Real-Time Hierarchical Classification of Time Series Data for Locomotion Mode Detection

Objective: Accurate real-time estimation of motion intent is critical for rendering useful assistance using wearable robotic prosthetic and exoskeleton devices during user-initiated motions. We aim to evaluate hierarchical classification as a strategy for real-time locomotion mode recognition for the control of wearable robotic prostheses and exoskeletons during user-initiated motions. Methods: We collect motion data from 8 subjects using a set of 7 inertial sensors for 16 lower limb locomotion modes of different specificities. A CNN based hierarchical classifier is trained to classify the modes into a specified label hierarchy. We measure the accuracy, stability, behaviour during mode transitions and suitability for real-time inference of the classifier. Results: The method achieves stable classification of locomotion modes using 1280 ms of time history data. It achieves average classification accuracy of 94.34% and an average AU ((PRC) over bar) of 0.773 - comparable to similar classifiers. The method produces more informative classifications at transitions between modes. Less specific classes are classified earlier than more specific classes in the hierarchy. The inference step of the classifier can be executed in less than 2 ms on embedded hardware, indicating suitability for real-time operation. Conclusion: Hierarchical classification can achieve accurate detection of locomotion modes and can break up mode transitions into multiple transitions between modes of different specificity. Significance: Multi-specific hierarchical classification of locomotion modes could lead to smoother, more fine grained control adaptation of wearable robots during locomotion mode transitions.