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

Neena Damodaran,Jörg Schäfer,Muyassar Kokhkharova,Elis Haruni

doi:10.1007/s42486-020-00027-1

Abstract

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 Schäfer, 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).

Highlights

While human activity recognition (HAR) is an interesting fundamental research problem on its own, it is becoming increasingly important in areas such as health care of Muyassar Kokhkharova kokhkharovam@gmail.com 1 Faculty of Computer Science and Engineering, Frankfurt University of Applied Sciences, Nibelungenplatz 1, 60318
WiFall e.g. is a fall detection system focusing on a one-class classification (Fall) using an anomaly detection based approach to retrieve the activity’s pattern segment, whereas in our work we address multiclass problems
We show that long short-term memory (LSTM) together with this light preprocessing is almost on a par with the more sophisticated former algorithm considering classification performance

Summary

Introduction

While human activity recognition (HAR) is an interesting fundamental research problem on its own, it is becoming increasingly important in areas such as health care of1 3 Vol.:(0123456789)elderly and sick or otherwise impaired people. While human activity recognition (HAR) is an interesting fundamental research problem on its own, it is becoming increasingly important in areas such as health care of. There is a tremendous increase in the elderly population and while some elderly suffer from the loss of cognitive or physical autonomy, they choose to live independently at their residence instead of living under the care of a hospital. Monitoring of human activity and fall detection systems might mitigate some of the risks. For the relevance of fall as a specific activity and the importance of fall detection, we refer to Sect. 6

Objectives

Methods

Results

Conclusion