Radio-based device-free activity recognition with radio frequency interference

Abstract

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

Device-Free Indoor Activity Recognition System

“…Furthermore, to approve the validity of our feature extraction method, we compare our feature-based classification methodology (SRC with feature extraction) with the featureless-SRC methodology. Wei et al [25] proposed a device-free human activity system by leveraging the fluctuation of CSI, and they adopted SRC to classify several activities. Unlike our system, Wei et al used SRC without the feature extraction method.…”

“…Sparse representation classification (SRC) has been adopted in various signal and image processing tasks such as image classification and face recognition [24]. Moreover, recently, SRC is used in device-free activity recognition [25]. Wei et al [25] relied on the superiority of SRC which is featureless, since SRC training can be built from CSI measurements without extracting features.…”

“…Moreover, recently, SRC is used in device-free activity recognition [25]. Wei et al [25] relied on the superiority of SRC which is featureless, since SRC training can be built from CSI measurements without extracting features. However, this may lead to false detection specifically in the case of radio interferences or environmental changes.…”

Activity and Emotion Recognition for Elderly Health Monitoring

“…They can be divided into three categories related on the type of data they are based on: i) Motion-sensor-based methods [7,[10][11][12][13], attached or wearable, they utilize on body sensors like the accelerometer and gyroscope, to sense the movements of body parts. ii) Radio-based methods [14,15], the wireless radio types include: ZigBee; which build a small network of sensors on the body. WiFi; passive activity recognition system, RFID which is practically based on the gesture recognition system that employs signal fluctuations in a limited area.…”

Sensor-based smart recognition system For wearable devices

“…Misra et al [100] used CS to compress GPS signals and exploits the information of various propagation paths to improve the SNR of GPS signals. In a recent work [136], the researchers improved activity classification accuracy by fusing several channel state information (CSI) vectors. In my study, I use CS to reduce the feature dimension of face images as will be described in Section 3.2.1.…”