Human activity recognition plays a significant role in smart building applications, healthcare services, and security monitoring. In particular, WiFi-based indoor wireless sensing system becomes increasingly popular due to its noninvasiveness. This work presents the design and implementation of DARMS, a Device-free human Activity Recognition and Monitoring System that can be deployed with low-cost commodity WiFi devices. DARMS is a passive wireless sensing system, and it can accurately distinguish various daily activities without the user wearing any sensor. DARMS makes two key technical contributions. First, an effective signal processing methodology is designed to extract the CSI features both in the time domain and frequency domain. Second, a dual-channel neural network that combines temporal and frequency information is proposed to achieve fine-grained activity recognition. In our experiments, DARMS shows outstanding performance in different indoor environments, with an average accuracy of 96.9% for fall detection and 93.3% for human activity recognition.

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Device-Free Human Activity Recognition Based on Dual-Channel Transformer Using WiFi Signals

Practical and accurate localization systems are important to mobile targets that enable promising services such as navigation and augmented reality. With the proliferation of WiFi, existing WiFi-based localization systems have leveraged RSSI, fingerprints, landmarks, time of arrival, or angle of arrival to locate targets, while no related work pays attention to mobile targets themselves. For wheel-driven mobile targets, such as vehicles, bikes, and wheeled robots, we design and implement WheelLoc, a novel WiFi-based localization system leveraging the rotation of wheels. The specially designed WheelLoc hardware is cost-effective and self-powered with the composition of three commercial antennas and a solar cell, which is also easy to be installed on wheels. A hybrid WheelLoc algorithm is further proposed to realize accurate localization in diverse environments, whether the wheel of targets is static or mobile, indoor or outdoor, on flat or bumpy ground. The movements of individual antennas are exploited to emulate linear, cycloid, and circular antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Extensive experiments are conducted on bikes in the real world. Performance results demonstrate that WheelLoc does not require any user interaction, yet achieves comparable accuracy with the state-of-the-art localization systems using WiFi.

WheelLoc: Practical and Accurate Localization for Wheeled Mobile Targets via Integrated Sensing and Communication

Recent years have seen significant advances in WiFi Localization. However, existing systems require either multiple access points to cooperate with each other or a single access point to have multiple antennas and transceiver chains. Therefore, they cannot be integrated into most IoT WiFi chipsets which have only a single transceiver chain. This paper presents WiSight, a novel approach to bringing WiFi localization to any WiFi devices, especially those with a single RF chain. We propose a WiFi antenna design and use the inherent properties of the 802.11 protocol to measure Angle-of-Arrival (AoA) and Time-of-Flight (ToF) using a single transceiver chain. Our proof-of-concept simulation and real world experiments promise the feasibility of this approach.

Bringing wifi localization to any wifi devices

Continuous monitoring of health status has the potential to enhance the quality of life and life expectancy of people suffering from chronic illness and of the elderly. However, such systems can only come into widespread use if the cost of manufacturing is low. Advancements in material science and engineering technology have led to a significant decrease in the expense of developing healthcare monitoring devices. This review aims to investigate the progress of the use of low-cost sensors in healthcare monitoring and discusses the challenges faced when accomplishing continuous and real-time monitoring tasks. The major findings include (1) only a small number of publications (N = 50) have addressed the issue of healthcare monitoring applications using low-cost sensors over the past two decades; (2) the top three algorithms used to process sensor data include SA (Statistical Analysis, 30%), SVM (Support Vector Machine, 18%), and KNN (K-Nearest Neighbour, 12%); and (3) wireless communication techniques (Zigbee, Bluetooth, Wi-Fi, and RF) serve as the major data transmission tools (77%) followed by cable connection (13%) and SD card data storage (10%). Due to the small fraction (N = 50) of low-cost sensor-based studies among thousands of published articles about healthcare monitoring, this review not only summarises the progress of related research but calls for researchers to devote more effort to the consideration of cost reduction as well as the size of these components.

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Healthcare Monitoring Using Low-Cost Sensors to Supplement and Replace Human Sensation: Does It Have Potential to Increase Independent Living and Prevent Disease?

The past decade has demonstrated the potential of human activity recognition (HAR) with WiFi signals owing to non-invasiveness and ubiquity. Previous research has largely concentrated on enhancing precision through sophisticated models. However, the complexity of recognition tasks has been largely neglected. Thus, the performance of the HAR system is markedly diminished when tasked with increasing complexities, such as a larger classification number, the confusion of similar actions, and signal distortion To address this issue, we eliminated conventional convolutional and recurrent backbones and proposed WiTransformer, a novel tactic based on pure Transformers. Nevertheless, Transformer-like models are typically suited to large-scale datasets as pretraining models, according to the experience of the Vision Transformer. Therefore, we adopted the Body-coordinate Velocity Profile, a cross-domain WiFi signal feature derived from the channel state information, to reduce the threshold of the Transformers. Based on this, we propose two modified transformer architectures, united spatiotemporal Transformer (UST) and separated spatiotemporal Transformer (SST) to realize WiFi-based human gesture recognition models with task robustness. SST intuitively extracts spatial and temporal data features using two encoders, respectively. By contrast, UST can extract the same three-dimensional features with only a one-dimensional encoder, owing to its well-designed structure. We evaluated SST and UST on four designed task datasets (TDSs) with varying task complexities. The experimental results demonstrate that UST has achieved recognition accuracy of 86.16% on the most complex task dataset TDSs-22, outperforming the other popular backbones. Simultaneously, the accuracy decreases by at most 3.18% when the task complexity increases from TDSs-6 to TDSs-22, which is 0.14–0.2 times that of others. However, as predicted and analyzed, SST fails because of excessive lack of inductive bias and the limited scale of the training data.

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WiTransformer: A Novel Robust Gesture Recognition Sensing Model with WiFi

This work presents the design and implementation of TyrLoc, an accurate multi-technology switching MIMO localization system that can be deployed on low-cost SDRs. TyrLoc only uses a single RF Chain to switch on each antenna in an antenna array within the coherence time asynchronously, thus mimicking a MIMO platform to pinpoint the positions of WIFI, Bluetooth Low Energy (BLE) and LoRa devices. TyrLoc makes three key technical contributions. First, TyrLoc modifies the firmware of inexpensive PlutoSDR that controls the antenna switching pattern and tags the signal associated with each antenna. Second, it develops a two-stage fine-grained carrier frequency offset (CFO) calibration algorithm that harnesses the agile antenna switching pattern and is 10× more accurate than the baseline method. Third, TyrLoc employs an interpolated transform approach to facilitate angle-of-arrival (AoA) estimation in the presence of missing antennas. The AoA-based localization experiments in a multipath-rich indoor environment show that TyrLoc with eight antennas achieves the median errors of 63cm for WIFI, 39cm for BLE and 32cm for LoRa, respectively.

Taiwei He

Papers

TyrLoc: a low-cost multi-technology MIMO localization system with a single RF chain

Device-Free Human Activity Recognition Based on Dual-Channel Transformer Using WiFi Signals