Feasibility test of activity index summary metric in human hand activity recognition
Activity monitoring is a technique for assessing the physical activity that a person undertakes over some time. Activity Index (AI) is a metric that summarizes the raw measurements from tri-axial accelerometers, often used for measuring physical activity. Our research compared the Activity Index for...
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Veröffentlicht in: | Serbian journal of electrical engineering 2022-01, Vol.19 (2), p.225-238 |
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Zusammenfassung: | Activity monitoring is a technique for assessing the physical activity that a
person undertakes over some time. Activity Index (AI) is a metric that
summarizes the raw measurements from tri-axial accelerometers, often used
for measuring physical activity. Our research compared the Activity Index
for different activity groups and hand usage [1]. We also tested this metric
as a classification feature, and how different data acquisition and
segmentation parameter configurations influence classification accuracy.
Data acquisition was done with a previously developed system that includes a
smartwatch on each wrist and a smartphone placed in the subject?s pocket;
raw data from smartwatch accelerometers was used for the analysis. We
calculated the Activity Index for labeled data segments and used ANOVA1
statistical test with Bonferroni correction. Significant differences were
found between cases of hand usage (left, right, none, both). In the next
analysis phase, the Activity Index was used as the classification feature
with three supervised machine learning algorithms-Support Vector Machine,
k-Nearest Neighbors, and Random Forest. The best accuracy (measured by F1
score) of classifying hand usage was achieved by using the Random Forest
algorithm, 50 Hz sampling frequency, and a window of 10 s without overlap
for AI calculation, and it was 97%. On the other hand, the classification of
activity groups had a low accuracy, which indicated that a specific activity
group can?t be identified by using only one simple feature. |
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ISSN: | 1451-4869 2217-7183 |
DOI: | 10.2298/SJEE2202225M |