the execution of various movement types.Differentiating between movement types at the same time from brain activity is challenging, but it might be accessible from residual muscular activity that many patients retain regain.This study examines whether forearm EMG from five stroke patients can be used to decode seven distinct motion classes of the hand and forearm. This study evaluates classifiers like Support vector machine (SVM), Lineardiscriminant analysis (LDA) and K nearest neighbor (KNN). It investigated the relation of motor impairment with classification accuracy by the classifiers. During the following motion classes: Supination, Pronation, Hand Close, Hand Open, Wrist Extension, Wrist Flexion, and Pich, five surface EMG channels were recorded.Every motion was performed by patients three times repetition over the course of eight weeks.Support vector machines, k nearest neighbor, and linear discriminant analysis were used to classify decoding of hand moments for stroke patients. On average,73.69 ± 6.39%SVM,71.6 ± 5.09% KNNand 50±4.56 LDA of the movements were correctly classified.Seven motion classes were demonstrated to be decoded from residual EMG, and SVM proved to be the most effective classification method when compared to the other three classifiers for decoding of hand motion for stroke patients.The results of this study may have implications for the development of exoskeletons, suits, or gadgets, that are powered by EMG signals. These devices might be utilized in the comfort of the patient's home to assist stroke sufferers with their training activities. Therefore, the findings of this study may assist in improving the effectiveness and accessibility of these useful tools for stroke survivors. 30cm.

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Effect Of Emg Based Therapy On Stroke Patient /

By Gul, Adnan . . 51p. , Stroke is one the main cause of death around the world. Only in USA, someone dies of the stroke in every 4 minutes which is an alarming condition in health sector. The upper limb malfunction is a grouping of muscular feebleness decreased adroitness, non-coordination, loss of senses and abnormalities in motor interaction, that adversely affects the action of happenings of mundane livings. Many therapies are underway to treat the stroke patients. One of them is traditional way and the other is EMG based therapy. In this research, we focused on the EMG based therapy on stroke patients and its relativity with the traditional therapy. EMG based therapy was implemented on 06 subject and it was found that EMG based therapy was significantly better traditional therapies. There has been seen great improvement in the pre-assessment and post assessment tests. Majority participants have showed significantly improved scores in postassessment tests as compared to pre-assessments test. This shows great potential of EMG therapy in stroke patients. It was also observed after therapy that patients were slightly able to perform Activities of Daily Living such as eating, drinking, bathing, writing etc. Another major benefit for this therapy was it doesn’t require any specialization. Anyone at can home with some practice can perform EMG at homes. Another advantage of this therapy was that EMG device readily available and are available at very low cost. 30cm.

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Assessment of Stress Biomarkers in the Saliva of Smokers and Nonsmokers via UV Photospectrometry and POMS /

By Fahim, Maria . . 99p. , Smoking is one of the major health catastrophes. Smoking is believed to be the major cause of chronic diseases like Cardiovascular complications, stroke, pregnancy issues, respiratory failure, etc. There are three important transdiagnostic emotional factors that make the population vulnerable to initiation of smoking i.e. anhedonia anxiety sensitivity, distress tolerance. Research studies for the past five decades have proven the adverse effect of stress on brain physiology and functioning. The human body responds to trauma (physical or non-physical stress) in a definite manner. This response of the body can be qualitatively and quantitatively monitored through several chemicals in the bloodstream, saliva, or urine; responding to the stress, called stress biomarkers i.e. Brain-derived neurotrophic factor, cortisol, cytokines etc. Saliva delivers an efficient specimen for various diagnostic procedures due to the presence of different biological products and secondary metabolites of xenobiotics and helps in determining the disease progressions as well as therapy outcomes depending on the variations in the markers/triggers. The nature of mindset and mood states are evaluated by a scales designed to rate the behavior of an individual towards the environmental stimuli that may be physical or psychological in nature. This psychological rating scale is known as the profile of mood state (POMS). This scale was initially originated by McNair, Droppleman, and Lorr in 1971. This scale is presented in the form a questionnaire including different questions regarding the mode and feelings of a subject.This research work aims to further elucidate the utilization of UV Photospectrometry for quantitatively assessing POMS and its relation to the stress biomarker. The samples were collected form the vicinity of the university campus H-12 Islamabad. The samples were processed and stored at the biomedical laboratory of School of Mechanical and Manufacturing Engineering (SMME), NUST. A total of twenty-four (24) male subjects were analyzed. A total of two groups were considered. Group 1 included the non-smoking participants, while group 2 included smoking participants. Simple spitting technique was used for the collection of unstimulated saliva. About 4 ml unstimulated saliva was collected in the sterile falcon tube. Saliva was temporarily stored in cool boxes at 4°C and immediately II shifted to the facility. Centrifugation of the salivary sample was done at 4°C for 5 minutes and 10,000 rpm. Saliva sample was frozen at -80°C until sample collection span was completed. The mood state of the participants was also evaluated using the profile of mood state technique used initially by McNair, Lorr, and Droppleman in 1971. The total mood disturbance (TMD) score was calculated that ranges from -32 to 200. The questionnaire was accessed from ―Mackenzie, B. (2001) Profile of Mood States (POMS) [WWW] Available from: https://www.brianmac.co.uk/poms.htm [Accessed 26/6/2022]‖. Simulated neural networking (SNN) was applied to the collected data from smokers and non-smokers for accuracy scoring. The required statistical analysis was performed and the data was statistically analyzed through a software ―GraphPad Prism 8.0‖ and the respective graphs were plotted. UV spectrophotometry studies provided peak plasma concentration peaks at the lower UV range of 190 to 210 nm, but with no significant difference, representing the presence of biological stress markers. The profile of mood state evaluation studies concluded that the smoking participants were presented with a significantly higher level of individual mood profile scores i.e. anger (****, P<0.0001), confusion (**, P<0.0014), fatigue (*, P<0.0354), tension (*, P<0.0422) and stress as compared to nonsmoking participants. The vigorous score was significantly high in the nonsmoking individuals (****, P<0.0001). Similarly, total mood disturbance score was also significantly high in the smoking participants. The application of artificial neural networking through artificial machine learning scored the accuracy of the results 84% which is a reliable outcome. The current research work concludes that different stress stimuli including physiological stress and psychological stress tends to initiate/increase the smoking behavior among the community. Likewise, it is also concluded that smoking initiation may not be always triggered in response to stress. Numerous factors i.e. lack of education, negative inspiration, or behavior to impress are also involved. Furthermore, the adaptation of smoking behavior as a result of stressful stimuli is not a valid approach to reduce the noxious/stressful stimuli. The stress may further be exaggerated by smoking. 30cm.

