Waterloo Robotic Rollator (WatRR)
In Ontario alone, over 40,000 new rollators (or 4-wheeled walkers) are subsidized publicly at an annual cost of $16M. Despite the recommended use to address gait and balance disorders, ~50% are abandoned for reasons associated with usability, such as poor maneuverability, difficulty using brakes, and limited accessibility. The NRE Lab aim to develop new mechatronic features (e.g., intelligent braking, active assistance) to improve safety and effectiveness of rollator devices while promoting safe mobility.
Publications:
- A. Yeaser, J. Tung, J. Huissoon, and E. Hashemi, “Learning-aided state estimation for robotic rollators with experimental validation,” Robotics and Autonomous Systems, vol. 194, p. 105140, Dec. 2025, doi: https://doi.org/10.1016/j.robot.2025.105140.
- Y. Liao, A. Yeaser, B. Yang, J. Tung, and E. Hashemi, “Unsupervised fault detection and recovery for intelligent robotic rollators,” Robotics and autonomous systems, vol. 146, pp. 103876–103876, Dec. 2021, doi: https://doi.org/10.1016/j.robot.2021.103876.
Sensor-Mediated Assessment of physical RehabiliTation (SMART)
Assessing recovery is a critical process in physical rehabilitation that informs a range of key clinical decisions, including selecting safe treatment plans, monitoring progression, and detecting incidental concerns (e.g., conditions affecting rehabilitation progress and safety). While technical developments in sensor-mediated assessment tools have demonstrated promising measurement capabilities, their translation to inform clinical decisions remains underexamined. The proposed project aims to investigate the clinical validity of a wearable sensor system to inform rehabilitation care following arthroscopic partial knee meniscectomy. Specifically, performance-based metrics (e.g., range of motion, movement quality) will first be generated from wearable IMU sensor signals, followed by focus group evaluation to assess the system’s feasibility and utility to inform clinical care.
Publication:
- A. Colpitts, R. Ibey, J. F. S. Lin and J. Tung, "Kinematics-Based Lower Limb Rehabilitation Monitoring Following Partial Knee Meniscectomy: Case Study," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 2531-2534, doi: 10.1109/EMBC48229.2022.9871925.
Dynamic Transtibial Prosthetic Socket
Of the 7,500 lower-limb amputations occurring in Canada each year, an estimated 70-85% arise from complications associated with diabetes and/or vascular disease. Considering diabetic amputees are prone to impaired tactile sensation (i.e., neuropathy), users have difficulty perceiving socket fit leading to increased risk of pressure ulcers, gait instability, device abandonment, and even revision amputation. This program of research aims to develop new sensing methods to indicate socket fit and novel actuation methods to dynamically adjust socket fit under real-world conditions.
Publications:
- A. J. Yu et al., “Soft robotics–inspired sensing system for detecting downward movement and pistoning in prosthetic sockets: A proof-of-concept study,” Prosthetics and Orthotics International, vol. 48, no. 5, Nov. 2023, doi: https://doi.org/10.1097/pxr.0000000000000302.
- P. S. Lee et al., “Air microfluidics-enabled soft robotic transtibial prosthesis socket liner toward dynamic management of residual limb contact pressure and volume fluctuation,” Biomicrofluidics, vol. 16, no. 3, May 2022, doi: https://doi.org/10.1063/5.0087900.
Concussion Screening/Assessment
Kinematic assessment of limb movements provides insight into the neural control strategies during development, aging, and neuropathologies, including stroke, concussion. Building on research-grade laboratory-based kinematic measurement using fixed and costly motion capture capabilities, the NRE lab has developed low-cost, portable kinematic measurement systems to conduct motor assessments in real-world environments. In particular, new tools have been developed to permit concussion screening on the bench or sideline of sporting activities.
Publication:
- D. Al-Mfarej, D. . Gonzalez, and J. Tung, “Sensor-based 9-week Serial Balance Data Show Need for Individualized Baseline Profiles: Implications on Concussion Diagnosis”, CMBES Proc., vol. 44, May 2021.
Wearable Fall Risk Assessment
A common consequence of unsuccessful adaptation to real-world conditions is falling, which is the leading cause of disability in seniors. Despite intensive research, fall prevention approaches are challenged by the multitude and heterogeneity of factors precipitating falls. Our research team and I are developing multimodal wearable sensor methods (e.g., inertial measurement units (IMU) + EMG + EEG) to identify individual fall risks and inform rehabilitation. Academic contributions comprise of literature reviews examining wearable sensor approaches. Technical contributions include novel methods to detect involuntary stepping reactions to recover balance, estimate attentional loads, and extract features of the environment (e.g., terrain, obstacles) using machine vision and deep learning techniques.
Publications:
- M. Nouredanesh, A. Godfrey, D. Powell, and J. Tung, “Egocentric vision-based detection of surfaces: towards context-aware free-living digital biomarkers for gait and fall risk assessment,” Journal of Neuroengineering and Rehabilitation, vol. 19, no. 1, Jul. 2022, doi: https://doi.org/10.1186/s12984-022-01022-6.
- M. Nouredanesh et al., “Automated Detection of Older Adults’ Naturally-Occurring Compensatory Balance Reactions: Translation From Laboratory to Free-Living Conditions,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1–13, Jan. 2022, doi: https://doi.org/10.1109/jtehm.2022.3163967.
Lower-limb exoskeleton
Current exoskeleton controls rely heavily on embedded sensors, principally joint position encoders, ground contact forces, and/or user-robot interaction torques. While these systems have focused on optimizing low-level joint control for specific task modes (i.e., sitting, standing, walking), high-level switching between modes requires manual control. In this research theme, we aim to advance high-level exoskeleton control by investigating measurement and estimation of user intention and environmental context using brain-machine interfaces and multimodal sensors.
