Collaborative Research: An Integrated, Proactive, and Ubiquitous Prosthetic Care Robot for People with Lower Limb Amputation: Sensing, Device Designing, and Control

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Individuals with lower limb amputation face many disadvantages while walking, such as increased strains on the lower back, increased metabolic cost, and gait asymmetry. Existing prosthetic robot prescriptions suffer from several deficiencies: they either cannot provide accurate and prompt device control or can only be used in a constrained laboratory environment. The key challenge for lower limb amputees is that there is no feedback control to the device from the visual information. As a result, the rest of the body requires extra musculature recruitment to compensate for increased instability, posing a greater risk of falling and degenerative joint issues. This award aims to develop a novel prosthetic robot that would use reconstruction of the neuromuscular feedback from user visual information to provide proactive and optimal control under different walking conditions. The outcome of this project will help clinicians provide enhanced care when prescribing lower limb prostheses and positively affect the lives of amputees living within the United States. In addition, this project will create a multidisciplinary education program comprising design and control knowledge, human-machine interface, sensors, and machine learning for undergraduate and graduate students from engineering, computer science, and medicine. Students with disabilities will also participate via data collection, involvement in meetings, and mentorship.The objective of this project is to develop a new prosthetic robot that enables proactive and user-specific prosthetic control to improve walking function in a variety of daily life conditions. The approach relies on interrelated advances in sensing strategy, device hardware, and control framework. The research incorporates: 1) Developing a motion capture method using only wearable inertial motion unit (IMU) sensors to reconstruct and predict human gait during daily life, while enabling assessment and clinical diagnosis beyond lab settings. This will be achieved through knowledge distillation that leverages a complex teacher network using multiple sensing modalities to train a simpler student network that uses IMU sensors. 2) Developing a lightweight, energy-efficient, and semi-active pneumatic and hydraulic hybrid device to enable the user to tune settings as per the operating environment. 3) developing a reinforcement-learning-based adaptive algorithm to control and sync user-device cooperation. In addition, the mixed-criticality design paradigm will be borrowed from real-time systems field to ensure the robot safety and stability.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.