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Design, Experimentation and Analysis of Mechanical Model using EMG signals for Hand Rehabilitation /

By Ajdar Ullah . . 33p. , Ageing and accidents all around the world are two of the main causes of disabilities. Even though it is quite expensive, rehabilitation is crucial for enhancing the mobility and quality of life of individuals with disabilities. For rehabilitation purposes, a variety of techniques are employed, including medication, homoeopathic remedies, and rehabilitative gadgets. Few hands rehabilitation gadgets are said to be a successful form of therapy, according to the literature. Due to developments, especially in the fields of robotics and artificial materials, the use of such devices without the assistance of medical professionals is expanding significantly. For hand disability, we have proposed the low-cost mechanical rehabilitation equipment EXOMECHHAND in this study, along with three distinct kinds of resistive plates. The therapist determines the type of resistive plate via Manual Muscle Testing. 30cm.

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Camera Based Eye Movement and EOG Detection to Control Mobility Assistive Device Using Graphical User Interface /

By Rashid, Anum . . 77p. , Researchers from all over the world have recently become increasingly interested in biobased human machine interfaces (HMI) for the assistance of paralyzed people enabling them to live an assistance free life. Among various approaches of designing a Human machine interface, eye signals are considered the most appropriate type of input. In general, eye-tracking systems assess a person's eyeball position and gaze direction and are classified into two approaches: electrooculography-based and computer vision based. This research uses EOG, and computer vision technique to predict which input method is more appropriate and user friendly for the mobility of an electric wheelchair. EOG data is acquired for four different eye movements i.e., right, left, upward, downward using BIOPAC. Video based data set is acquired using a webcam mounted at a fixed distance from the subject. EOG dataset is then processed and classified using eleven different classifiers among which the Decision tree shows the highest accuracy and F1 score i.e., 88.94 ± 13.82, 89.12 ± 13.58 respectively. The other data set of videos is processed using computer vision. Deep learning algorithms are used to classify the results. Both systems mentioned in this study have their own limitations. For EOG based system, the attachment of electrodes is a must requirement. This causes irritation to the user and sometimes generates motion artifacts which can be a source of hinderance for the motion of any HMI. For computer vision-based system, camera is a must requirement. However, it can’t be used in dark rooms, outdoor; during night times, wearing sunglasses and in similar other situations. For such situations, another alternative is an infrared camera, but prolonged usage of such camera can damage the eye. Therefore, a hybrid system should be developed which involves both techniques i.e, EOG and a camera which can effectively drive any mobility assistive device. 30cm.

