Is "frequency distribution" enough to detect tremor in PD patients using a wrist worn accelerometer?
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10.4108/icst.pervasivehealth.2014.254928
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/27688
Tipus de documentText en actes de congrés
Data publicació2014
EditorAssociation for Computing Machinery (ACM)
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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.
CitacióAhlrichs, C.; Sama, A. Is "frequency distribution" enough to detect tremor in PD patients using a wrist worn accelerometer?. A: 8th International Conference on Pervasive Computing Technologies for Healthcare. "PervasiveHealth '14 8th International Conference on Pervasive Computing Technologies for Healthcare Oldenburg, Germany — May 20 - 23, 2014". Oldenburg: Association for Computing Machinery (ACM), 2014, p. 65-71.
ISBN978-1-63190-011-2
Versió de l'editorhttp://eudl.eu/doi/10.4108/icst.pervasivehealth.2014.254928
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