The TF8 actuator is an untethered, lower-extremity powered-prostheses designed to replicate biological kinetic and kinematic function of ankles. An energy optimal hardware specification was found by kinematically clamping walking gait data to the dynamic model of a series elastic actuator (SEA). We searched for a minimal electrical energy configuration of motor, reduction ratio, and spring, subject to specified constraints and ultimately discretely available components. The outcome translated into a mechanical design that heavily weighted the importance of mechanical energy storage in springs. The resulting design is a moment-coupled cantilever-beam reaction-force SEA (RFSEA) that has a nominal torque rating of 85Nm, peak torque of 175Nm, 105 degree range of motion, and a hardware mass of 1.6kg.

We present electric energetic consequences for mechanical design trade-offs in lower-extremity powered prostheses. There are four main hardware components commonly implemented in these devices that can be tuned to achieve desired performance: motor, reduction ratio N, series spring stiffness Ks, and parallel spring stiffness Kp. The allowed joint range of motion is a fifth parameter that can also drastically change energy consumption. We apply a kinematically clamped analysis to the system equations to map the electric cost of transport (COT) for knee and ankle level-ground walking, in addition to ankle stair ascent and descent. We also utilize an optimization procedure to identify minimum energy hardware configurations. The energy map provides insight into consequences of variance from optimal parameters. Our results support the contribution of the series elastic element for improved power output. Parallel stiffness can provide up to 8% improvements in walking with minimal negative effect with varied terrain, and a varying ankle transmission ratio can similarly improve COT by 8% from level-ground to stair ascent. Limited dorsiflexion can further improve COT by 30%. These observations can provide the designer clarity to how design decisions modulate hardware performance.

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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. 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 two terrain prediction algorithms. The first employed a state-of-the-art machine learning approach, while the second 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 algorithm and overall error for the heuristic algorithm. 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. 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 state-of-the-art machine learning methods, and relies only on integrated prosthesis sensors. Finally, the method provides intuitively tunable thresholds to improve performance for specific walking conditions.

Objective: This paper describes the developmentand preliminary offline validation of an algorithm facilitatingautomatic, self-contained learning of ground terrain transitionsin a lower limb prosthesis. This method allows for continuous,in-field convergence on an optimal terrain prediction accuracyfor a given walking condition, and is thus not limited bythe specific conditions and limited sample size of an in-labtraining scheme. Methods: We asked one subject with a below-kneeamputation to traverse level ground, stairs, and rampsusing a high-range-of-motion powered prosthesis while internalsensor data were remotely logged. We then used these datato develop a dynamic classification algorithm which predictsthe terrain of each stride and then continuously updates thepredictor using both data from the previous stride and anaccurate terrain back-estimation algorithm. Results: Across 100simulations randomizing stride order, our method attained amean next-stride prediction accuracy of ? 96%. This valuewas first reached after ? 200 strides, or about ? 5 minutesof walking. Conclusion and significance: These results demonstratea method for automatically learning the gait patternspreceding terrain transitions in a prosthesis without relyingon any external devices. By virtue of its dynamic learningscheme, application of this method in real-time would allow forcontinuous, in-field optimization of prediction accuracy across avariety of walking variables including physiological conditions,variable terrain geometries, control methodologies, and users.