deepesh.bme's picture
Dr. Deepesh Kumar
Assistant Professor
School of Bio-Medical Engineering IIT (BHU)
deepesh.bme@itbhu.ac.in
+91-9586301041
Area of Interest: 
Neuromorphic tactile and vision sensing; Virtual reality-based neurorehabilitation interventions; Biomedical signal processing; Eye tracking for health monitoring; Machine Learning

Dr. Deepesh Kumar is currently an Assistant Professor in the School of Biomedical Engineering, IIT(BHU) Varanasi. He obtained his Bachelor's Degree in Electronics and Telecommunication Engineering from Chhattisgarh Swami Vivekananda Technical University (CSVTU), Bhilai, India in 2009. He completed his Master of Technology Degree in Electronics and Instrumentation from the National Institute of Technology (NIT) Rourkela, India in 2013. Subsequently, he obtained a Ph.D. degree from the Indian Institute of Technology (IIT) Gandhinagar, India in 2018. He pursued his Ph.D. on “Technology-assisted Screening and Balance Training Systems for Stroke Patients”. During his Ph.D., he visited the LIRMM laboratory at the University of Montpellier, France in 2015 and 2016 as a part of the Indo-French project funded by the Department of Science and Technology (DST) India and The National Institute for Research in Computer Science and Automation (INRIA), France.  After his Ph.D., he joined the National University of Singapore (NUS) as Research Fellow in July 2018 and was affiliated with The N.1 Institute for Health (previously, SINAPSE). At the N.1, he worked on Neuromorphic tactile and vision sensing for neurorobotics and neuroprosthesis applications before moving to IIT (BHU) Varanasi in July 2021.  
Dr. Deepesh Kumar’s current research work is focused on providing an artificial sense of touch to the prosthetic/robotic hands. The core of this work is to develop a Neuromorphic tactile (touch) sensing and encoding framework that mimic the response characteristics of tactile neurons available at the human hand. Since biological neurons respond to external stimuli in the form of a spike train, this work aims to develop an end-to-end spiking neural network architecture for emulating the biological touch receptors’ response and implementation of neuromorphic circuits for spike-based computation of tactile information.  
He is also interested in the use of technology such as virtual reality, eye tracking, balance boards, motion capture devices, etc to develop novel rehabilitation paradigms for people having motor impairment due to neurological disorders. He is also interested in biomedical signal processing for diagnosis and/or prognosis of neurological disorders and quantifying the effect of rehabilitation efforts.
July 2021 - Present Assistant Professor, School of Biomedical Engineering, IIT(BHU) Varanasi
Sept 2020 - May 2021 Research Fellow, The N.1 Institute for Health, NUS Singapore.
Principal Investigator of the externally funded project “Newprowler: A low-power end-to-end hybrid neuromorphic framework for surveillance applications.
July 2018 - August 2020 Research Fellow, The N.1 Institute for Health, NUS Singapore
Project: Neuromorphic tactile sensing for robotics/prosthetics applications.
July 2018 - August 2020 Teaching Assistant, Department of Biomedical Engineering, NUS Singapore.
Assisted in theory and laboratory classes for the course Neurosensor and Signal Processing.
July 2013 - April 2018 Teaching Assistant: Department of Electrical Engineering, IIT Gandhinagar.
Teaching assistant for courses such as Artificial Intelligence, Electronic Instrumentation, Biostatistical Signal Analysis, Electronic Devices Laboratory.

Book Chapter

[1] Kumar, D., Nakagawa-Silva, A., Soares, A. B. & Thakor, N. V. (2020). Neuromorphic touch. Handbook of Neuroengineering (Springer Nature publication), [In press].

