GOAL: To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). METHODS: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. RESULTS: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. CONCLUSION: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. SIGNIFICANCE: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.
GOAL: To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). METHODS: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. RESULTS: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. CONCLUSION: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. SIGNIFICANCE: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.
Authors: Andrea Cimolato; Josephus J M Driessen; Leonardo S Mattos; Elena De Momi; Matteo Laffranchi; Lorenzo De Michieli Journal: J Neuroeng Rehabil Date: 2022-05-07 Impact factor: 5.208