2018 13th International Conference on Computer Engineering and Systems (ICCES) 2018
DOI: 10.1109/icces.2018.8639309
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Daily Activity Recognition using Wearable Sensors via Machine Learning and Feature Selection

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Cited by 7 publications
(11 citation statements)
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“…As mentioned earlier [6] and boldfaced in Table 5, the best accuracy is achieved using the RF classifier. e best single axis achieved 94.7% with 28 features, and the combination of all parameters achieved 96.8% with 112 features.…”
Section: Comparison Between Single Axis Vs Triple Axes and Allmentioning
confidence: 59%
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“…As mentioned earlier [6] and boldfaced in Table 5, the best accuracy is achieved using the RF classifier. e best single axis achieved 94.7% with 28 features, and the combination of all parameters achieved 96.8% with 112 features.…”
Section: Comparison Between Single Axis Vs Triple Axes and Allmentioning
confidence: 59%
“…is dataset contains twelve activities, with the following ID numbers: walking with ID number [0], running with ID number [1], going down with ID number [2], going up with ID number [3], sitting with ID number [4], sitting down with ID number [5], standing up with ID number [6], standing with ID number [7], bicycling with ID number [8], down by elevator with ID number [9], up by elevator with ID number [10], and sitting in the car with ID number [11]. e data was collected from 18 adults (14 males and 4 females).…”
Section: Dataset Descriptionmentioning
confidence: 99%
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