Journal Publications

[1]  Parvizi-Fard, A., Amiri, M., Kumar, D., Iskarous, M. M., & Thakor, N. V. (2021). A functional spiking neuronal network for tactile sensing pathway to process edge orientation. Nature Scientific reports, 11(1), 1-16.
[2] Kumar, D., Ghosh, R., Silva, A. N., Soares, A. B. & Thakor, N. V. (2020). Neuromorphic Approach to Tactile Edge Orientation Estimation using Spatiotemporal Similarity. Neurocomputing, 407, 246-258.
[3] Kumar, D., Sinha, N., Dutta, A., & Lahiri, U. (2019). Virtual reality-based balance training system augmented with operant conditioning paradigm. Biomedical engineering online,18(1), 1-23.
[4] Kumar, D., Gonjalez, A., Das, A., Dutta, A., Fraisse, P., Hayashibe, M., & Lahiri, U. (2017). Virtual Reality based Center of Mass Assisted Balance Training System. Frontiers in Bioengineering and Biotechnology,5, 85.
[5] Verma, S., Kumar, D., Kumawat, A., Dutta, A., & Lahiri, U. (2017). A Low-cost Adaptive Balance Training Platform for Stroke Patients: A Usability Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 933-944.
[6] Kumar, D., Dutta, A., Das, A., & Lahiri, U. (2016). Smarteye: developing a novel eye tracking system for quantitative assessment of oculomotor abnormalities. IEEE Transactions on neural systems and rehabilitation engineering, 24(10), 1051-1059.
[7] Kumar, D., Das, A., Lahiri, U., & Dutta, A. (2016). A human-machine-interface integrating low-cost sensors with a neuromuscular electrical stimulation system for post-stroke balance rehabilitation. JoVE (Journal of Visualized Experiments), (110), e52394-e52394.
[8] Kumar, D., Dutta, A., Das, A., & Lahiri, U. (2016). Engagement sensitive visual stimulation. European journal of translational myology, 26(2).
[9] Kumar, D., Verma, S., Bhattacharya, S., & Lahiri, U. (2016). Audio-visual stimulation in conjunction with functional electrical stimulation to address upper limb and lower limb movement disorder. European journal of translational myology, 26(2).
[10] Dutta, A., Kumar, D., Lahiri, U., Das, A., & Padma, M. V. (2014). Poststroke engagement-sensitive balance rehabilitation under an adaptive multi-level electrotherapy: clinical hypothesis and computational framework. Neuroscience and Biomedical Engineering, 2(2), 68-80.
[11] Kumar, D., Tripathy, R. K., & Acharya, A. (2014). Least square support vector machine based Multiclass classification of EEG signals. WSEAS Transactions on Signal Processing, 10, 86-94.

Conference Publications

[1] Wang, K., Yang, S., Kumar. D. and Thakor, NV. (2020). Hybrid Frame-Event Solution for Vision-Based Grasp and Pose Detection of Objects. In IEEE International Conference on Automation Science and Engineering (CASE). 2020.
[2] Sankar, S., Brown, A., Balamurugan, D., Nguyen, H., Iskarous, M., Simcox, T., Kumar, D., Nakagawa, A. and Thakor, N. (2019, October). Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger. In 18th IEEE Sensors, SENSORS 2019 (p. 8956704). Institute of Electrical and Electronics Engineers Inc.
[3] González, A., Das, A., Kumar, D., Dutta, A., Lahiri, U., Fraisse, P. & Hayashibe, M. (2017). Use of the Personalized Mass Center for Balance Assessment. In memories of the National Congress of Biomedical Engineering (CNIB2017), (pp. 273-276).
[4] Kodappully, M., Kumar, D., & Lahiri, U. (2017, July). A step towards smart health: A pelvic wearable device for gait health quantification. In IEEE Region 10 Symposium (TENSYMP), 2017(pp. 1-5). IEEE.
[5] Kumar, D., Das, A., Dutta, A., & Lahiri, U. (2016) “A Step Towards Developing a Gaze-based Screening Tool for Neurological Disorder,” In 10th World Stroke Congress, 26th -29th October, Hyderabad, India.
[6] Kumar, D., Aggarwal, G., Sehgal, R., Das, A., Lahiri, U., & Dutta, A. (2015, April). Engagement-sensitive interactive neuromuscular electrical therapy system for post-stroke balance rehabilitation-a concept study. In Neural Engineering (NER), 2015 7th International IEEE/EMBS Conference on (pp. 190-193). IEEE
[7] Kumar, D., Goyal, Y., Nair, S., Chauhan, A., & Lahiri, U. (2014, August). Design of a physiologically informed virtual reality based interactive platform for individuals with upper limb impairment. In Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium on (pp. 112-117). IEEE.
[8] Gautam, G., & Kumar, D. (2013, April). Biometric system from heart sound using wavelet based feature set. In Communications and Signal Processing (ICCSP), 2013 International Conference on (pp. 551-555). IEEE

Session 2021-2022 Odd Semester

  • Biomechanics (BM302)
  • Microprocessor and Microcontroller (BM311)
Actively looking for motivated Ph.D./Masters/Undergraduate students to work on the i) Development of spiking neural network architecture for neuromorphic tactile sensing, ii) Finite element modeling and fabrication of flexible tactile sensor iii) Virtual reality-based human-machine-interaction for physical rehabilitation, iv) Biomedical signal processing for diagnosis/prognosis of health condition.
If you are interested in any of the above-mentioned research areas or any related research field then drop me an email with your resume.
  • 23rd International Symposium on Robot and Human Interactive Communication, Edinburgh, Scotland, 2014
  • 7th International IEEE EMBS Neural Engineering Conference, Montpellier, France, 2015
  • 20th Conference of the International Functional Electrical Stimulation Society, IFESS, La Grande-Motte, France, 2016
  • 10th World Stroke Congress, Hyderabad, India, 2016
  • 11th Indian National Stroke Conference, Amritsar, India, 2017