디지털 디바이스를 이용한 이상운동증에서의 운동손상 정량화 방법
A Digital Device-Based Method for Quantifying Motor Impairment in Movement Disorders
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초록
Accurate diagnosis of movement disorders is important for providing right patient care at right time. In general, assessment of motor impairment relies on clinical ratings conducted by experienced clinicians. However, this may introduce subjective opinions into scoring the severity of motor impairment. Digital devices such as table PC and smart band with accelerometer can be used for more accurate and objective assessment and possibly helpful for clinicians to make right decision of patient’s states. In this study, we introduce quantification algorithms of motor impairment which uses the digital data acquired during four clinical motor tests (Line drawing, Spiral drawing, Nose to finger and Hand flip tests). The step by step procedure of quantifying metrics (Tremor Frequency, Tremor Magnitude, Error Distance, Time, Velocity, Count and Period) are provided with flowchart. The effectiveness of the proposed algorithm is presented with the result from simulated data (normal, normal with tremor and slowness, poor with tremor, poor with tremor and slowness).
Abstract
Accurate diagnosis of movement disorders is important for providing right patient care at right time. In general, assessment of motor impairment relies on clinical ratings conducted by experienced clinicians. However, this may introduce subjective opinions into scoring the severity of motor impairment. Digital devices such as table PC and smart band with accelerometer can be used for more accurate and objective assessment and possibly helpful for clinicians to make right decision of patient’s states. In this study, we introduce quantification algorithms of motor impairment which uses the digital data acquired during four clinical motor tests (Line drawing, Spiral drawing, Nose to finger and Hand flip tests). The step by step procedure of quantifying metrics (Tremor Frequency, Tremor Magnitude, Error Distance, Time, Velocity, Count and Period) are provided with flowchart. The effectiveness of the proposed algorithm is presented with the result from simulated data (normal, normal with tremor and slowness, poor with tremor, poor with tremor and slowness).
참고문헌 (12)
- Koh SB, "Diagnosis and Treatment of Parkinson’s Disease", J Korean Acad Fam Med, 24, pp.1059~1068 (2003)
- 권다은, "파킨슨병의 운동적 특징 및 공학적 정량화 방법에 대한 조사", 정보과학회논문지, 46(4), pp.308~320 (2019), 한국정보과학회[DOI]
- James KF, "Quantifying Motor Impairment in Movement Disorders", FronT Neurosci, 12, pp.202~ (2018)
- Arora S, "Detecting and monitoring the symptoms of Parkinson's disease using smartphones : A pilot study", Parkinsonism & Related Disorders, 21, pp.650~653 (2015)
- Jang W, "Waveform analysis of tremor may help to differentiate Parkinson's disease from drug-induced parkinsonism", Physiological measurement, 34(3), pp.N15~ (2013)
- 정선주, "손떨림의 진단과 치료", 대한의사협회지, 55(10), pp.987~995 (2012), 대한의사협회[DOI]
- Kim JM, "Tremor", 2015 Education Symposium for Neurology Trainee on Movement Disorder Basics, pp.51~55 (2015)
- Lee KW, "Estimation of acceleration by noise rejection from velocity signals using Smoothing technique", J KOREAN SOC RAILW, 10, pp.247~251 (2003)
- 김연태, "가속도 센서를 이용한 수면 무호흡증 모니터링 시스템", 석사, 전자공학과, pp.2014
- 유주연, "3축 가속도 센서 기반의 무선 진동 측정 시스템", 센서학회지, 20(2), pp.131~136 (2011), 한국센서학회