Optimal Placement of Accelerometers for the Detection of Everyday Activities
By I. Cleland, Basel Kikhia, C. Nugent, A. Boytsov, J. Hallberg, Kåre Synnes, S. McClean, D. Finlay. Italian National Conference on Sensors, 2013
392 citations recorded by the source corpus.
This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physiology
View Paper: https://www.semanticscholar.org/paper/0dee5da742d606dbe4eb0062ebac16b6c6f52f29
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In Biomedical Engineering
In Computer Vision and Pattern Recognition
Citations in the corpus
- SisFall: A Fall and Movement Dataset (2017, methodology): “Here, we fixed our device as a belt buckle as recommended in previous works [24,25].”
- Physical Human Activity Recognition Using Wearable Sensors (2015, methodology): “To extract features from raw data, windowing techniques are generally used, which consist of dividing sensor signals into small time segments.”