Accurate Heuristic Terrain Prediction in Powered Lower-Limb Prostheses Using Onboard Sensors

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 2; pp. 384 - 392
• Main Authors: Stolyarov, Roman, Carney, Matthew, Herr, Hugh
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ankle; Errors; Estimation; Gait; Geometry; Heuristic; Heuristic methods; inertial measurement; Inertial platforms; Intent recognition; Joints (anatomy); Kinematics; Learning algorithms; Legged locomotion; Machine learning; Pattern recognition; Performance enhancement; Prediction algorithms; Predictions; Prostheses; Prosthetics; Ramps; Remote sensors; Sensors; Terrain; Thresholds; Torque; Translations; Walking; wearable robotics
• DOI: 10.1109/TBME.2020.2994152
• ISSN: 0018-9294; 1558-2531

Objective: This study describes the development and offline validation of a heuristic algorithm for accurate prediction of ground terrain in a lower limb prosthesis. This method is based on inference of the ground terrain geometry using estimation of prosthetic limb kinematics during gait with a single integrated inertial measurement unit. Methods: We asked five subjects with below-knee amputations to traverse level ground, stairs, and ramps using a high-range-of-motion powered prosthesis while internal sensor data were remotely logged. We used these data to develop three terrain prediction algorithms. The first two employed state-of-the-art machine learning approaches, while the third was a directly tuned heuristic using thresholds on estimated prosthetic ankle joint translations and ground slope. We compared the performance of these algorithms using resubstitution error for the machine learning algorithms and overall error for the heuristic algorithm. Results: Our optimal machine learning algorithm attained a resubstitution error of 3.4% using 45 features, while our heuristic method attained an overall prediction error of 2.8% using only 5 features derived from estimation of ground slope and horizontal and vertical ankle joint displacement. Compared with pattern recognition, the heuristic performed better on each individual subject, and across both level and non-level strides. Conclusion and significance: These results demonstrate a method for heuristic prediction of ground terrain in a powered prosthesis. The method is more accurate, more interpretable, and less computationally expensive than machine learning methods considered state-of-the-art for intent recognition, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.