2018
DOI: 10.1166/jmihi.2018.2232
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Optimization of Sensor Placement Combinations and Classification Thresholds for the Accelerometer-Based Activity Recognition

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Cited by 3 publications
(5 citation statements)
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“…However, these data were collected by using a structured format, for short durations (<10 min), and were, therefore, unlikely to capture the posture variations present in unobserved free-living. This variation would reduce the accuracy of threshold-based classifiers, and this is supported by the findings of Kwon et al [11], who found poorer classification accuracy (66.7%, SD = 6.0%) when using the same classifier on free-living data. Archer et al [12] attempted to classify postures using both accelerometers and gyroscopes along with a decision tree and k-nearest neighbour classifier, achieving 95% sensitivity.…”
Section: Introductionmentioning
confidence: 54%
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“…However, these data were collected by using a structured format, for short durations (<10 min), and were, therefore, unlikely to capture the posture variations present in unobserved free-living. This variation would reduce the accuracy of threshold-based classifiers, and this is supported by the findings of Kwon et al [11], who found poorer classification accuracy (66.7%, SD = 6.0%) when using the same classifier on free-living data. Archer et al [12] attempted to classify postures using both accelerometers and gyroscopes along with a decision tree and k-nearest neighbour classifier, achieving 95% sensitivity.…”
Section: Introductionmentioning
confidence: 54%
“…The performance achieved by these models highlights the advantage of developing a classifier using machine learning methods as opposed to threshold-based classifiers. Previous research has shown that the accuracy of threshold-based classifiers is reduced by up to 40% when tested on free-living data compared to previously presented results on laboratory data [11]. This has been attributed to the variation in the execution of different postures typically found in free-living conditions, as opposed to the strict and considered movements performed in a laboratory.…”
Section: Discussionmentioning
confidence: 74%
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“…Kwon et al . studied the wearing position of the sensor in human motion recognition and set the wireless three‐axis accelerators at different positions. The experimental results showed that the wearing position with the highest recognition rate of single‐sensor was the wrist, the best combination of sensors was to wear them at arms and thighs, and the best combination of three sensors was to wear them at arms, thighs, and ankles.…”
Section: Introductionmentioning
confidence: 99%
“…Hsu et al [6] identified 10 common family activities and 11 sports activities using two inertial sensors and found that the recognition rates were 98.23 and 99.55% respectively, which verified the reliability of the method. Kwon et al [7] studied the wearing position of the sensor in human motion recognition and a Correspondence to: Lei Zhao. E-mail: zleizhaol@yeah.net Department of Physical Education, Tianjin University of Commerce, Tianjin, 300134, China set the wireless three-axis accelerators at different positions.…”
Section: Introductionmentioning
confidence: 99%