Our primary research focus is on the design of nonlinear and adaptive control algorithms for functional electrical stimulation (FES) and rehabilitation robots. Currently, we are interested in designing control algorithms that coordinate FES and a powered exoskeleton that assist walking and standing functions in persons with spinal cord injury. Integrating FES and an exoskeleton has many technical and therapeutic benefits. FES induces artificial but active muscle contractions contributing to muscle health and reanimation of paralyzed muscles. However, FES-induced muscle fatigue hinders its clinical implementation. We are designing control algorithms that enable a powered exoskeleton to work in tandem with FES and thus compensate for the muscle fatigue. The control design problem is challenging due to the disparate dynamics of FES and the exoskeleton, need to monitor the state of the muscle, and residual volitional effort of the user, if present. We are also working to include novel muscle state sensing modalities such as ultrasound imaging and surface electromyography. Our research is multidisciplinary and therefore we collaborate with clinicians in physical medicine and rehabilitation, physical and occupational therapists, and faculty specializing in ultrasound imaging.
Research Highlights
News
- Dr. Sharma invited to 2022 EU-US Frontiers of Engineering Symposium in Slovenia
- Dr. Sharma (PI) gets an NSF SCH grant award of $1.2 million to develop wearable multi-modal ultrasound and stimulation arrays
Recent Publications
- Z. Sun, T. Qiu, A. Iyer, B.E., Dicianno, N. Sharma, “Continuous Switching Control of an Input-Delayed Antagonistic Muscle Pair During Functional Electrical Stimulation,” IEEE Transactions on Control Systems and Technology, online, 2022.
- Z. Sheng, A. Iyer, Z. Sun, K. Kim, N. Sharma, “A Hybrid Knee Exoskeleton Using Real-Time Ultrasound-Based Muscle Fatigue Assessment,” IEEE Transactions on Mechatronics, vol. 27, no. 4, pp. 1854-1862, Aug. 2022
- V. Molazadeh, Q. Zhang, X. Bao, N. Sharma, “An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton,” IEEE Transactions on Control Systems Technology, vol. 30, no. 3, pp. 1021-1036, May 2022.