Abstract
Cerebral palsy (CP) is a group of nonprogressive neuro-developmental conditions occurring in early childhood that causes movement disorders and physical disability. Measuring activity levels and gait patterns is an important aspect of rehabilitation programs for CP. Hippotherapy is a rehabilitation method to improve motor coordination ability of children with CP. However, there is still no practical evidence for the effectiveness of hippotherapy. This paper introduces a method of motor measurement and evaluation for children with CP based on body area sensor network. Our method uses wearable inertial sensors to measure the motor function of children with CP by sensor fusion algorithm, whose accuracy is verified by optical system. In addition, via introducing the control group, the differences of motor coordination ability and gait parameters between CP and healthy children were discussed. Generally speaking, our method can effectively measure the movement posture and gait parameters of children with CP during hippotherapy, which provides a basis for proving the effectiveness of hippotherapy.
This work was supported by National Natural Science Foundation of China under Grant No. 61873044, No. 61803072 and No. 61903062 Dalian Science and Technology Innovation fund (2018J12SN077, 2019J13SN99), National Defense Pre-Research Foundation under Grant No. 614250607011708, Fundamental Research Funds for the Central Universities (DUT18RC(4)036) and China Postdoctoral Science Foundation No. 2017M621131 and No. 2017M621132.
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Acknowledgment
we would like to express our sincere thanks to Dalian Qiyu Ipony Equestrian Club Co. Ltd and Suzhou Industrial Park Boai School & Clinic for their support of this study.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Qiu, S. et al. (2019). Using Distributed Wearable Inertial Sensors to Measure and Evaluate the Motions of Children with Cerebral Palsy in Hippotherapy. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_25
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DOI: https://doi.org/10.1007/978-3-030-34833-5_25
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