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Progressive Rehabilitation in Stroke Patients Using EMG Controlled Exoskeleton /

By Khan, Ahmad Saadullah . . 58p. , The nervous system's interaction with other body parts and the environment to achieve desired and coordinated actions is known as motor control. It is a reflex and decision-based system that systematically regulates movement functions. If the contact is weakened or interrupted, a variety of neurological problems such as apraxia, tremors, and neurological and neuromuscular strokes may result. To improve hand function in stroke patients, new medical technology such as wearable gadgets and rehabilitative therapies are being developed. Because three-dimensional (3D) printing allows for the creation of low-cost, individualized devices, interest in applying this technology in rehabilitation equipment is developing in line with scientific discoveries. A novel electromyography (EMG)-controlled 3D-printed hand orthosis is demonstrated in this study. Force transfer is a major worry for these gadgets that are worn on the user's hand. The orthosis is designed to help stroke survivors recover their grip ability. As a result, active and passive devices can be utilized to perform a range of rehabilitation activities to regain or strengthen lost or compromised control while also improving strength, mobility, and motor conditions. Active devices are controlled devices used in rehabilitation to improve muscle function and restore appropriate biomechanics by providing stability, maintaining posture, and maintaining joint alignment. This device allows the wrist and fingers to move in specific directions depending on their degree of flexibility, allowing patients to do daily tasks more easily. Finger extension and flexion (hand opening and closing) and wrist extension are among the motions performed by stroke patients. 30cm.

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Deep Neural Networks for Ventilator Pressure Prediction /

By Asif, Ali Raza . . 55p. , Invasive mechanical ventilation is a common medical treatment required for applications spanning anesthesia, neonatal intensive care, and life support for the current COVID-19 pandemic. Despite its importance, the core technology of medical ventilation has remained largely unchanged for years. It is common for a clinician to continuously monitor and adjust the ventilator for a patient manually thus increasing workload. With machine learning (ML) taking center stage in healthcare recently, the question has been asked whether ML based control methods can be developed to replace manual intervention. The main challenge however remains the robustness, safety, and the high cost of development. In addition, the controller must be able to adapt reliably and quickly across varying clinical conditions and requirements not observable directly to the clinicians. Proportional-Integral-Derivative (PID) controllers have been to go to method of choice because of its limited parameters size, fewer samples for tuning, and its ability to generalize over the dynamic lung conditions. Current ventilator or patient simulators are trained by an ensemble of multiple models each simulating the parameters of a single lung. However, human lungs and respectively their parameters or attributes are dynamic and form a continuous space therefore, based on the physiological differences in patient lungs a parametric approach is better suited to improve generalization. This work centers around the possibility of developing an ML-based method which can improve performance simultaneously across a wide range of lung parameters based upon the ISO standard for performance of ventilatory support equipment (ISO 80601-2-80:2018). The results are compared against previously published data and closely match the expected outcome. This is a significant improvement towards a more robust alternative to PID tuning and more importantly cost-effective mechanical ventilators. 30cm.

Place Hold on Deep Neural Networks for Ventilator Pressure Prediction /

Evaluation of Techniques Used to Estimate the Effect of Gait Training on the Rehabilitation of Gait Pattern in Hemiplegic Stroke Patients /

By Tariq, Sidra . . 45p. , Many stroke survivors usually suffer from hemiplegia as stroke is considered to be the leading cause of disability in long term. Hemiplegia is associated with deformities and gait disturbances, which may cause difficulty in walking and there is an increased risk of falls. While many studies have shown that ankle foot orthosis (AFO) is usually frequently prescribed to correct deformities and it improves gait speed in hemiplegic stroke patients but the effect of combination therapy which consists of balance exercises and AFO remains unclear. This study aimed to analyze the effects combination therapy consisting of balance exercises and orthotic treatment on gait parameters. RCT was conducted on thirty two 32 chronic stroke patients (n=32; age from 40-60 years; duration of stroke: 12-18 weeks). The patients were divided into two groups i.e. control group and experimental group. Each subject in the control group received balance exercises for 4 weeks and each patient in the experimental group received combination therapy consisting of balance exercises for hemiplegic lower limb and gait training was done with rigid ankle foot orthosis (AFO) for 4 weeks. The Activities Specific Balance Confidence (ABC) Scale, Timed Up and Go Test (TUG) and the 10 Meter Walk Test as a measure of functional ambulation were evaluated before and after the combination therapy. Results showed that there was a marked improvement in the findings of Timed Up and Go (TUG) Test, 10m Walk Test after the intervention in the experimental group. Combination therapy which consisted of balance exercises and orthotic treatment is therefore more effective than the balance exercises alone in the improvement of gait of hemiplegic stroke patients. 30cm.

Place Hold on Evaluation of Techniques Used to Estimate the Effect of Gait Training on the Rehabilitation of Gait Pattern in Hemiplegic Stroke Patients /

Analyzing and Decoding Natural Reach & Grasp Action Using Convolutional Neural Network /

By Nazir, Abida . . 44p. , Reaching and Grasping is most signi cant component of human life.Translation of EEG in the form of upper limb movement is of great importance for realization of natural neuroprosthesis control and restoration of hand movements of patients with motor disorders. Patients su ering from spinal cord injury (SCI)problems have lost most of voluntary motor control functions. Such type of loss can be cured using movement related cortical potentials (MRCPS) analysis. Brain computer interface with limb neuro-prosthesis is considered as a solution to such problems. This study anlyzes EEG signals in relation with natural reach and grasp actions. EEG signals have movement related cortical potentials (MRCPS) which can be used to decode upper limb movements. This experiment was performed in Graz University of Technology Austria and they o ered free access dataset for further exploration.Total 45 subjects were involved in this study, 15 subjects with every type of electrode:gel,water and dry performed the experiment. All subjects accomplished self-initiated 80 reach and grasp actions toward a spoon within the jar (lateral grasp) and toward an empty glass (palmar grasp).EEG signals are recorded using three types of electrodes: water based, Gel based and Dry electrodes. In this study signals are classi ed using Deep learning technique i.e Convulotional Neural Networks. For analysis, EEG signals were preprocessed using various lteration techniques. After ltration data is fed into classi er for classi cation of signals. Data is divided into test set and training set. Grand average peak accuracy calculated on unseen test data resulted in 54.2% classi cation accuracy i.e Gel based accuracy approached 56.8.4%, water based 52.7% and dry based 51.8%. 30cm.

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Design & Development of 3 Degrees of Freedom (DoF) Real-Time Game for Post-Stroke Rehabilitation Using Fitts’ Law & Pattern Recognition /

By Imran, Urwah . . 98p. , This research addresses challenges in stroke rehabilitation by proposing an alternative to current methods that are often costly and resource-intensive. With limited accessibility to rehabilitation interventions due to constraints in skilled therapists and advanced technology, there is a need for innovative solutions. Past research predominantly focused on 2-DoF systems, limiting exploration in diverse multiple DoF EMG classification systems. The initial phase of this study involves a comprehensive investigation into the potential of user-specific non-functional hand movements. By examining 16 distinct wrist and finger motions using five classifiers (Wide Neural Networks, Ensemble Bagged Trees, Cubic SVM, Fine KNN, Logistic Regression Kernel), significant differences were revealed. One-Way Anova testing (p<0.05) revealed significant classifier differences while Wilicoxon signed-rank tests (p<0.05) distinguished individual finger and wrist getsures. For the development of a 3-DOF real-time game, specific hand movements were strategically selected based on the best obtained accuracies. Noteworthy movements include "Abduction of thumb and flexion of others," "Flexion of all fingers," "Extension of index and flexion of others”, “Wrist Radial Deviation (Clockwise)”, Wrist Extension”, and “Wrist Supination Axis: middle finger.” A MATLAB APP DESIGNER-based GUI was developed with two tabs. Real-time metrics for the designed game exhibited promising results: a completion rate of 94.22% (±2.74%), throughput of 0.40 bits s−1 (±0.03), path efficiency of 87.02%, and minimal overshoot of 12.68%. These findings underscore the game's potential as an effective and engaging tool for stroke rehabilitation, offering a novel approach to address the challenges associated with current methods. 30cm.

Place Hold on Design & Development of 3 Degrees of Freedom (DoF) Real-Time Game for Post-Stroke Rehabilitation Using Fitts’ Law & Pattern Recognition /

The Effect of Virtual Reality-Based Rehabilitation on Hand Motor Function and Activities of Daily Living Performance in Sub-acute Stroke Patients- A Randomized Control Trial /

By Syed, Sania . . 85p. , Background: This randomized controlled trial aimed to investigate the efficacy of Virtual Reality (VR) games compared to Conventional Physical Therapy on Hand motor functions, activities of daily living, and quality of life in subacute stroke patients. Method: Forty stroke patients who met the inclusion criteria were randomly assigned to either the experimental group receiving VR games or the control group undergoing traditional physical therapy interventions. Outcome measures included the Fugl-Meyer Assessment for Upper Extremity (FMA-UE) to assess motor function, the Action Research Arm Test (ARAT)to evaluate functional performance, the Box and Block Test (BBT) to assess hand dexterity, the Modified Barthel Index (MBI) to measure ADL performance, and Stroke-Specific Quality of Life (SSQOL) to measure quality of life after stroke. Results: No differences were observed in patients' demographic and clinical data at baseline between both groups. Statistical analysis revealed significant improvements in all outcome measures for both groups post-intervention. However, the experimental group exhibited notably greater improvements in hand motor function, functional ability, hand dexterity, activities of daily living (ADLs), and quality of life compared to the control group (p<0.05). Specifically, in the follow-up week, the VR games group continued to demonstrate sustained improvements, surpassing the improvements observed in the physical therapy group. Conclusion: These findings underscore the potential of VR-based interventions as a promising adjunct to traditional therapy in enhancing hand motor function and overall quality of life in patients with motor impairments. 30cm.

