Arvind Gautam

TL;DR: This work has attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor.

TL;DR: The accurate evaluation on a huge population with CVD complications, validates the robustness of the proposed framework in pervasive healthcare monitoring especially cardiac and stroke rehabilitation monitoring.

TL;DR: A transfer-learning based Long-term Recurrent Convolution Network named as ‘MyoNet’ is introduced for the classification of lower limb movements, along with the prediction of the corresponding knee joint angle, which could be beneficial for remote monitoring of rehabilitation progress by the physiotherapist using wearables.

TL;DR: A deep learning framework built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording can be favorable for the potential improvement in quality of life of an amputated persons.

TL;DR: A data driven iterative low pass filtering technique, the Empirical Iterative Algorithm (EIA) for Galvanic Skin Response (GSR) signal preprocessing is introduced, with results from EIA being compared with a smoothing technique based on moving average filter - commonly used to pre-process GSR signals.