NUST Institutions Library Catalogue Search for 'an:"119477"'

Phenolic Extract of Crithmum maritimum L. prevents Benzene induced leukemia in Balb/c mice /

By Shehzad ,Iqra . . 75p. ; 33cm..

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DECODING OF DIFFERENT HAND MOTIONS /

By Zaheen, Tahreem . . 47p. ; 30cm..

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Effect of time domain features on pattern recognition based myoelectric control /

By GHAZAL ,SADIA . . 72p. ; 30cm..

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Discrimination Among Hand Motions Using Combination of Features /

By Hamid , Anum . . 56P. ; 30cm..

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Decoding Lower Limb Residual Muscle Activity in Chronic Stroke /

By Noor ,Afaq Ahmed. . 50P. ; 30cm..

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Real-time approach for the effect of arm position using Fitts’ Law /

By Ahmad , Jamil . . 50p. ; 30cm..

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Machine/Deep Learning Based Adaptive Myoelectric Control /

By Ashraf ,Hassan . . 56p. ; 30cm..

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Evaluation of Upper Limb Assistive Device of 1 DOF and 2 DOF Grip Motions for Stroke Patients /

By UMAIR TARIQ ,MUHAMMAD . . 46p. ; 30cm..

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Modeling and Simulation of Rehabilitation/Assistive Device for Stroke Effected Hand /

By Faiz ,Muhammad Sohaib. . 47p. ; 30cm..

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Detection of Local Muscle Fatigue in Writing Task Using Surface Electromyography /

By Abbas ,Nadir . . 46p. ; 30cm..

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Effect of rapid exercise test on mild COVID-19 pneumonia patients and its significance on the detection of COVID-19 disease at an early stage /

By Arif ,Fatima. . 37p. ; 30cm..

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Study of compounds in exhaled breath for detection of obstructive lung disease /

By Shahzad ,Adil Ahmad . . 58p. ; 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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Development of Machine Learning Model for Classification of No Specific Chronic Lower Back Patients from Healthy Patients Usin Spinal Kinematic Data Through Motion Capture (MOCAP) /

By Kazim, Assad Ali . . 70p. , Non-specific lower back pain remains a global physiological disability surpassing pathological diseases. Its diagnosis through modern machines are expensive and frequent doses of radiation lead to deterioration of body cells. Recently with development of allied technologies such as Motion Capture (MoCap) can evaluate skeletal motion of the patient. This technology vastly used in cinematography has a hidden usage for diagnosis of patients with NSLBP. Through various sampling of motion and effective utilisation of Machine Learning, we can classify a healthy patient from NSLBP patient. Various supervised learning models such as Scalar Vector Machine (SVM), Random Forest (RF), XGBoost and ANN have shown promising results which reflects that such AI tools can predict patients having NSLBP. Effective utilization can lead to exact determination of location where said problem is being developed in the patient through various motion examinations. 30cm.

Place Hold on Development of Machine Learning Model for Classification of No Specific Chronic Lower Back Patients from Healthy Patients Usin Spinal Kinematic Data Through Motion Capture (MOCAP) /