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.

河马疗法是一种针对脑瘫（CP）儿童的新型康复疗法。尽管已被证明在临床研究中是有效的，但是在先前的研究中仍缺乏对此类结果的定量评估。在这项研究中，提出了一种基于人体传感器网络（BSN）评估髋关节治疗效果的方法。该方法采用分布式磁，角速率和重力（MARG）传感器，通过多传感器数据融合算法评估CP儿童的总运动功能，与黄金标准光学捕获系统的比较结果证明了传感器融合算法的鲁棒性。此外，通过跟踪一年的河马疗法项目，进行了一项初步研究，以测量和评估CP儿童的运动协调功能和步态参数。最后，通过运动学分析评估所选受试者在不同治疗期间的总体运动功能的恢复情况。我们的方法的结果表明，一些儿童的症状如肢体僵硬，关节活动范围差，剪刀步态，膝盖屈曲步态已得到缓解。因此，它为接受CP儿童的运动功能康复提供了河马疗法的经验基础。