Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

© 2022 by the authors; CC BY-NC-ND license

accelerometers; wearable sensors; exercise; measurement; GMFCS level; relative orientation estimation; IMU; magnetometer-free; gait analysis; machine learning; inertial measurement units; neurological disorders; falls; validity; 3-D motion analysis; single leg squat; motion capture; clinical; rehabilitation; motor function; wearable sensors; outcomes; measurement; implementation; machine learning; locomotion; assistive devices; embedded sensors; accelerometry; physical activity; Fourier transform; functional linear model; inertial measurement units; walking distance; lower limb amputation; rehabilitation; gait; Lie group; constrained extended Kalman filter; gait analysis; motion capture; pose estimation; wearable devices; IMU; distance measurement; gait planning; stride length; center of pressure; human–machine interaction; perinatal stroke; kinematics; upper extremity; cerebral palsy; hemiplegia; constraint; inertial measurement unit; wireless sensors network; motion tracking; kinematics; range of motion; shoulder; goniometer; spinal cord injury; tetraplegia; clinical setting; circadian motor behavior; body-worn sensors; older adults; physically active workers; low back pain; inertial motion units; wearable sensor; real-time gait detection; gait analysis; insole pressure sensors; inertial measurement unit; pathological gait; gait rehabilitation; assistive device; wearable technology; rehabilitation; stroke; implementation; physical therapy; stroke; upper extremity; arm use; upper limb performance; accelerometer; sensor; walking; rehabilitation; n/a