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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 MIMO subplots of sitting down onto a chair. (a) The original CSIs; (b) The filtered CSIs by average weight; (c) The filtered CSIs by Single-sideband Gaussian (SG) weight. It is obvious SG weight outperforms the average weight in time region 0–5 ms and 1500–2000 ms.
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sensors-15-17195-f001: The MIMO subplots of sitting down onto a chair. (a) The original CSIs; (b) The filtered CSIs by average weight; (c) The filtered CSIs by Single-sideband Gaussian (SG) weight. It is obvious SG weight outperforms the average weight in time region 0–5 ms and 1500–2000 ms.

Mentions: An Ntx × Nrx × 30 matrix is taken as the data structure of the CSI, where the third dimension is across 30 subcarriers in the Orthogonal Frequency Division Multiplexing (OFDM) channel. In an Intel 5300 NIC, there is only one transmitting terminal and three receiving ends, so it is a 1 × 3 MIMO system. We aggregate 30 subcarriers’ CSI values into one single value by their average for each MIMO plot, e.g., Figure 1a.


Robust Indoor Human Activity Recognition Using Wireless Signals.

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

The MIMO subplots of sitting down onto a chair. (a) The original CSIs; (b) The filtered CSIs by average weight; (c) The filtered CSIs by Single-sideband Gaussian (SG) weight. It is obvious SG weight outperforms the average weight in time region 0–5 ms and 1500–2000 ms.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-17195-f001: The MIMO subplots of sitting down onto a chair. (a) The original CSIs; (b) The filtered CSIs by average weight; (c) The filtered CSIs by Single-sideband Gaussian (SG) weight. It is obvious SG weight outperforms the average weight in time region 0–5 ms and 1500–2000 ms.
Mentions: An Ntx × Nrx × 30 matrix is taken as the data structure of the CSI, where the third dimension is across 30 subcarriers in the Orthogonal Frequency Division Multiplexing (OFDM) channel. In an Intel 5300 NIC, there is only one transmitting terminal and three receiving ends, so it is a 1 × 3 MIMO system. We aggregate 30 subcarriers’ CSI values into one single value by their average for each MIMO plot, e.g., Figure 1a.

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