2022
DOI: 10.3390/s22197482
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Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Abstract: Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the si… Show more

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Cited by 12 publications
(8 citation statements)
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“…Prior studies have used colocated investigational and reference sensors (eg, 2 wrist-worn devices). But because of the known potential errors associated with body placement when capturing walking-related data [36][37][38][39], colocation could be vulnerable to bias toward overestimating performance. Our approach sought to mitigate that by using a highly accurate but pragmatic and ankle-worn source for ground truth labels.…”
Section: Discussionmentioning
confidence: 99%
“…Prior studies have used colocated investigational and reference sensors (eg, 2 wrist-worn devices). But because of the known potential errors associated with body placement when capturing walking-related data [36][37][38][39], colocation could be vulnerable to bias toward overestimating performance. Our approach sought to mitigate that by using a highly accurate but pragmatic and ankle-worn source for ground truth labels.…”
Section: Discussionmentioning
confidence: 99%
“…Prior studies have used co-located investigational and reference sensors (e.g., two wrist-worn devices). Due to the known potential errors associated with body placement when capturing walking-related data [36][37][38][39], co-location could be vulnerable to bias towards overestimating performance, which our approach seeks to mitigate. Further, most studies have had a narrow focus on step counts [10][11][12][13][14][15][16], mostly in controlled laboratory environments and/or for limited time periods (e.g., single day in real-world setting).…”
Section: Discussionmentioning
confidence: 99%
“…This is especially useful in recognizing periodic or rhythmic activities, as the model captures the repeating patterns inherent in activities like walking, running, or cycling. By comparing the predicted and actual values, deviations can be detected, helping to identify anomalies or changes in activity patterns ( Bennasar et al, 2022 ; Liu et al, 2021 ). For example, variations in step lengths, gait irregularities, or sudden changes in motion can be indicative of different activities or health conditions ( Wen et al, 2023 ).…”
Section: Methodsmentioning
confidence: 99%
“…In order to validate and evaluate the robustness of the proposed feature set, we compare the extracted features in this study with other latest existing state-of-the-art methods. Initially, we categorize all features into different sets, from the latest SOTA systems ( Bennasar et al, 2022 ; Tian et al, 2019 ; Muaaz et al, 2023 ). The features are partitioned into 4 sets.…”
Section: Methodsmentioning
confidence: 99%