Place Hold on The Effect of Virtual Reality-Based Rehabilitation on Hand Motor Function and Activities of Daily Living Performance in Sub-acute Stroke Patients- A Randomized Control Trial /

A Comparative Analysis of Different Features for EMG Signal Classification /

By Ikram, Zainab . . 72p. , Electromyography (EMG) signals serve as vital tools in neurological and neuromuscular conditions diagnosis. Various features are used as inputs for pattern recognition algorithms. This project intends to increase the precision and efficacy of prosthetic limb control, with the goal of boosting the quality of life for individuals with limb amputations, using a Linear Support Vector Machine technique. Specifically, we intend to analyze the usefulness of the distinctive feature known as Cardinality within diverse combinations of time-domain and frequency-domain features. In order to improve signal quality, the raw EMG signal is filtered and segmented. The time-domain and frequency-domain features are then retrieved from overlapping segments, and the most relevant ones are retained using exhaustive feature selection. An SVM classifier is then used to examine the possible impact of Cardinality on prosthetic control and rehabilitation outcomes. The research findings show that the efficiency of Cardinality is dependent on the precision of the units used. Cardinality performed best when seven decimal points are used. MAV stands out among time-domain features, as it generated a high number of combinations with Cardinality, enhancing its performance in myoelectric pattern recognition and BP emerges as the top-performing frequency-domain feature when integrated with Cardinality, surpassing other frequency-domain features and forming the most numerous combinations. The SVM classifier achieved classification accuracy of 85.58% of M1, 70.49% of M2, 77.32% of M3, 77.24% of M4, 80.82% of M5, 77.52% of M6, 82.94% of M7, 84.34% of M8, 84.75% of M9, 86.92% of M10 for the combination of Cardinality with MAV and BP. As advancements in prosthetics and rehabilitation technologies continue, the insights gained from this study can play a pivotal role in refining the precision and efficiency of Myoelectric Control systems, ultimately benefiting individuals with limb loss or motor impairments 30cm.

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Comparative Analysis of Machine Learning Techniques for Phonocardiogram-Based Classification of Cardiac Abnormalities /

By Zaheer, Iqra. . 79p. , Cardiovascular Diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, necessitating early and accurate detection for effective disease management. This work employs advanced signal processing techniques in conjunction with machine learning methodologies to classify normal and particular cardiac conditions—Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP)—using phonocardiogram (PCG) signals. Preprocessing involved denoising using the Discrete Wavelet Transform (DWT) technique with the db8 wavelet and cA2 component, optimizing noise reduction while retaining valuable features for further analysis. Feature extraction was performed using Mel-Frequency Cepstral Coefficients (MFCC) and Mel Power Spectrogram, providing a robust and efficient representation of heart sounds. Two machine learning models—Deep Neural Network (DNN) and Convolutional Neural Network (CNN)—were used to assess the extracted features. With three hidden layers and 80% of the dataset used for training, the DNN model produced 90%±0.37 accuracy, 89% sensitivity, and 91% specificity. On the other hand, the CNN model, which consists of two fully connected layers and two convolutional layers with max pooling, performed by achieving 96%±0.38 accuracy, 95% sensitivity, and 95% specificity. These results underscore DNN’s enhanced capability in handling complex PCG data and reducing false negatives. This comprehensive study addresses multiple cardiac abnormalities, surpassing previous research that often focuses on a single condition or model. The findings highlight the potential of combining advanced signal processing with deep learning techniques to improve the timely and accurate identification of cardiac abnormalities. Future research will explore additional feature extraction methods and larger datasets to further enhance classification performance. This work significantly contributes to the field of biomedical engineering, offering a framework to improve patient outcomes through advanced diagnostic techniques. 30cm.

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Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning /

By Nayyab, Rida. . 81p. , Cardiovascular diseases are considered the major cause of death worldwide surpassing cancer. However, despite the broad category of diseases, research has been limited to binary classification i.e. normal and abnormal class leaving behind the accurate classification of specific diseases that affect the ECG waveform. PTB – XL database offers a wide variety of ECG records, but little research is dedicated to extracting morphological features for multi-class classification. Therefore, the paper used the open database to filter the ECG signal records having single unique labels and pre-processed them using the Butterworth bandpass filter and DWT db8. The Bandpass filter corrected baseline wander and reduced noise however, a high signal-to-noise ratio was achieved after applying 8-level DWT. The processed signals were fed into the Pan-Tompkins algorithm to extract R peaks. These peaks served as a baseline to identify other morphological features i.e. P-QRS-T intervals and amplitudes. These extracted features were labelled into 1 normal and 4 abnormal classes. There was a class imbalance in the dataset that could cause bias while training models. Therefore, SMOTE-NC was applied to upsample the dataset. The new dataset was split into the training set and the testing set. These sets were given as inputs to CNN and DNN models for a 5-fold loop. The performance was evaluated for both models using metrics like F1 score, recall, precision and accuracy. The CNN model achieved a mean accuracy of 81% whereas the mean accuracy for DNN was 84%. It was also noted that among the 5 classes, HYP was consistently being classified accurately at 98%. 30cm.

