A Two-Stage Tremor Detection Algorithm for Wearable Inertial Sensors During Normal Daily Activities
Published In
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Document Type
Citation
Publication Date
7-1-2019
Abstract
Continuous monitoring of tremor with wearable wrist sensors during normal daily activities is more difficult than in a clinical setting when subjects perform prescribed activities because some normal daily activities resemble tremor, many normal movements contain frequency content that overlaps with the tremor frequency, and the tremor amplitude has a large dynamic range during normal daily activities. We describe a novel two-stage algorithm that offers improvement at discriminating tremor from other activities. Some of this improvement is attained by using prior domain knowledge that tremor occurs over a narrow range of frequencies for an individual, but the mean tremor frequency may vary significantly between individuals in a study population. We validated the algorithm in continuous recordings from people with Parkinson's disease and matched control subjects. The algorithm has good face validity, a low rate of false positives on recordings from control subjects (less than 1.1%), and good correspondence with the constancy of rest tremor as measured by this question on the MDS-UPDRS (ρ = 0.54).
Locate the Document
DOI
10.1109/EMBC.2019.8857133
Persistent Identifier
https://archives.pdx.edu/ds/psu/34792
Publisher
IEEE
Citation Details
McNames, J., Shah, V. V., Mancini, M., Curtze, C., El-Gohary, M., Aboy, M., Carlson-Kuhta, P., Nutt, J. G., & Horak, F. (2019). A Two-Stage Tremor Detection Algorithm for Wearable Inertial Sensors During Normal Daily Activities. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/embc.2019.8857133