TY - GEN
T1 - Detection of Tremor Associated with Rest and Effort Activity Using Machine Learning
AU - Aljihmani, Lilia
AU - Kerdjidj, Oussama
AU - Zhu, Yibo
AU - Mehta, Ranjana K.
AU - Qaraqe, Khalid
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Tremor or handshaking can be provoked by neurological diseases like Parkinson's, physical efforts, stress, medicine, etc. To investigate the tremor characteristics, we asked volunteers to make three kinds of exercises: postural, rest, and effort. Accelerometer data were collected using sensors positioned on the wrist and finger of the participant's dominant hand. In the study, models for estimation and prediction of the tremor was generated. Machine learning was applied to detect and classify rest, effort, and postural tasks according to two scenarios. In the first scenario, we separated the tasks into three classes, while in the second one, two classes (effort and rest) were used. To compute the statistical features as maximum, minimum, and mean amplitude, number of peaks above the mean, standard deviation (STD), root mean square (RMS), and Pearson correlation of the finger and wrist acceleration, the accelerometer data were divided on windows with different length (128, 256, 320, and 512 samples per window). The following algorithms were used to build the models for the events' classification: decision tree, support vector machine, k-nearest neighbor, and ensemble bagging classifier. A cross-validation method was applied to train and test the models. The achieved performance of the models was from 85.0% to 94.1% for the 3-classes scenario and 86.5% to 94.9% for the two-classes scenario.
AB - Tremor or handshaking can be provoked by neurological diseases like Parkinson's, physical efforts, stress, medicine, etc. To investigate the tremor characteristics, we asked volunteers to make three kinds of exercises: postural, rest, and effort. Accelerometer data were collected using sensors positioned on the wrist and finger of the participant's dominant hand. In the study, models for estimation and prediction of the tremor was generated. Machine learning was applied to detect and classify rest, effort, and postural tasks according to two scenarios. In the first scenario, we separated the tasks into three classes, while in the second one, two classes (effort and rest) were used. To compute the statistical features as maximum, minimum, and mean amplitude, number of peaks above the mean, standard deviation (STD), root mean square (RMS), and Pearson correlation of the finger and wrist acceleration, the accelerometer data were divided on windows with different length (128, 256, 320, and 512 samples per window). The following algorithms were used to build the models for the events' classification: decision tree, support vector machine, k-nearest neighbor, and ensemble bagging classifier. A cross-validation method was applied to train and test the models. The achieved performance of the models was from 85.0% to 94.1% for the 3-classes scenario and 86.5% to 94.9% for the two-classes scenario.
KW - activity recognition
KW - decision tree
KW - ensemble classifier
KW - k-nearest neighbor
KW - support vector machine
KW - tremor
UR - http://www.scopus.com/inward/record.url?scp=85100104792&partnerID=8YFLogxK
U2 - 10.1109/ICAI50593.2020.9311368
DO - 10.1109/ICAI50593.2020.9311368
M3 - Conference contribution
AN - SCOPUS:85100104792
T3 - 2020 International Conference Automatics and Informatics, ICAI 2020 - Proceedings
BT - 2020 International Conference Automatics and Informatics, ICAI 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference Automatics and Informatics, ICAI 2020
Y2 - 1 October 2020 through 3 October 2020
ER -