TY - GEN
T1 - A Two-Stage Tremor Detection Algorithm for Wearable Inertial Sensors during Normal Daily Activities
AU - McNames, James
AU - Shah, Vrutangkumar V.
AU - Mancini, Martina
AU - Curtze, Carolin
AU - El-Gohary, Mahmoud
AU - Aboy, Mateo
AU - Carlson-Kuhta, Patricia
AU - Nutt, John G.
AU - Horak, Fay
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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 (< 1.1%), and good correspondence with the constancy of rest tremor as measured by this question on the MDS-UPDRS (ρ = 0.54).
AB - 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 (< 1.1%), and good correspondence with the constancy of rest tremor as measured by this question on the MDS-UPDRS (ρ = 0.54).
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U2 - 10.1109/EMBC.2019.8857133
DO - 10.1109/EMBC.2019.8857133
M3 - Conference contribution
C2 - 31946413
AN - SCOPUS:85077886309
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 2535
EP - 2538
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -