A wavelet-based approach to monitoring Parkinson's disease symptoms

Avishai Wagner; Naama Fixler; Yehezkel S. Resheff

doi:10.1109/ICASSP.2017.7953304

A wavelet-based approach to monitoring Parkinson's disease symptoms

Avishai Wagner
, Naama Fixler
, Yehezkel S. Resheff

Intel Advanced Analytics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

Parkinson's disease is a neuro-degenerative disorder affecting tens of millions of people worldwide. Lately, there has been considerable interest in systems for at-home monitoring of patients, using wearable devices which contain inertial measurement units. We present a new wavelet-based approach for analysis of data from single wrist-worn smart-watches, and show high detection performance for tremor, bradykinesia, and dyskinesia, which have been the major targets for monitoring in this context. We also discuss the implication of our controlled-experiment results for uncontrolled home monitoring of freely behaving patients.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5980-5984
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7953304
State	Published - 16 Jun 2017
Externally published	Yes
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: 5 Mar 2017 → 9 Mar 2017

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	0
ISSN (Print)	1520-6149

Conference

Conference	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	5/03/17 → 9/03/17

Keywords

Accelerometer
Monitoring
Parkinson's Disease
Wavelets
Wearable Devices

Access to Document

10.1109/ICASSP.2017.7953304

Cite this

Wagner, A., Fixler, N., & Resheff, Y. S. (2017). A wavelet-based approach to monitoring Parkinson's disease symptoms. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 5980-5984). Article 7953304 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7953304

Wagner, Avishai ; Fixler, Naama ; Resheff, Yehezkel S. / A wavelet-based approach to monitoring Parkinson's disease symptoms. 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 5980-5984 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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Wagner, A, Fixler, N & Resheff, YS 2017, A wavelet-based approach to monitoring Parkinson's disease symptoms. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7953304, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 5980-5984, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 5/03/17. https://doi.org/10.1109/ICASSP.2017.7953304

A wavelet-based approach to monitoring Parkinson's disease symptoms. / Wagner, Avishai; Fixler, Naama; Resheff, Yehezkel S.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 5980-5984 7953304 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Parkinson's disease is a neuro-degenerative disorder affecting tens of millions of people worldwide. Lately, there has been considerable interest in systems for at-home monitoring of patients, using wearable devices which contain inertial measurement units. We present a new wavelet-based approach for analysis of data from single wrist-worn smart-watches, and show high detection performance for tremor, bradykinesia, and dyskinesia, which have been the major targets for monitoring in this context. We also discuss the implication of our controlled-experiment results for uncontrolled home monitoring of freely behaving patients.

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KW - Monitoring

KW - Parkinson's Disease

KW - Wavelets

KW - Wearable Devices

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Wagner A, Fixler N, Resheff YS. A wavelet-based approach to monitoring Parkinson's disease symptoms. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 5980-5984. 7953304. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2017.7953304

A wavelet-based approach to monitoring Parkinson's disease symptoms

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Keywords

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