OBJECTIVE: Walking task prediction in powered leg prostheses is an important problem in the development of biomimetic prosthesis controllers. This paper proposes a novel method to predict upcoming walking tasks by estimating the translational motion of leg joints using an integrated inertial measurement unit. METHODS: We asked six subjects with unilateral transtibial amputations to traverse flat ground, ramps, and stairs using a powered prosthesis while inertial signals were collected. We then performed an offline analysis in which we simulated a real-time motion tracking algorithm on the inertial signals to estimate knee and ankle joint translations, and then used pattern recognition separately on the inertial and translational signal sets to predict the target walking tasks of individual strides. RESULTS: Our analysis showed that using inertial signals to derive translational signals enabled a prediction error reduction of 6.8% compared to that attained using the original inertial signals. This result was similar to that seen by addition of surface electromyography sensors to integrated sensors in previous work, but was effected without adding any extra sensors. Finally, we reduced the size of the translational set to that of the inertial set and showed that the former still enabled a composite error reduction of 5.8%. CONCLUSION AND SIGNIFICANCE: These results indicate that translational motion tracking can be used to substantially enhance walking task prediction in leg prostheses without adding external sensing modalities. Our proposed algorithm can thus be used as a part of a task-adaptive and fully integrated prosthesis controller.
OBJECTIVE: Walking task prediction in powered leg prostheses is an important problem in the development of biomimetic prosthesis controllers. This paper proposes a novel method to predict upcoming walking tasks by estimating the translational motion of leg joints using an integrated inertial measurement unit. METHODS: We asked six subjects with unilateral transtibial amputations to traverse flat ground, ramps, and stairs using a powered prosthesis while inertial signals were collected. We then performed an offline analysis in which we simulated a real-time motion tracking algorithm on the inertial signals to estimate knee and ankle joint translations, and then used pattern recognition separately on the inertial and translational signal sets to predict the target walking tasks of individual strides. RESULTS: Our analysis showed that using inertial signals to derive translational signals enabled a prediction error reduction of 6.8% compared to that attained using the original inertial signals. This result was similar to that seen by addition of surface electromyography sensors to integrated sensors in previous work, but was effected without adding any extra sensors. Finally, we reduced the size of the translational set to that of the inertial set and showed that the former still enabled a composite error reduction of 5.8%. CONCLUSION AND SIGNIFICANCE: These results indicate that translational motion tracking can be used to substantially enhance walking task prediction in leg prostheses without adding external sensing modalities. Our proposed algorithm can thus be used as a part of a task-adaptive and fully integrated prosthesis controller.
Authors: Kyle R Embry; Dario J Villarreal; Rebecca L Macaluso; Robert D Gregg Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2018-11-05 Impact factor: 3.802