NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Rehabilitation")'

Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Recent Advances in Biomedical Engineering (E-Book)

By Dr Ganesh R Naik. India In-Teh 2009 . xii,660 P;

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Systems engineering to improve traumatic brain injury care in the military health system (EBOOK) workshop summary /

Washington, D.C. : National Academies Press, 2009 . xix, 174 p. : 23 cm.. 0309127580 | 9780309127585

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Community participation in China : issues and processes for capacity building /

London ; | Sterling, VA : Earthscan, 2004 . xxv, 358 p. : , "Research for DFID, Department for International Development." | "G H K." 24 cm.. 1844070867 (alk. paper)

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New Approaches to Building Pathology and Durability /

. 1 online resource (VIII, 207 pages) 9789811006487

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Community participation in China : issues and processes for capacity building /

London ; | Sterling, VA : Earthscan, 2004 . xxv, 358 p. : , "Research for DFID, Department for International Development." | "G H K." 24 cm.. 1844070867 (alk. paper)

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Bridges : Analysis, Design, Structural Health Monitoring, and Rehabilitation /

By Bakht, Baidar,. . 1 online resource (XV, 425 pages 325 illustrations, 79 illustrations in color.) 9783319178431

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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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Design and Development of a System for Pressure Measurement in Patients using Lower Limb Prosthetic and Orthotic Appliances /

By Javaid, Ajla . . 68p. , Pain and pressure sensation as parts of somatosensory system provides sense of awareness of joint position & body orientation in space. Proprioception works like a constant feedback loop where human beings are very well aware of body position and forces acting on it. Any abnormality in pressure especially for amputees would result in skin break down, joint disorders and noncompliance of prosthetic limb. In order to avoid problems related to skin & joints along with psychological satisfaction of patient, pressure mapping is performed with improved device compliance. Due to unavailability and high cost of MEMS based bubble sensors and TEKSCAN system, an effective and low-cost solution to problems related to pressure mapping before and after prosthetic fittings is introduced wherein a device capable of being incorporated within stump and prosthesis with minimal chances of breakage due to its flexibility is designed. Twelve pressure tolerant & sensitive areas of stump were marked upon silicon-based stump liner of 3mm thickness for TTP socket. FSR are attached to those specified areas and a Bluetooth module is used in PCB to send data through PLX-DAQ into MS-Excel. When a patient wears his/her prosthesis along with specially designed silicon liner having FSR and bears weight or walk, data is sent back into excel sheet recording the pressure values from 12 locations of a stump. Then adjustments can be made in prosthesis on the exact locations that showed increased pressure values so to eliminate pain and pinch and relieve the patient. Data from 7 patients during standing/ loading response of gait cycle was taken at Rehabilitation department of hospitals and required adjustments were made in prosthetic socket and alignment by the clinician/prosthetist. Comparison between means of pressure values before (27N/m2 ) and after making adjustment in prosthesis was made which showed improved values of pressure on distal tibia (<17N/m2 ) Data from 1 patient was obtained through all phases of his gait cycle and plotted on MS-Excel to find out exact phase of gait cycle causing more pressure on stump. The results showed significantly increased values of pressure on sensitive regions of stump during mid-stance phases of gait cycle (>75N/m2 ). While during early stance and swing phases the pressure values were significantly low (<28.7N/m2 30cm.

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Real-Time Target Acquisition Test for Rehabilitation Using EMG /

By Ain ,Qurat Ul . . 109p. ; , This research investigates the use of electromyography (EMG) signals for real-time control in rehabilitation applications. Utilizing the Myo armband, we captured EMG signals corresponding to 12 distinct hand and finger movements. We compared the performance of two machine learning classifiers, Long Short-Term Memory (LSTM) networks and Vanilla Neural Networks (VNN), in accurately classifying these movements. LSTM networks demonstrated superior performance, achieving higher accuracy and robustness in signal classification compared to VNN. To address adaptability and reduce training time for new users, we employed transfer learning techniques. Our research also incorporated transfer learning techniques to enhance model performance, leveraging both a broad dataset collected from multiple subjects and a focused dataset from a single individual over an extended period. Our results show that transfer learning significantly improves the adaptability of the system, allowing for quicker and more efficient integration of new subjects into the model. The study further includes statistical analysis to validate the performance improvements, with paired t-tests and ANOVA confirming the significance of our findings. This work highlights the potential of LSTM networks and transfer learning in enhancing the usability and effectiveness of EMG-based control systems for rehabilitation, paving the way for more responsive and adaptable prosthetic devices. The integration of advanced machine learning techniques into EMG signal processing presents a promising avenue for future research and clinical applications. 30cm..

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Design and Development of an Air-Driven Posture Correction Device /

By Haris, Muhammad. . 129p. , The prevalence of postural kyphosis continues to rise across all demographics because of inactive lifestyles and extended screen time usage which has established itself as a major musculoskeletal issue. The current solutions including static braces and feedback-only wearables, fail to provide adaptability and comfort with active correction features. This restricts their ability to achieve longterm success and user adherence. The research introduces a new wearable air-driven posture correction device which combines real-time sensing with pneumatic actuation to treat flexible thoracic kyphosis. The proposed wearable tech device focuses on posture correction by integrating multiple features into one compact device. The system includes an MPU-6050 inertial measurement unit which tracks the user’s trunk in real time in six degrees of freedom. A microcontroller processes this data and determines if changes to posture exceed a calibrated angular threshold. If so, the system triggers a pneumatic actuation module within an orthopedic vest that has been altered for this purpose. This module includes butyl rubber chambers which are constrained but designed to inflate and mechanically stress the upper back by simulating the action of scapular retractor muscles. To measure the corrective force, force-sensitive resistors (FSRs) are placed where the actuators and the body interface. A closed-loop control system dynamically adaptive and responsive to real time conditions with sensor fusion guarantees timely action and feedback. The prototype was tested by means of both objective and subjective methods, with a full-scale experimental protocol involving 24 healthy participants. Data collected in this study included realtime pitch angle vs actuator force, and subjective user feedback through standardized ergonomic surveys such as the Borg CR10 scale, Corlett & Bishop discomfort map, and the System Usability Scale (SUS). Results indicate that the system successfully minimized thoracic pitch deviation while maintaining safe tactile force levels, achieving average corrective pressures of 7–12 kPa, resulting in notable postural enhancement. The system reliably attained enhancements in posture, surpassing 85% in 22 out of 24 participants. This work contributes a fully automated, textile-integrated pneumatic solution for posture correction, combining real-time sensing, adaptive actuation, and ergonomic design. The proposed xix system offers a replicable framework for intelligent musculoskeletal rehabilitation wearables and lays the groundwork for future closed-loop personalization strategies in postural health technologies. 30cm..

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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.

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