Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery

Abstract

The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for “precision rehabilitation”. Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients’ responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.

Kinematic Evaluation via Inertial Measurement Unit Associated with Upper Extremity Motor Function in Subacute Stroke: A Cross-Sectional Study

“…is was analogous with a prior work by Adans-Dester et al, which used eight motor tasks of Wolf Motor Function Test (WMFT) and found satisfactory results to estimate upper limb impairment and activity scales [42]. Although this was in line with our second hypothesis, the difference was small and needed to be further studied [19].…”

“…erefore, future studies could implement other statistical models, such as machine-learning approaches, to investigate the associations between FNT kinematic variables and upper extremity motor function in individuals with stroke [42,44]. Second, this was a cross-sectional study and unable to investigate the longitudinal associations between kinematics and clinical measurements.…”

Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review

“…In this category, systems that aim to quantify the level of correctness in executing the prescribed exercises are identified. Researchers achieved this by using popular post-stroke assessment scoring systems [65][66][67][68][69][70][71][72][73][74]:…”

Differential cardiopulmonary monitoring system for artifact-canceled physiological tracking of athletes, workers, and COVID-19 patients

“…For example, when mounted on the neck or the chest, the recordings enable detailed assessments of cardiac activity from motions of the heart and from pulsatile flow of blood through near-surface arteries, of respiratory cycles from chest wall movements, of respiratory sounds from airflow through the lungs and trachea, of swallowing behaviors from laryngeal motions and actions of the esophagus, of vocalization patterns from vocal fold activation, and of movements and changes in orientation of the core body. Distinct features in the temporal and spectral characteristics of these processes yield insights into physical activity and health status via a rich range of conventional [e.g., heart rate (HR)] ( 36 , 40 ) and unconventional (e.g., coughing frequency) metrics ( 36 , 37 , 41 – 43 ), in a seamless manner, without privacy concerns that would follow from use of microphones or other recording devices.…”