Place Hold on Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning /

Enhanced Electromyogram Signal Denoising Using Canonical Correlation Analysis Informed Variational Mode Decomposition /

By Mahmood ,Saad . . 59p. ; 30cm..

Place Hold on Enhanced Electromyogram Signal Denoising Using Canonical Correlation Analysis Informed Variational Mode Decomposition /

Leveraging Deep Neural Networks and Surface Electromyography for Real-Time Airwriting Gesture Recognition /

By Saeed, Atiqa . . 70p. , Airwriting enables users to write letters or characters in free space using hand or finger movements, with potential applications in human-computer interaction, virtual reality, augmented reality and development of assistive technologies. Despite advancements in gesture recognition technology, dynamic airwriting faces challenges with accuracy and often lacks real-time capabilities, limiting its application in non-verbal communication and rehabilitation devices. The primary objective of this research is to develop a novel real-time deep learning-based framework for airwriting recognition using surface electromyography (sEMG). This study presents a technique for real-time identification of uppercase English language alphabets written in free space by analyzing the electrical activity of forearm muscles involved in writing letters. The proposed framework involves sEMG data collection from 16 right handed healthy subjects with no neuromuscular or motor impairments, signal preprocessing, feature extraction, classification using Convolution neural network (CNN), Deep neural network (DNN) and Recurrent Neural Network (RNN).The best performing model was implemented in real-time and it was evaluated using performance metrics such as accuracy, precision, recall, F1 score, Confusion metrics and latency. Results show that 1 Dimensional (1D) CNN outperforms other models (p<0.05) and achieved an offline test accuracy of 89.81 ±0.87% and an average real-time test accuracy of 73.71 ±8.46% across subjects. The individual model of each subject performed even better, with an accuracy of 90.01 ±2.85% on offline testing of data and 75.45 ±1.53 % in real-time alphabet prediction. Thus, this work highlights the potential of deep learning models for real-time airwriting detection and provides foundations for sEMG-based airwriting applications in healthcare and telemedicine. 30p..

Place Hold on Leveraging Deep Neural Networks and Surface Electromyography for Real-Time Airwriting Gesture Recognition /

HemoSys: An Integrated Framework for Hemodialysis Machine Operation, Real-Time Monitoring, Data Management and Predictive Modeling for Patient Safety /

By Ali, Zumair . . 91p. , Hemodialysis is of paramount importance to patients who undergo renal failure and kidney dysfunction. It requires precise control and monitoring to ensure the safety of the patient and the efficacy of the process. HemoSys is a high-end framework for the revolutionized operation of hemodialysis machines by real-time monitoring, data management, and predictive modeling. The system will answer all diverse needs related to hemodialysis treatment. It has highly developed peristaltic pumps and syringe pump controllers to manage blood and dialysate flows, assuring cautious fluid management. Advanced sensors are integrated for real-time monitoring of pressure, temperature, and bubble formation that support the continuous tracking of critical parameters of treatment. The Graphical User Interface enables users to easily interact with real-time visualization of sensor data and offers intuitive controls for the hemodialysis operation. Other than the operational capacities, several strong data management features are hosted by HemoSys. All patients, operators, physicians, and sessions data get stored in a MySQL database, thereby providing an efficient retrieval and update of data. The system also offers export options to PDF and excel, thereby enhancing data accessibility and usability. One of the unique features of HemoSys is predictive modeling incorporated in the software framework. In this research, Random Forest, XG Boost, Cat Boost and Light GBM were employed, trained on the hemodialysis patient data of 10352 dialysis sessions, to predict the adequacy of hemodialysis session based on all the relative variables instead of only relying on few variables like Kt/V, Ultrafiltration etc. These models were trained using RFE and PCA methods to find the best feature classification/reduction technique for adequacy prediction.xvii The paired t-test indicated that there was a significant difference between outcome parameters for both in feature reduction techniques and RFE was proved better. And under RFE, the paired t-test was employed on performance matrix (accuracy, precision, F1 score, recall and AUROC) of all the models and there was significant difference between two combinations (RF, XG Boost) and (RF, LGBM). XG Boost and LGBM performed best based on outcome parameters (0.9932, 0.9949, 0.0.9949, 0.9949, 0.9924) and (0.9931, 0.9949, 0.9952, 0.9951, 0.9916) respectively. HemoSys is the step forward to introduce predictive modelling in dialysis technology to provide operators with significant insight to change session parameters for better patient outcomes. This approach increases safety, efficiency, and effectiveness regarding hemodialysis treatment and is therefore a very promising solution for healthcare providers, patients, and all stakehold 30cm.

