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Robust Indoor Human Activity Recognition Using Wireless Signals.

Wang Y, Jiang X, Cao R, Wang X - Sensors (Basel) (2015)

Bottom Line: Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information.Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method.Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

View Article: PubMed Central - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian 116620, China. dlutwangyi@dlut.edu.cn.

ABSTRACT
Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

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Related in: MedlinePlus

The average recognition error rate for actions in 100 (s).
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sensors-15-17195-f007: The average recognition error rate for actions in 100 (s).

Mentions: In the last experiment, we let five people of different figures and ages perform actions including AC1–AC13 in 100 (s) time steps in any part of the room. Each primitive action was conducted at a normal speed (average 0.5 s–0.87 s) and separated by walking, standing, sitting, or another static state. The average error rates of recognition by SVM with and without feature selection are demonstrated in Figure 7. As the results show, the FS and SVM method has a lower error rate and can satisfy the requirement of real-time action recognition.


Robust Indoor Human Activity Recognition Using Wireless Signals.

Wang Y, Jiang X, Cao R, Wang X - Sensors (Basel) (2015)

The average recognition error rate for actions in 100 (s).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541929&req=5

sensors-15-17195-f007: The average recognition error rate for actions in 100 (s).
Mentions: In the last experiment, we let five people of different figures and ages perform actions including AC1–AC13 in 100 (s) time steps in any part of the room. Each primitive action was conducted at a normal speed (average 0.5 s–0.87 s) and separated by walking, standing, sitting, or another static state. The average error rates of recognition by SVM with and without feature selection are demonstrated in Figure 7. As the results show, the FS and SVM method has a lower error rate and can satisfy the requirement of real-time action recognition.

Bottom Line: Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information.Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method.Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

View Article: PubMed Central - PubMed

Affiliation: School of Software, Dalian University of Technology, Dalian 116620, China. dlutwangyi@dlut.edu.cn.

ABSTRACT
Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Show MeSH
Related in: MedlinePlus