EMG-based prediction of step direction for a better control of lower limb wearable devices

• Published in: Computer methods and programs in biomedicine 2024-09, Vol.254, p.108305, Article 108305
• Main Authors: Anselmino, Eugenio, Mazzoni, Alberto, Micera, Silvestro
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Electromyography; EMG; Female; Gait; Humans; Intention decoding; Lower Extremity - physiology; Machine learning; Male; Muscle, Skeletal - physiology; Signal Processing, Computer-Assisted; Support Vector Machine; Walking - physiology; Wearable devices; Wearable Electronic Devices; Young Adult
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2024.108305

•A Toe-off intention decoder based on an SVM algorithm and sEMG signals has been proposed to detect step initiation intention independently from step direction.•A directional EMG decoder based on an SVM algorithm and sEMG signals has been proposed to classify step direction (forward, backward, right, or left).•The Toe-off and directional EMG decoder have been tested on 10 healthy subjects, using four sEMG electrodes placed on the thigh.•The directional EMG decoder reached a median accuracy of 90.65 % (IQR: 6.87) at the toe-off instant in its RBF kernel implementation, with an accuracy in detecting forward steps of 95.82 % (IQR: 5.23).•The Toe-off intention decoder identifies the movement onset with an 83.34 % (IQR: 6.48) accuracy in its RBF kernel implementation, with an accuracy in detecting the absence of intention of 92.72 % (IQR: 3.61). Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating conditions, such as amputations, neurodegenerative disorders, and stroke-related impairments. Current control approaches, limited to forward walking, fall short of replicating the complexity of human locomotion in complex environments, such as uneven terrains or crowded places. Here we propose a high-level controller based on two Support Vector Machines exploiting four surface electromyography (EMG) signals of the thigh muscles to detect the onset (Toe-off intention decoder) and the direction (Directional EMG decoder) of the upcoming step. We validated a preliminary version of the approach by acquiring EMG signals from ten healthy subjects, performing steps in four directions (forward, backward, right, and left), in three different settings (ground-level walking, stairs, and ramps), and in both steady-state and static conditions. Both the Toe-off intention and Directional EMG decoders have been tested with a 5-fold cross-validation repeated five times, using linear and radial-basis-function kernels, and by changing the classification output timing, from 200 ms before to 50 ms after the toe-off. The Toe-off intention decoder reached a median accuracy of 83.34 % (interquartile range (IQR): 6.48) and specificity of 92.72 % (IQR: 3.62) in its radial-basis-function version, while the Directional EMG decoder's median accuracy ranged between 73.92 % (IQR: 5.8), 200 ms before the toe-off, to 92.91 % (IQR: 4.11), 50 ms after the toe-off, with the radial-basis-function kernel implementation. For both the Toe-off inten