Gait event prediction from surface electromyography in parkinsonian patients

Gait disturbances are common manifestations of Parkinsons disease (PD), with unmet therapeutic needs. Inertial measurement units (IMU) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine-learning approach to approximate IMU angular velocity profiles, and subsequently gait events from electromyographic (EMG) channels. We recorded six parkinsonian patients while walking for at least three minutes. Patient-agnostic regression models were trained on temporally-embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement <50 msec), low numbers of missed events (<2%), and next to no false positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1 score [~]0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction while allowing the simultaneous acquisition of an electromyographic signal. This gait analysis approach has the potential to make additional measurement devices such as IMU and force plates less essential, and thereby to reduce financial and preparation overheads and discomfort factors in gait studies.