Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

Chen, Yen-Ping; Yang, Jhun-Ying; Liou, Shun-Nan; Lee, Gwo‐Yun; Wang, Jeen-Shing

doi:10.1016/j.amc.2008.05.099

Cited by 84 publications

(47 citation statements)

References 15 publications

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“…However, most of dimension reduction techniques such as principal component analysis and linear discriminant analysis cannot be used in the SSS problem. Inspired by the LDA (12) , this paper proposed a novel technique that can solve both problems. The proposed technique can be described as follows.…”

Section: The Proposed Dimension Reductionmentioning

confidence: 99%

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

Prapochanang

Leauhatong

Nattaitanakul

2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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This paper proposes a novel dimension reduction technique for physical activity recognition from a waistmounted accelerometer. Firstly, the wavelet transform is used to extract features from the acceleration signals. Then the proposed technique is used to reduce the dimension of the wavelet features. Finally, the Multi-Layer Perceptron Neural Network (MLPNN) is used to recognize the physical activities from the reduced features. In our experiments, 5 volunteers who were healthy with the ages between 21 to 25 year old were asked to mount a tri-axial accelerometer at the right side of their waists. Next, the volunteers were asked to perform 5 daily-life physical activities: 1) walking 2) standing up from a chair 3) sitting down on a chair 4) lying down on a bed and 5) getting up from a bed; and 5 falling events: 1) forward fall 2) backward fall 3) falling to the right side 4) falling to the left side and 5) falling when standing up. In order to evaluate the performance of the proposed algorithm, the precision of the recognition and the total number of the used operators were used. The performance of the recognition with different setting of mother wavelets, vanishing moments, the rate of the dimension reduction, and the number of nodes of the hidden layer were evaluated. From the experiments, the proposed dimension reduction not only reduce the total number of the used operators but also increase the precision of the recognition.

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Section: The Proposed Dimension Reductionmentioning

confidence: 99%

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

Prapochanang

Leauhatong

Nattaitanakul

2016

The Proceedings of the 4th International Conference on Industrial Application Engineering 2016

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“…These algorithms are mostly carried out in offline analysis, which also makes the solution impractical. The constraints may also stem from sensor degradation, interconnection failures, and jitter in [91], [96], [98], [140] ACC [90] MIC [95] ACC, GPS [79] ACC, MIC, BT [118], [141] WiFi, GPS [105] ACC,MIC, GPS [76], [82], [89] ACC, MIC, WiFi, GPS Wearable Devices [83], [86], [94], [102] ACC [78] ACC, Proximity [100] ACC,HR [80], [81] ACC, BT Mobile Development Boards [84], [94], [101], [142] ACC [98] ACC,BT [77], [85], [104] ACC, Temperature, Light [80], [93] ACC, MIC, Compass, Temperature, Light -ACC: Accelerometer; BT: Bluetooth; MIC: Microphone; HR: Heart Rate Monitor the sensor placement. Hence, the reduction of sensor dimension is highly important for node interconnection, and make the system stay still unobtrusive.…”

Section: B Human Activity Recognitionmentioning

confidence: 99%

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Yurur

Liu

Sheng

et al. 2016

IEEE Commun. Surv. Tutorials

169

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“…Extensive study has been done on human activity recognition with wearable sensors [7][8][9][10][11]. Fleury et al [7] recognized seven kinds of human daily activities.…”

Section: Introductionmentioning

confidence: 99%

“…However, when the activities take place in the real-home setting or outdoors environment, the accuracy of activity recognition would be affected by variable lighting condition or the clutter disturbance [6]. Wearable-sensorbased system offers an appropriate alternative to activity recognition [7][8][9][10][11], which is inherently immune to the shadow and occlusion effects. Furthermore, compared with the vision-based systems, this kind of systems would not supply additional privacy information, thus the subjects may act more naturally as in their daily life.…”

Section: Introductionmentioning

confidence: 99%

Recognition of human activities with wearable sensors

Guo

Gao

et al. 2012

EURASIP J. Adv. Signal Process.

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A novel approach for recognizing human activities with wearable sensors is investigated in this article. The key techniques of this approach include the generalized discriminant analysis (GDA) and the relevance vector machines (RVM). The feature vectors extracted from the measured signal are processed by GDA, with its dimension remarkably reduced from 350 to 12 while fully maintaining the most discriminative information. The reduced feature vectors are then classified by the RVM technique according to an extended multiclass model, which shows good convergence characteristic. Experimental results on the Wearable Action Recognition Dataset demonstrate that our approach achieves an encouraging recognition rate of 99.2%, true positive rate of 99.18% and false positive rate of 0.07%. Although in most cases, the support vector machines model has more than 70 support vectors, the number of relevance vectors related to different activities is always not more than 4, which implies a great simplicity in the classifier structure. Our approach is expected to have potential in real-time applications or solving problems with large-scale datasets, due to its perfect recognition performance, strong ability in feature reduction, and simple classifier structure.

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Online classifier construction algorithm for human activity detection using a tri-axial accelerometer

Cited by 84 publications

References 15 publications

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

A Novel Dimension Reduction Technique for Physical Activity Recognition from a Waist-Mounted Accelerometer

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Recognition of human activities with wearable sensors

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