Wearable Technology and Machine Learning to Monitor Upper-Limb Use in Brain Injury Survivors

The study explored the use of machine learning-based algorithms to estimate clinical scores (meant to capture the quality of upper-limb movements) via the analysis of wearable sensor data collected during the performance of functional tasks. Data was recorded using accelerometer units from thirty-seven individuals with upper-limb hemiparesis due to acquired brain injury while they performed eight activities of daily living. Clinicians assessed the study participants' quality of movement using the Functional Ability Scale. A hierarchical Random Forest-based algorithm was developed to derive the Functional Ability Scale scores from the accelerometer data. The results showed a high correlation (R
²
=0.91) between the estimates derived from the accelerometer data and the scores provided by the clinicians. The method herein presented has the potential to provide a means to track the motor recovery process in patients experiencing upper-limb motor impairments following a stroke or a traumatic brain injury hence enabling precision rehabilitation interventions.