LAUSR

Objective: Remote monitoring of physical activity using body-worn sensors provides an objective alternative to current functional assessment tools. The purpose of this study was to assess the feasibility of classifying categories of activities of daily living from the functional arm activity behavioral observation system (FAABOS) using muscle activation and motion data. Methods: Ten nondisabled, healthy adults were fitted with a Myo armband on the upper forearm. This multimodal commercial sensor device features surface electromyography (sEMG) sensors, an accelerometer, and a rate gyroscope. Participants performed 17 different activities of daily living, which belonged to one of four functional groups according to the FAABOS. Signal magnitude area (SMA) and mean values were extracted from the acceleration and angular rate of change data; root mean square (RMS) was computed for the sEMG data. A $k-$ nearest neighbors machine learning algorithm was then applied to predict the FAABOS task category using these raw data as inputs. Results: Mean acceleration, SMA of acceleration, mean angular rate of change, and RMS of sEMG were significantly different across the four FAABOS categories ( $p<0.001$ in all cases). A classifier using mean acceleration, mean angular rate of change, and sEMG data was able to predict task category with 89.2% accuracy. Conclusion: The results demonstrate the feasibility of using a combination of sEMG and motion data to noninvasively classify types of activities of daily living. Significance: This approach may be useful for quantifying daily activity performance in ambient settings as a more ecologically valid measure of function in healthy and disease-affected individuals.