Delaying Ambulation Mode Transition Decisions Improves Accuracy of a Flexible Control System for Powered Knee-Ankle Prosthesis

Abstract

Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study's goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis' mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable intent recognition system for a powered knee-ankle prosthesis.

Fig.1

A portion of the finite state machine is shown for walking mode only. Within-mode transitions that occurred based on mechanical sensor data are shown with black arrows. Between-mode transitions that occurred based on mechanical sensors are shown with blue arrows. Between-mode transitions that occur based on the execution of a key fob or a pattern recognition classifier are shown with green arrows. Walking mode transitions are displayed with solid lines and all outgoing ambulation mode transitions from walking mode are displayed with dashed lines.

Fig. 2

A transfemoral amputee wearing the powered knee-ankle prosthesis demonstrating various transitions from stair descent to level-ground walking.

Fig. 3

An overview of the mode-specific classifier architecture. State machine prosthesis modes are shown indicating the corresponding classifier (see Table 2 for descriptions) or mechanical trigger associated with each transition. Mechanical transitions are labeled in black, heel contact classifiers are in red, mid-stance classifier in green, toe off classifiers are in blue, and mid-swing classifier in purple. Ramp ascent data was grouped together with walking data.

Fig. 4

Example mechanical sensor data for the HC_LW classifier. The heel contact data window is shown for both the non-delayed and delayed system window for steady-state level-ground walking, level-ground walking to ramp descent transition and level-ground walking to stair descent transition. The delayed data window provides additional data after heel contact that is beneficial for separating classes.

Fig. 5

Average effect of non-delayed vs. delayed system on steady-state (left) and transitional (right) classification error. Heel contact error rates are the average of the HC_LW, HC_SD, HC_RD, and HC_ST classifiers. Toe off error rates are the average of the TO and TO_ST classifiers. Error bars indicate standard deviation. Note different vertical axis scaling between the left and right figures.