Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

• Published in: Journal of neural engineering Vol. 11; no. 5; pp. 56021 - 12
• Main Authors: Young, A J, Kuiken, T A, Hargrove, L J
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.10.2014
• Subjects: Actigraphy - instrumentation; Actigraphy - methods; Adult; Algorithms; Amputation Stumps - physiopathology; Amputees - rehabilitation; Artificial Limbs; Attitude; biomedical signal classification; Dynamical systems; Dynamics; Electromyography - methods; EMG signal processing; Female; Humans; Intention; Leg - physiopathology; Limbs; Locomotion; lower limb prostheses; Male; Man-Machine Systems; Micro-Electrical-Mechanical Systems - methods; Middle Aged; Pattern Recognition, Automated - methods; Prostheses; Prosthetics; Recognition; Reproducibility of Results; Sensitivity and Specificity; sensor fusion; Sensors; Surgical implants; Young Adult
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2560/11/5/056021

Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition-based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis-such as inertial measurement units, position and velocity sensors, and load cells-may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.