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: 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.

For the past three decades, neurosurgeons have utilized cranial neuro-navigation systems, bringing millimetric accuracy to operating rooms worldwide. These systems require an operating room team, anesthesia, and, most critically, cranial fixation. As a result, treatments for acute neurosurgical conditions, performed urgently in emergency rooms or intensive care units on awake and non-immobilized patients, have not benefited from traditional neuro-navigation. These emergent procedures are performed freehand, guided only by anatomical landmarks with no navigation, resulting in inaccurate catheter placement and neurological deficits. A rapidly deployable image-guidance technology that offers highly accurate, real-time registration and is capable of tracking awake, moving patients is needed to improve patient safety. The Zeta Cranial Navigation System is currently the only non-fiducial-based, FDA-approved neuro-navigation device that performs real-time registration and continuous patient tracking. To assess this system’s performance, we performed registration and tracking of phantoms and human cadaver heads during controlled motions and various adverse surgical test conditions. As a result, we obtained millimetric or sub-millimetric target and surface registration accuracy. This rapid and accurate frameless neuro-navigation system for mobile subjects can enhance bedside procedure safety and expand the range of interventions performed with high levels of accuracy outside of an operating room.

We present an actuator designed for untethered, lower-extremity powered-prostheses that replicates biological kinetic and kinematic function of both human knees and ankles. An electric energy optimal hardware specification is defined by kinematically clamping walking gait data to the dynamic model of a series elastic actuator (SEA) and searching for motor, reduction ratio, and spring. The actuator is shown to achieve the required torque, angle, and velocity requirements for nominal walking conditions on level ground as well as varied terrain. The performance of the actuator is demonstrated on benchtop and as worn by a human subject with unilateral below knee amputation. The resulting design is a moment-coupled cantilever-beam reaction-force SEA (MC-RFSEA) that has a nominal torque rating of 85Nm, repeated peak torque of 175Nm, 105 degree range of motion, and a hardware mass of 1.6kg. Preliminary results from level-ground walking with the actuator tested in an ankle configuration show an electric cost of transport of 0.053J/kg when walking at 1.5m/s.

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.

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.