Device-Free Indoor Activity Recognition System

Al-qaness, Mohammed A. A.; Li, Fangmin; Ma, Xiaolin; Zhang, Yong; Guo, Liang

doi:10.3390/app6110329

Cited by 19 publications

(16 citation statements)

References 24 publications

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“…In this research WiFi RSSI was used for (indoor) localization. Since then WiFi RSSI information has been used in localization Schäfer 2014;Aversente et al 2016) for human activity recognition (Wang et al 2015a, b;Al-Qaness et al 2016), and for gesture recognition (Pu et al 2013). RSSI is a very simple metric and does not require any special hardware changes neither at the access point end nor at the mobile end.…”

Section: Prior Workmentioning

confidence: 99%

“…RSSI is a very simple metric and does not require any special hardware changes neither at the access point end nor at the mobile end. Using RSSI for human activity recognition is very easy but RSSI suffers from multipath fading, severe distortions and instability in a complex environment (Al-Qaness et al 2016Cheng and Chang 2017). RSSI is a coarse-grained information and it does not leverage the subcarriers of an OFDM channel (Wang et al 2016b).…”

Section: Prior Workmentioning

confidence: 99%

“…Recently channel state information from the WiFi network interface cards (NIC) (Al-Qaness et al 2017) has gained a lot of attention. Unlike received signal strength indication (RSSI), CSI is measured from radio links per orthogonal frequency division multiplexing (OFDM) subcarriers for each received packet (Al-Qaness et al 2016Cheng and Chang 2017). RSSI provides coarse grained MAC layer information whereas CSI provides a fine grained, PHY layer information such as subcarriers and amplitude/phase information for each subcarrier (Al-Qaness et al 2016Cheng and Chang 2017).…”

Section: Introductionmentioning

confidence: 99%

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Device free human activity and fall recognition using WiFi channel state information (CSI)

Damodaran

Haruni

Kokhkharova

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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Human activity recognition (HAR) is a broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in order to mitigate or avoid these limitations, device free solutions based on radio signals like (home) WiFi, in particular 802.11 are considered. Recently, channel state information (CSI), available in WiFi 802.11n networks have been proposed for fine-grained analysis. We are able to detect human activities like Walk, Sit, Stand, Run (in the sequel, any human activity used for classification is capitalised, i.e. is denoted by its corresponding label. For example, "standing" is denoted as Stand, the activity "sitting" is denoted by Sit and so on), etc. in a line-of-sight (LOS) scenario and a non-line-of-sight (N-LOS) scenario within an indoor environment. We propose two algorithms-one using a support vector machine (SVM) for classification and another one using a long shortterm memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques based on wavelet analysis, the latter processes the raw data directly (after denoising). We show that it is possible to characterize activities and/or human body presence with high accuracy and we compare both approaches with regard to accuracy and performance. Furthermore, we extend the experimental setup to detect human falls, too which is a relevant use-case in the context of ambient assisted living (AAL) and show that with the developed algorithms it is possible to detect falls with high accuracy. In addition, we also show that the algorithms can be used to count the number of people in a room based on the CSI-data, which is a first step towards detecting more complex social behavior and activities. Our paper is an extended version of the paper (Damodaran and Schäfer, Device free human activity recognition using wifi channel state

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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Device free human activity and fall recognition using WiFi channel state information (CSI)

Damodaran

Haruni

Kokhkharova

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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“…Carefully integrating two or more classification algorithms may enhance the recognition performance of a classifier for automotive interfaces. In this context, both KNN and SRC classifiers have been efficiently used as stand-alone, to solve various classification problems in WiFi-based device-free localization and activity or gesture recognition [13,[16][17][18].…”

Section: Head Tilting Right Do Not Pick Call Hrmentioning

confidence: 99%

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Akhtar

Wang

2019

Applied Sciences

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In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.

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“…However, RSSI suffers from instability and severe deterioration in complex environments. Most recent, the channel state information (CSI) of wireless PHY layer has gained more attention to be utilized for indoor positioning, motion tracking, and activity recognition [7][8][9]. Unlike RSSI, CSI is measured from radio link per OFDM subcarrier of each received packet.…”

Section: Introductionmentioning

confidence: 99%

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

Al-qaness¹,

Al-Eryani²,

AL-Jallad³

2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Human activity recognition has been studied for decades by leveraging vision-based and sensor-based technologies. However, the drawback of such techniques such as short-range area, and invasion of human privacy in vision-based technology, and inappropriate usage for sensor devices or inconvenient feeling of the user to carry sensor devices. All of these reasons encouraged researchers to wireless-based sensing technology or called device-free because the user needn't carry devices nor monitoring with a camera. In this regard, we present a device-free activity recognition system by exploiting the Channel State Information (CSI) of Wi-Fi signals which recognizes three dynamic activities. We build our prototype with a high efficient feature extraction algorithm and an agile and accurate classification algorithm. To examine the feasibility and performance of the proposed system, hundreds of experiments have been implemented in LOS and NLOS scenarios in a dynamic environment with different volunteer users. The experiment results show that the proposed method has gained a high-accuracy rate in both LOS and NLOS.

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Device-Free Indoor Activity Recognition System

Cited by 19 publications

References 24 publications

Device free human activity and fall recognition using WiFi channel state information (CSI)

Device free human activity and fall recognition using WiFi channel state information (CSI)

WiFi-Based Gesture Recognition for Vehicular Infotainment System—An Integrated Approach

Indoor Human Activity Recognition Method Using CSI of Wireless Signals

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