The Dream-maker Man

https://digitalcommons.library.umaine.edu/mmb-me/1053/thumbnail.jp

Mighty Lak\u27a Rose

https://digitalcommons.library.umaine.edu/mmb-me/1149/thumbnail.jp

The Rosary

https://digitalcommons.library.umaine.edu/mmb-me/1473/thumbnail.jp

Physical Activity Sensing via Stand-Alone WiFi Device

WiFi signals for physical activity sensing shows promising potential for many healthcare applications due to its potential for recognising various everyday activities, non-invasive nature and low intrusion on privacy. Traditionally, WiFi-based sensing uses the Channel State Information (CSI) from an offthe- shelf WiFi Access Point (AP) which transmits signals that have high pulse repetition frequencies. However, when there are no users on the communication network only beacon signals are transmitted from the WiFi AP which significantly deteriorates the sensitivity and specificity of such systems. Surprisingly WiFi based sensing under these conditions have received little attention given that WiFi APs are frequently in idle state. This paper presents a practical system based on passive radar technique which does not require any special setup or preset firmware and able to work with any commercial WiFi device. To cope with the low density of beacon signal, a modified Cross Ambiguity Function (CAF) has been proposed to reduce redundant samples in the recorded. In addition, an external device has been developed to send WiFi probe request signals and stimulate an idle AP to transmit WiFi probe responses thus generate usable transmission signals for sensing applications without the need to authenticate and join the network. Experimental results prove that proposed concept can significantly improve activity detection and is an ideal candidate for future healthcare and security applications

Passive WiFi Radar for Human Sensing Using A Stand-Alone Access Point

Human sensing using WiFi signal transmissions is attracting significant attention for future applications in ehealthcare, security and the Internet of Things (IoT). The majority of WiFi sensing systems are based around processing of Channel State Information (CSI) data which originates from commodity WiFi Access Points (AP) that have been primed to transmit high data-rate signals with high repetition frequencies. However, in reality, WiFi APs do not transmit in such a continuous uninterrupted fashion, especially when there are no users on the communication network. To this end, we have developed a passive WiFi radar system for human sensing which exploits WiFi signals irrespective of whether the WiFi AP is transmitting continuous high data-rate OFDM signals, or periodic WiFi beacon signals whilst in an idle status (no users on the WiFi network). In a data transmission phase, we employ the standard cross ambiguity function (CAF) processing to extract Doppler information relating to the target, whilst a modified version is used for lower data-rate signals. In addition, we investigate the utility of an external device that has been developed to stimulate idle WiFi APs to transmit usable signals without requiring any type of user authentication on the WiFi network. In the paper we present experimental data which verifies our proposed methods for using any type of signal transmission from a stand-alone WiFi device, and demonstrate the capability for human activity sensing

SimHumalator: An Open Source End-to-End Radar Simulator For Human Activity Recognition

Radio-frequency based non-cooperative monitor ing of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and bio-medical applications for non-intrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever- increasing processing speeds of computers could drive forward the data- driven deep-learning focused research in the above applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi standard(IEEE 802.11g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks

Using RF Transmissions from IoT Devices for Occupancy Detection and Activity Recognition

IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing)

On CSI and Passive WiFi Radar for Opportunistic Physical Activity Recognition

The use of Wi-Fi signals for human sensing has gained significant interest over the past decade. Such techniques provide affordable and reliable solutions for healthcare-focused events such as vital sign detection, prevention of falls and long-term monitoring of chronic diseases, among others. Currently, there are two major approaches for Wi-Fi sensing: (1) passive Wi-Fi radar (PWR) which uses well established techniques from bistatic radar, and channel state information (CSI) based wireless sensing (SENS) which exploits human-induced variations in the communication channel between a pair of transmitter and receiver. However, there has not been a comprehensive study to understand and compare the differences in terms of effectiveness and limitations in real-world deployment. In this paper, we present the fundamentals of the two systems with associated methodologies and signal processing. A thorough measurement campaign was carried out to evaluate the human activity detection performance of both systems. Experimental results show that SENS system provides better detection performance in a line-of-sight (LoS) condition, whereas PWR system performs better in a non-LoS (NLoS) setting. Furthermore, based on our findings, we recommend that future Wi-Fi sensing applications should leverage the advantages from both PWR and SENS systems