Multi-body sensor data fusion to evaluate the hippotherapy for motor ability improvement in children with cerebral palsy

Abstract

Hippotherapy is a new rehabilitation therapy for children with cerebral palsy (CP). Although it has been proved to be effective in clinical research, a quantitative evaluation of such results is still lacking in previous studies. In this research, one method for evaluating the effectiveness of hippotherapy based on body sensor network (BSN) is proposed. The method adopts distributed magnetic, angular rate, and gravity (MARG) sensors to evaluate the gross motor function of CP children by multi-sensor data fusion algorithm, the comparison results with the golden standard optical capture system demonstrate the robustness of sensor fusion algorithm. Moreover, via tracking one-year’s hippotherapy projects, a pilot study was conducted for measuring and evaluating the motor coordination function and gait parameters of CP children. Finally, the rehabilitation of gross motor function for the chosen subjects under different treatment periods was evaluated through kinematic analysis. The results of our method show that some children’s symptoms, such as limb stiffness, poor joint range of motion, scissors gait, knee flexion gait, have been relieved. Thus, it provides an empirical basis for hippotherapy in the rehabilitation of motor function corresponding to CP children.