Place Hold on HemoSys: An Integrated Framework for Hemodialysis Machine Operation, Real-Time Monitoring, Data Management and Predictive Modeling for Patient Safety /

Development of Vision Based Tactile Sensor Rendering Distributed Contact Force /

By Qaisar, Muhammad Raheel . . 90p. , Tactile sensing in robotics plays a crucial role in enabling precise and safe object manipulation, particularly when dealing with fragile or delicate items that require accurate force control. Traditional tactile sensors primarily provide single-point force measurements, which limits their utility in complex manipulation tasks where distributed contact information is essential. As robotic systems advance toward more human-like dexterity, the demand for distributed tactile sensing has increased significantly. Various modalities have been developed for this purpose, including capacitive, resistive, piezoelectric, and vision-based tactile sensors (VBTSs). Among these, VBTSs have gained considerable attention due to their unique advantages, such as high spatial resolution, resistance to hysteresis, immunity to electromagnetic interference, and the capability to measure distributed forces accurately. Despite their promising attributes, the development of VBTSs still lacks a structured and generalized design methodology. This paper addresses that gap by proposing a comprehensive design framework for the development of a multimodal VBTS system. The proposed sensor architecture integrates visual and tactile stimuli using an elastic skin embedded with square fiducial markers, coupled with a depth camera. The core sensing principle relies on tracking the deformation of these markers under external contact, which allows for accurate estimation of distributed contact forces. The stiffness of the elastic skin was experimentally characterized, and this data was utilized to correlate marker displacement with applied forces through controlled indentation experiments. A prototype sensor was fabricated following the proposed framework, and experimental validation was conducted by performing object manipulation tasks. Results demonstrate that the sensor effectively estimates distributed contact forces and can handle fragile objects with precision. This study contributes a robust methodology for developing VBTSs and highlights their potential in advancing tactile capabilities in robotic systems. 30cm.

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Algorithm Development for Distributed Force Estimation During Tactile Sensing /

By Zulfiqar, Nimra. . 72p. , Vision-based tactile sensors provide an effective solution for robotic manipulation tasks requiring high sensitivity, such as grasping fragile objects. This research presents a tactile sensing approach based on a flat elastic skin embedded with visual markers and integrated with a depth camera on a parallel robotic gripper. A set of algorithms has been developed that process marker displacements to estimate distributed contact forces and contact areas during object interaction. These estimations are then utilized in computing distributed tactile pressure values and for detecting incipient slip through pressure variation analysis. The main contribution of this research is the development of a unified framework that integrates distributed force estimation with force-based contact detection and pressure-based slip detection for improved grasping of fragile objects. Specifically: (1) a marker tracking algorithm is proposed to detect surface deformation from the tactile skin, enabling distributed force estimation; (2) the estimated forces are used to compute pressure distribution maps and identify contact regions; and (3) a slip detection algorithm is introduced that leverages changes in the pressure field to reliably detect incipient slip during manipulation. Experimental results demonstrate that the proposed method achieves accurate slip detection with 98.9% success rate, and improves grasp reliability, achieving 99.5% success in real-time manipulation tasks. The integration of distributed force, pressure, and slip estimation significantly enhances robotic grasp stability and enables safe handling of delicate items. 30cm..

Place Hold on Algorithm Development for Distributed Force Estimation During Tactile Sensing /

Adaptive Denoising of Respiratory Sounds with a Hybrid Discrete Wavelet Transform and 1D CNN Framework /

By Ashraf, Naila . . 63 , Accurate analysis of respiratory sounds holds key importance in precise diagnosis of pulmonary diseases. These sounds are sometimes noisy which require an effective diagnosing method for noise reduction and correct analysis of lung sounds. Traditional denoising methods are limited to spectral overlap with background noise. This study attempts for denoising biomedical signals utilizing discrete wavelet convolutional neural network (DW-CNN), an adaptive filter consists of multiresolution ability of discrete wavelet transform (DWT) and deep feature learning ability of neural networks, which preserve the signal details. Encoder-decoder structure of DW-CNN with inverse DWT accurately reconstructs the signal, outperforming traditional wavelet denoising. LS signals under real noise conditions were denoised using DW-CNN, where DWT replaced traditional pooling layers. Results were quantitatively compared with baseline traditional methods using Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE) metrics. On average there is an improvement of 9.61 dB in SNR and a reduction of 0.35489 in RMSE. Final results highlight the better performance of implemented model over conventional methods, increasing the scope of combined model in pattern recognition, clinical diagnostics and wearable devices. 30cm.

