Integration of Wearable Sensors and Electrical Muscle Stimulation in Lower Limb Robotics for Gait Rehabilitation

Main Article Content

Mandar Malawade, Mohammad Sohail Pervez, Anil Sahu, Dharmesh Dhabliya, Vibha Vyas, Yatin Gandhi


Combining wearing devices with electrical muscle stimulation (EMS) in robotic lower limbs has shown promise in helping people learn how to walk again. This new method blends the benefits of smart technology and EMS to help people with neurological or joint problems improve their motor function and walking patterns. Wearable devices, like inertial measurement units (IMUs) and electromyography (EMG) monitors, record information about how the body moves and what muscles are doing while the person moves. These monitors give the user and the therapy team feedback in real time, which makes it possible for personalized and flexible recovery plans. Data from personal monitors can also be used to look at how people walk and see how they're doing over time. EMS is used to target certain muscles and make them stronger, more coordinated, and easier to control. By combining EMS with robots for the lower limbs, therapists can focus on certain muscle groups and make recovery routines more successful. EMS can also be timed to move with the robotic suit to help the person walk in a more natural and useful way. Wearable sensors and EMS work well together in lower limb robots for many reasons. EMS therapy can be made more specific and flexible because wearing monitors let doctors change the frequency and strength of the therapy in real time. The person is also encouraged to be more involved with this method because they get instant feedback on their actions and can see how they're doing over time. The combining wearing sensors and EMS in lower limb robots has a lot of promise to make gait therapy work better. Future study should focus on finding the best ways to combine these tools and looking at how they affect the quality of life and ability to walk for people who have movement problems in the long run.

Article Details

Author Biography

Mandar Malawade, Mohammad Sohail Pervez, Anil Sahu, Dharmesh Dhabliya, Vibha Vyas, Yatin Gandhi

[1]Dr. Mandar Malawade,

2Dr.Mohammad Sohail Pervez

3Dr. Anil Sahu

4Dharmesh Dhabliya

5Dr. Vibha Vyas

6Yatin Gandhi


[1] Professor & HOD, Department of Paediatric Neurosciences, Krishna College of Physiotherapy, Krishna Vishwa Vidyapeeth (Deemed to be University), Karad.  Email:

2Associate Professor in Mechanical Engineering Department, Anjuman College of Engineering and Technology, Nagpur, Email:

3Professor and Dean PhD, G H Raisoni College of Engineering and Management Pune Email:,                                       Email:

4Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India                    Email:

5Assistant Professor, Department of Electronics and Telecommunication, COEP Technological University, Pune Maharashtra.                      Email:

6Competent Softwares, Pune, Maharashtra, India.

*Corresponding: Dr. Mohammad Sohail Pervez (,



Chen, G.; Qi, P.; Guo, Z.; Yu, H. Gait-Event-Based Synchronization Method for Gait Rehabilitation Robots via a Bioinspired Adaptive Oscillator. IEEE Trans. Biomed. Eng. 2017, 64, 1345–1356.

Maqbool, H.F.; Husman, M.A.B.; Awad, M.I.; Abouhossein, A.; Iqbal, N.; Dehghani-Sanij, A.A. A real-time gait event detection for lower limb prosthesis control and evaluation. IEEE Trans. Neural Syst. Rehabil. Eng. 2017, 25, 1500–1509.

Song, L.; Wang, Y.; Yang, J.J.; Li, J. Health sensing by wearable sensors and mobile phones: A survey. In Proceedings of the IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), Natal, Brazil, 15–18 October 2014; pp. 453–459.

Derawi, M.; Bours, P. Gait and activity recognition using commercial phones. Comput. Secur. 2013, 39, 137–144.

Schneider, O.S.; MacLean, K.E.; Altun, K.; Karuei, I.; Wu, M. Real-time gait classification for persuasive smartphone apps: Structuring the literature and pushing the limits. In Proceedings of the 2013 International Conference on Intelligent User Interfaces, Santa Monica, CA, USA, 19–22 March 2013; pp. 161–172.

Skelly, M.M.; Chizeck, H.J. Real-time gait event detection for paraplegic FES walking. IEEE Trans. Neural Syst. Rehabil. Eng. 2001, 9, 59–68.

Rueterbories, J.; Spaich, E.G.; Andersen, O.K. Gait event detection for use in FES rehabilitation by radial and tangential foot accelerations. Med. Eng. Phys. 2014, 36, 502–508.

Salarian, A.; Russmann, H.; Vingerhoets, F.J.G.; Burkhard, P.R.; Aminian, K. Ambulatory monitoring of physical activities in patients with parkinsonapos’s disease. IEEE Trans. Biomed. Eng 2007, 54, 2296–2299.

Gavrila, D.M.; Davis, L.S. 3-D Model-based tracking of humans in action: A multi-view approach. Proceedings of the IEEE Computer Vision and Pattern Recognition, San Francisco, CA, USA, 18–20 June 1996; pp. 73–79.

Ajani, S. N. ., Khobragade, P. ., Dhone, M. ., Ganguly, B. ., Shelke, N. ., & Parati, N. . (2023). Advancements in Computing: Emerging Trends in Computational Science with Next-Generation Computing. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 546–559

Karaulovaa, I.A.; Hallb, P.M.; Marshall, A.D. Tracking people in three dimensions using a hierarchical model of dynamics. Image Vis. Comput 2002, 20, 691–700.

Furnée, H. Real-time motion capture systems. In Three-Dimensional Analysis of Human Locomotion; Allard, P., Cappozzo, A., Lundberg, A., Vaughan, C.L., Eds.; John Wiley & Sons: Chichester, UK, 1997; pp. 85–108.

Cappozzo, A.; Della Croce, U.; Leardini, A.; Chiari, L. Human movement analysis using stereophotogrammetry. Part 1: Theoretical background. Gait Posture 2005, 21, 186–196.

Chiari, L.; Della Croce, U.; Leardini, A.; Cappozzo, A. Human movement analysis using stereophotogrammetry. Part 2: Instrumental errors. Gait Posture 2005, 21, 197–211.

Anandpwar, W., Barhate, S., Limkar, S., Vyawahare, M., Ajani, S. N., & Borkar, P. (2023). Significance of Artificial Intelligence in the Production of Effective Output in Power Electronics. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 30–36.

Casadio, M.; Morasso, P.G.; Sanguineti, V. Direct measurement of ankle stiffness during quiet standing: Implications for control modelling and clinical application. Gait Posture 2005, 21, 410–424.

Bonato, P. Wearable sensors/systems and their impact on biomedical engineering. Eng. Med. Biol. Mag 2003, 22, 18–20.

Engin, M.; Demirel, A.; Engin, E.Z.; Fedakar, M. Recent developments and trends in biomedical sensors. Measurement 2005, 37, 173–188.

Bachmann, E.R. Inertial and Magnetic Tracking of Limb Segment Orientation for Inserting Humans into Synthetic Environments. Ph.D. Thesis,. Naval Postgraduate School, Monterey, CA, USA, 2000.

Bamberg, S.J.M.; Benbasat, A.Y.; Scarborough, D.M.; Krebs, D.E.; Paradiso, J.A. Gait analysis using a shoe-integrated wireless sensor system. IEEE Trans. Inf. Technol. Biomed 2008, 12, 413–423.

Liu, T.; Inoue, Y.; Shibata, K. A wearable ground reaction force sensor system and its application to the measurement of extrinsic gait variability. Sensors 2010, 10, 10240–10255.

Jagos, H.; Oberzaucher, J.; Reichel, M.; Zagler, W.L.; Hlauschek, W. A multimodal approach for insole motion measurement and analysis. Procedia Eng 2010, 2, 3103–3108.