Abstract
Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.
Highlights
IntroductionWith the rapid development of wearable robotic system, many new intelligent products have been introduced, e.g., exoskeleton assist device and powered lower limb prosthesis
A single inertial measurement unit (IMU) with a short sampling time window was used in the current study; it is impossible to assess the merits of the original values and subjective selection of eigenvalues
The confusion matrix for the improved backpropagation neural network (IBPNN)-decision tree structure (DTS) A is shown in Figure 5c, and the results indicate that miscalculation between level walking, ramp ascent (RA), ramp descent (RD), sit, and stand persists
Summary
With the rapid development of wearable robotic system, many new intelligent products have been introduced, e.g., exoskeleton assist device and powered lower limb prosthesis. These products can effectively improve the athletic ability of healthy people and recover the daily life ability of the disabled. The intelligence of wearable robotic system is reflected in the fact that such devices, e.g., Rewalk Personal 6.0 from Rewalk Robotics and Genium X3 from Ottobock can understand human thoughts and realize corresponding functions according to human intent. The development of locomotion mode recognition determines the upper limit of wearable robotic system, which is the most critical link relative to elevating a machine into a robot
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