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 short-term 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 Schafer, Device free human activity recognition using wifi channel state information, in: 16th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2019), 5th IEEE Smart World Congress, Leicester, vol 16, IEEE, 2019).

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

Human Activity Recognition (HAR) is a rather 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 are considered. Recently, channel state information (CSI), available in WiFi networks have been proposed for fine-grained analysis. We are able to detect the human activities like Walk, Stand, Sit, Run, etc. in a Line of Sight scenario (LOS) 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 short-term memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques the latter processes the raw data directly (after denoising with wavelets). We show that it is possible to characterize activities and / or human body presence with high accuracy and we compare both approaches with regards to accuracy and performance.

Device Free Human Activity Recognition using WiFi Channel State Information

This paper presents a new approach for recognizing human activities that involve two humans, referred to as human-human interactions, using Wi-Fi signals. The proposed approach utilizes the Channel State Information (CSI) metric of the Wi-Fi signals to characterize the performed interactions in indoor environment. Specifically, the proposed approach analyzes the CSI data and extracts a set of time-domain and frequency-domain features that comprise salient information to distinguish between the performed interactions. The extracted features are used to construct a multi-class support vector machine classifier that can recognize the classes of the interactions comprised within the CSI data. The performance of the proposed approach was evaluated using our publicly available human-human interaction CSI dataset that contains the CSI data recorded for 40 pairs of participants while performing 13 interactions. The experimental results indicate that our proposed approach achieved an average recognition accuracy of 69.78% computed overall the 13 interactions. The reported results for each pair of participants demonstrate the feasibility of our proposed approach to recognize human-human interactions using the CSI metric of the Wi-Fi signals.

Yousif Al-Eryani

Papers

Indoor Human Activity Recognition Method Using CSI of Wireless Signals