An estimated 1.5 million individuals in the United States are hospitalized each year because of strokes, brain injuries and spinal cord injuries. Severe impairment such as paralysis, paresis, weakness and limited range of motion are common sequels resulting from these injuries, requiring extensive rehabilitation. This project is developing invisible sensing systems embedded into bed sheets, pillows, wheelchair pads, and clothing, for environmental control and physical therapy for such paralysis patients. The system detects gestures regardless of evolving environmental and patient conditions and provides explicit real-time feedback to the user. Through the use of low-cost and ultra-low power capacitive sensing, the system reduces hospital visits and therapy costs.
The proposed system addresses the limitations of existing assistive care sensors through three novel technical contributions: (1) The use of a self-sustainable hierarchy of sensors; textile-based capacitive sensor arrays (CSA) and inertial sensors on the human body; to improve the accuracy of gesture recognition while consuming minimal energy. The inertial sensors train the capacitive sensor arrays for different body positions; (2) A self-learning algorithm that determines gestures automatically regardless of the position of the patient's body and conditions using templates of gestures and patient conditions over time; and (3) Seamless integration of the patient in the feedback loop using amplification and animation to provide explicit real-time feedback to the user on how she/he is performing on his/her physical therapy, and how the system is interpreting his/her gestures. Additionally, the PIs are developing a cross-disciplinary undergraduate and graduate course that focuses on developing sensing systems while being cognizant of the actual needs in a rehabilitation hospital. The PIs are also using local university initiatives to engage minority and women researchers in the project.