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Monitoring motor symptoms in Parkinson’s disease via instrumenting daily artifacts with inertia sensors

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Abstract

Daily monitoring of Parkinson’s disease is important since clinical assessments can only provide a brief and limited view of a patient’s condition. However, traditional approaches rely heavily on patients’ self-reports or diaries, which are subjective and often lack of necessary details. In this work, we instrument a handle that can be attached to cutlery with inertial sensors to collect motion data unobtrusively. By analyzing the data of patients and normal people collected in the clinic, we demonstrated that our machine learning based model can not only distinguish between patients and normal people, but also identify the disease levels in a fine-grained manner. To further understand how the self-tracking data is used in clinic, we conducted a semi-structured interview with several clinicians. Through the interpretation from the perspective of both physicians and patients, we found that our handle can help the physicians better understand disease progression and promote patients’ engagement in tackling the disease.

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Acknowledgements

We thank all subjects for participating in our study, and the clinicians of the Peking Union Medical College Hospital for their guidance. This work was supported by National Key R&D Program of China (Grant no. 2016YFB1001405).

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Authors and Affiliations

  1. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China

    Nianlong Li, Feng Tian, Xiangmin Fan, Hongan Wang & Guozhong Dai

  2. Beijing Key Lab of Human–Computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Nianlong Li, Feng Tian, Xiangmin Fan, Hongan Wang & Guozhong Dai

  3. Peking Union Medical College Hospital, Beijing, China

    Yicheng Zhu

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  1. Nianlong Li
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  2. Feng Tian
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  3. Xiangmin Fan
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Correspondence to Feng Tian.

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Li, N., Tian, F., Fan, X. et al. Monitoring motor symptoms in Parkinson’s disease via instrumenting daily artifacts with inertia sensors. CCF Trans. Pervasive Comp. Interact. 1, 100–113 (2019). https://doi.org/10.1007/s42486-019-00008-z

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  • DOI: https://doi.org/10.1007/s42486-019-00008-z

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