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

Author:

Li Nianlong,Tian Feng,Fan Xiangmin,Zhu Yicheng,Wang Hongan,Dai Guozhong

Funder

National Key R&D Program of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction

Reference57 articles.

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2. Agostino, R., Berardelli, A., Currà, A., Accornero, N., Manfredi, M.: Clinical impairment of sequential finger movements in Parkinson’s disease. Mov. Disord. 13(3), 418–421 (1998)

3. Bhavana, C., Gopal, J., Raghavendra, P., Vanitha, K., Talasila, V.: Techniques of measurement for Parkinson’s tremor highlighting advantages of embedded imu over emg. In: Recent trends in information technology (ICRTIT), 2016 international conference on, pp. 1–5. IEEE (2016)

4. Bonato, P., Sherrill, D.M., Standaert, D.G., Salles, S.S., Akay, M.: Data mining techniques to detect motor fluctuations in Parkinson’s disease. In: Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual International Conference of the IEEE, vol. 2, pp. 4766–4769. IEEE (2004)

5. Brown, R.G., MacCarthy, B., Jahanshahi, M., Marsden, C.D.: Accuracy of self-reported disability in patients with Parkinsonism. Arch. Neurol. 46(9), 955–959 (1989)

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