An intelligent system for “tuning” powered prosthetic knees allows patients to walk comfortably with the prosthetic device in minutes, rather than the hours necessary if the device is tuned by a trained clinical practitioner. The system is the first to rely solely on reinforcement learning to tune the robotic prosthesis.
When a patient receives a robotic prosthetic knee, the device needs to be tuned to accommodate that specific patient. The new tuning system tweaks 12 different control parameters, addressing prosthesis dynamics, such as joint stiffness, throughout the entire gait cycle.
Normally, a human practitioner works with the patient to modify a handful of parameters. This can take hours. The new system relies on a computer program that makes use of reinforcement learning to modify all 12 parameters. It allows patients to use a powered prosthetic knee to walk on a level surface in about 10 minutes.
Data on the device and the patient’s gait are collected via a suite of sensors in the device. A computer model adapts parameters on the device and compares the patient’s gait to the profile of a normal walking gait in real time. The model can tell which parameter settings improve performance and which settings impair performance. Using reinforcement learning, the computational model can quickly identify the set of parameters that allows the patient to walk normally. Existing approaches, relying on trained clinicians, can take half a day.
While the work is currently done in a controlled, clinical setting, one goal would be to develop a wireless version of the system, which would allow users to continue fine-tuning the powered prosthesis parameters when being used in real-world environments.
Researchers hope to make the process even more efficient. The researchers note that, while this work is promising, many questions need to be addressed before it is available for widespread use. The researchers also note that, if the system does prove to be effective and enter widespread use, it would likely reduce costs for patients by limiting the need for patients to make clinical visits to work with practitioners.