speaker

Dr. Deepak Joshi

Assistant Professor

Centre for Biomedical Engineering

IIT Delhi

email: joshid[at]cbme.iitd.ac.in

Brief Introduction

Deepak Joshi received his PhD in biomedical engineering from Indian Institute of Technology (IIT) Delhi. He has been working in the area of neural prosthetic design and development for last ten years. During his PhD he developed a prototype lower limb prosthesis controlled by contra lateral limb. This work earned Invention award from Intellectual Ventures Asia. During his tenure at Department of electrical and computer engineering in National University of Singapore (NUS), he worked on development of artificial hand with integrated sensors to create an illusion of touch from human hand. This work demonstrated a significant impact on the social acceptance of upper limb prosthesis and was reported to be the most popular article in IEEE Transaction on neural system and rehabilitation engineering. His research work at Institute of Neuroscience (ION), Newcastle University in United Kingdom (UK) discovered that artificial proprioception can significantly improve the myoelectric control in upper limb amputee. During his postdoctoral at University of Oregon in United States of America (USA), he worked on integration of various sensing modalities to provide seamless transitions in lower limb prosthesis. Dr. Joshi’s current research work combines experimental and computational techniques to understand the neural correlates during balancing and seamless transitions during walking. Besides that, he is actively engaged in projects related to development of wearable devices for applications specific to diagnosis of neuromuscular disorders, assistive devices for elderly and disabled, and biofeedback for rehabilitation in stroke patients.

Abstract

Wearable instrumentation integrated with machine learning for gait analysis in neuromuscular disorder diagnosis and rehabilitation: Wearable instrumentation for quantitative gait analysis have become an important clinical tool for assessing pathologies manifested by  gait abnormalities. In addition, it has benefited other research areas like sports biomechanics, rehabilitation engineering, and neuroprosthesis. Present talk will discuss the recent new developments in the area of wearable instrumentation for gait analysis with a focus on neuroprosthesis and rehabilitation engineering. The talk will also highlight application of wearable instrumentation integrated with machine learning as home-based diagnosis for early and accurate detection of neuromuscular diseases, fall detection in elderly, and stress detection among vehicle drivers.