Accuracy of sensor-based classification of clinically relevant motor activities in daily life of children with mobility impairments

BackgroundWearable inertial sensors enable objective, long-term monitoring of motor activities in the childrens habitual environment after rehabilitation. However, sophisticated algorithms are needed to derive clinically relevant outcome measures. Therefore, we developed three independent algorithms based on the needs of pediatric rehabilitation. The first algorithm estimates the duration of lying, sitting, and standing positions and the number of sit-to-stand transitions with data of a trunk and a thigh sensor. The second algorithm detects active wheeling periods and distinguishes it from passive wheeling with data of a wrist and a wheelchair sensor. The third algorithm detects walking periods, discriminates between free and assisted walking, and estimates the covered altitude change during stair climbing with data of a single ankle sensor and a sensor placed on walking aids. Research questionThis study aimed to determine the accuracy of each algorithm in children undergoing rehabilitation. MethodsThirty-one children and adolescents with various medical diagnoses and levels of mobility impairments performed a semi-structured activity circuit. They wore inertial sensors on both wrists, the sternum, and the thigh and shank of the less-affected side. Video recordings, which were labeled by two independent researchers, served as reference criteria to determine the algorithms performance. ResultsThe activity classification accuracy was 97% for the posture detection algorithm, 96% for the wheeling detection algorithm, and 93% for the walking detection algorithm. SignificanceThis study presents three novel algorithms that provide a comprehensive and clinically relevant view of the childrens motor activities. These algorithms are described reproducibly and can be applied to other inertial sensor technologies. Moreover, they were validated in children with mobility impairments and can be used in clinical practice and clinical trials to determine the childrens motor performance in their habitual environment. To enable the evaluation of future algorithms, we published the labeled dataset.