Is "frequency distribution" enough to detect tremor in PD patients using a wrist worn accelerometer?
Pages 65 - 71
Abstract
This paper presents two approaches on detecting tremor in patients with Parkinson's Disease by means of a wrist-worn accelerometer. Both approaches are evaluated in terms of specificity and sensitivity as well as their applicability for a real-time implementation. One approach is solely based on the frequency distribution of a windowed time series, while the second approach utilizes commonly employed features found in the literature (e.g. FFT, entropy, peak frequency, correlation). The two algorithms detect tremor at rest in windowed time series. The effects of varying window lengths and detection thresholds are studied. The results indicate that an SVM with a linear kernel, in combination with the frequency distribution, may already be enough to accurately and reliably detect tremor in windowed time series. The approach, after being trained with a first dataset of signals obtained from 12 patients, achieved a sensitivity of 88.4% and specificity of 89.4% in a second dataset from 64 PD patients.
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- Is "frequency distribution" enough to detect tremor in PD patients using a wrist worn accelerometer?
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May 2014
459 pages
ISBN:9781631900112
- General Chair:
- Andreas Hein,
- Program Chairs:
- Susanne Boll,
- Friedrich Köhler
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ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)
Brussels, Belgium
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Published: 20 May 2014
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PervasiveHealth '14
PervasiveHealth '14: 8th International Conference on Pervasive Computing Technologies for Healthcare
May 20 - 23, 2014
Oldenburg, Germany
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