In this paper, we propose a mecanum-built perturbation-based balance training (M-PBT) device to train a person with a neurological disorder or an elderly person to regain their deteriorated motor adaptive skill to prevent a fall. The following are the features of the device: to challenge the trainees to predict the fall direction, the device (1) generates multi-directional fall options that simulate a slip and trip scenario; (2) is portable to assist in-patients’ rehabilitation; (3) possesses qualities of modified constraint-induced movement therapy (mCIMT).
FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.