Activity recognition from accelerometer data
Citations
2,417 citations
Cites background or methods from "Activity recognition from accelerom..."
...Additional studies have similarly focused on how one can use a variety of accelerometerbased devices to identify a range of user activities [4-7, 9-16, 21]....
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...[15] collected data from two users wearing a single accelerometer-based device and then transmitted this data to the HP iPAQ mobile device carried by the user....
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1,501 citations
Cites background from "Activity recognition from accelerom..."
...Some approaches have adapted dedicated motion sensors in different body parts such as the waist, wrist, chest and thighs achieving good classification performance [4]....
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1,214 citations
1,078 citations
Cites background from "Activity recognition from accelerom..."
...These features are popular due to their simplicity as well as their high performance across a variety of activity recognition problems [Bao and Intille 2004; Ravi et al. 2005]....
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...These features are popular due to their simplicity as well as their high performance across a variety of activity recognition problems [Bao and Intille 2004; Ravi et al. 2005]....
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944 citations
Cites background or result from "Activity recognition from accelerom..."
...Some authors have attempted to compare discriminative and generative models [48], [153], generally...
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...also found that SVMs performed consistently well, but also investigated meta-level classifiers that combined the results of multiple base-level classifiers [153]....
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References
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16,118 citations
"Activity recognition from accelerom..." refers background or methods in this paper
...• Bagging (Breiman 1996) is another simple meta-level classifier that uses just one base-level classifier at a time....
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...In addition to analyzing the performance of base-level classifiers (Bao & Intille 2004), we have studied the effectiveness of meta-level classifiers (such as boosting (Freund & Schapire 1996), bagging (Breiman 1996), plurality voting, stacking using ODTs, and stacking using MDTs (Todorovski & Dzeroski 2003)) in improving activity recognition accuracy....
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...…classifiers (Bao & Intille 2004), we have studied the effectiveness of meta-level classifiers (such as boosting (Freund & Schapire 1996), bagging (Breiman 1996), plurality voting, stacking using ODTs, and stacking using MDTs (Todorovski & Dzeroski 2003)) in improving activity recognition…...
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...Bussmann et al. 2001). The most successful and exhaustive work in this regard is that of Bao & Intille (2004). In their experiments, subjects wore 5 biaxial accelerometers on different body parts as they performed a variety of activities like walking, sitting, standing still, watching TV, running, bicycling, eating, reading etc....
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9,995 citations
7,601 citations
"Activity recognition from accelerom..." refers background or methods in this paper
...…the performance of base-level classifiers (Bao & Intille 2004), we have studied the effectiveness of meta-level classifiers (such as boosting (Freund & Schapire 1996), bagging (Breiman 1996), plurality voting, stacking using ODTs, and stacking using MDTs (Todorovski & Dzeroski 2003)) in…...
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...Decision tree classifiers showed the best performance, recognizing activities with an overall accuracy of 84%....
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5,936 citations