Place Hold on Adaptive Denoising of Respiratory Sounds with a Hybrid Discrete Wavelet Transform and 1D CNN Framework /

The Role of Synthetic One-Dimensional Biomedical Data in Machine Learning Overcoming Data Limitations /

By Khuram, Nazli . . 118p. , This research presents a two-stage approach to investigate the use of generative deep learning models to synthesize realistic surface electromyography (sEMG) signals for improved gesture recognition. In the first study, a Conditional Variational Autoencoder (CVAE) was developed to generate synthetic gesture-conditioned EMG data. Experiments conducted on a self-collected dataset and the publicly available Ninapro DB3 dataset demonstrated that classifiers trained on hybrid datasets (real+synthetic) achieved higher accuracies compared to real-only training with gains in some scenarios. The second study introduced a novel CVAE-TCN architecture integrating temporal convolutional networks to learn sequential dependencies and to enhance temporal realism of synthetic EMG signals. Evaluation metrics including Pearson correlation, Dynamic Time Warping (DTW), and Wasserstein distance confirmed improved signal fidelity and better class separation in the latent space. Across both studies the generative models proved effective in addressing data scarcity boosting classification performance and enhancing the robustness of sEMG based gesture recognition systems. 30cm.

Place Hold on The Role of Synthetic One-Dimensional Biomedical Data in Machine Learning Overcoming Data Limitations /

High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /

By Shabbir, Nida . . 94p. , Low back pain (LBP) is one of the most prevalent musculoskeletal disorders worldwide, with diagnosis often relying on subjective evaluation rather than objective physiological measures. This thesis introduces a data-driven framework for quantitative assessment of LBP using high-density surface electromyography (HD-sEMG). As a non-invasive technique, HD-sEMG provides insight into spinal neuromuscular behavior, enabling spatial and temporal characterization of muscle activity patterns associated with dysfunction. The study recruited 39 participants, divided into three groups that are healthy, sub-clinical, and LBP based on chiropractic evaluation. In the first study, machine learning classifiers including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were trained on time and frequencydomain features to discriminate between groups. The SVM model achieved the highest accuracy, effectively distinguishing subtle neuromuscular differences between healthy and dysfunctional subjects. In the second study, a regression-based framework was developed to predict vertebral joint dysfunction scores (C1-Sacral) derived from chiropractic assessment. ANN and Convolutional Neural Network (CNN) models were trained under a CORAL (Consistent Rank Logits) ordinal regression framework, preserving the ordinal nature of dysfunction severity. The ANN model demonstrated superior predictive performance, capturing non-linear relationships between HD-sEMG activity and graded dysfunction levels. Overall, this research bridges the gap between clinical assessment and computational diagnostics, showing that HD-sEMG signatures can objectively quantify spinal dysfunction and support data-driven LBP diagnosis. The proposed framework establishes a foundation for personalized rehabilitation, automated dysfunction mapping, and AI-assisted musculoskeletal diagnostics, advancing the integration of biomedical signal processing with clinical neurophysiology. 30cm.

Place Hold on High-Density Surface Electromyography for the Assessment and Evaluation of Low Back Pain /

An Intelligent Framework for Real-Time PCG Classification and Cardiac Monitoring Using Machine Learning /

By Mansoor, Mariam . . 103p. , The accurate and timely diagnosis of cardiovascular diseases, particularly valvular heart disorders, is essential for reducing morbidity and mortality. However, conventional auscultation remains limited by subjectivity, ambient noise, and an inability to record or analyse sounds. This thesis addresses these limitations through a two-fold contribution: the development of a wearable, high-fidelity digital auscultation system and the integration of a real-time, AI-assisted classification framework for automated diagnosis. The first part of the research presents a custom-designed wearable digital stethoscope using a piezoelectric contact sensor embedded in an aluminium chestpiece. This configuration offers superior acoustic coupling and environmental noise rejection. A multi-stage low-noise analog signal chain was designed, incorporating band-specific filtering to isolate heart and lung signals. A miniaturized PCB was fabricated to house the circuit, and a Python-based GUI was developed to visualize, record, and archive phonocardiogram (PCG) signals while generating patient-linked reports. In the second phase, a lightweight 1D convolutional neural network (CardioSynx) was introduced for real-time classification of five PCG classes, including normal and pathological valve conditions. The model trained offline using Butterworth filtering, Hilbert envelope segmentation, and wavelet denoising, achieved 97.5% ± 0.22 accuracy on validation data. For deployment, the system employed a parallel processing architecture to enable simultaneous audio acquisition, denoising, feature extraction, and inference, achieving a frame-level latency of 58 ms, well below clinical standards and an SNR of 16.8 dB. Real-time evaluation on patients yielded over 90% accuracy across key pathologies. Together, the proposed system delivers a scalable, real-time, and clinically robust platform for digital auscultation and decision support in modern healthcare. 30cm.

Place Hold on An Intelligent Framework for Real-Time PCG Classification and Cardiac Monitoring Using Machine Learning /