Higher order feature extraction and selection for robust human gesture recognition using CSI of COTS Wi-Fi devices

Device-free human gesture recognition (HGR) using commercial o the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections ofWi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five di erent users was 96.23% in the laboratory environment.Taylor's University through its TAYLOR'S PhD SCHOLARSHIP Programmeinfo:eu-repo/semantics/publishedVersio

A systematic review of non-contact sensing for developing a platform to contain COVID-19

The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation

Non-contact smart sensing of physical activities during quarantine period using SDR technology

The global pandemic of the coronavirus disease (COVID-19) is dramatically changing the lives of humans and results in limitation of activities, especially physical activities, which lead to various health issues such as cardiovascular, diabetes, and gout. Physical activities are often viewed as a double-edged sword. On the one hand, it offers enormous health benefits; on the other hand, it can cause irreparable damage to health. Falls during physical activities are a significant cause of fatal and non-fatal injuries. Therefore, continuous monitoring of physical activities is crucial during the quarantine period to detect falls. Even though wearable sensors can detect and recognize human physical activities, in a pandemic crisis, it is not a realistic approach. Smart sensing with the support of smartphones and other wireless devices in a non-contact manner is a promising solution for continuously monitoring physical activities and assisting patients suffering from serious health issues. In this research, a non-contact smart sensing through the walls (TTW) platform is developed to monitor human physical activities during the quarantine period using software-defined radio (SDR) technology. The developed platform is intelligent, flexible, portable, and has multi-functional capabilities. The received orthogonal frequency division multiplexing (OFDM) signals with fine-grained 64-subcarriers wireless channel state information (WCSI) are exploited for classifying different activities by applying machine learning algorithms. The fall activity is classified separately from standing, walking, running, and bending with an accuracy of 99.7% by using a fine tree algorithm. This preliminary smart sensing opens new research directions to detect COVID-19 symptoms and monitor non-communicable and communicable diseases

Design of software defined radio based testbed for smart healthcare

Human Activity Recognition (HAR) help to sense the environment of a human being with an objective to serve a diverse range of human-centric applications in health care, smart-homes and the military. The prevailing detection techniques use ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concern as well. Monitoring human activities of daily living is a possible way of describing the functional and health status of a human. Therefore, human activity recognition (HAR) is one of genuine components in personalized life-care and healthcare systems, especially for the elderly and disabled. Recent advances in wireless technologies have demonstrated that a person’s activity can modulate the wireless signal, and enable the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. The aim of this PhD project is to design a novel, non-invasive, easily deployable, flexible and scalable test-bed for detecting human daily activities that can help to assess the general physical health of a person based on Software Defined Radios (SDRs). The proposed system also allows us to modify the power level of transceiver model, change the operating frequency, use self-design antennas and change the number of subcarriers in real-time. The results obtained using USRP based wireless sensing for activities of daily living are highly accurate as compared to off-the-shelf wireless devices each time when activities and experiments are performed. This system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis, and Naıve Bayes are used to evaluate the overall performance of the test-bed and achieved a high accuracy. The K-nearest neighbour outperformed all classifiers, providing an accuracy of 89.73% for activity detection and 91.01% for breathing monitoring. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being

Robust Audio and WiFi Sensing via Domain Adaptation and Knowledge Sharing From External Domains

Recent advancements in machine learning have initiated a revolution in embedded sensing and inference systems. Acoustic and WiFi-based sensing and inference systems have enabled a wide variety of applications ranging from home activity detection to health vitals monitoring. While many existing solutions paved the way for acoustic event recognition and WiFi-based activity detection, the diverse characteristics in sensors, systems, and environments used for data capture cause a shift in the distribution of data and thus results in sub-optimal classification performance when the sensor and environment discrepancy occurs between training and inference stage. Moreover, large-scale acoustic and WiFi data collection is non-trivial and cumbersome. Therefore, current acoustic and WiFi-based sensing systems suffer when there is a lack of labeled samples as they only rely on the provided training data. In this thesis, we aim to address the performance loss of machine learning-based classifiers for acoustic and WiFi-based sensing systems due to sensor and environment heterogeneity and lack of labeled examples. We show that discovering latent domains (sensor type, environment, etc.) and removing domain bias from machine learning classifiers make acoustic and WiFi-based sensing robust and generalized. We also propose a few-shot domain adaptation method that requires only one labeled sample for a new domain that relieves the users and developers from the painstaking task of data collection at each new domain. Furthermore, to address the lack of labeled examples, we propose to exploit the information or learned knowledge from sources where available data already exists in volumes, such as textual descriptions and visual domain. We implemented our algorithms in mobile and embedded platforms and collected data from participants to evaluate our proposed algorithms and frameworks in an extensive manner.Doctor of Philosoph

Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring

A bathroom has higher probability of accidents than other rooms due to a slippery floor and temperature change. Because of high privacy and humidity, we face difficulties in monitoring inside a bathroom using traditional healthcare methods based on cameras and wearable sensors. In this paper, we present a danger-pose detection system using commodity Wi-Fi devices, which can be applied to bathroom monitoring, preserving privacy. A machine learning-based detection method usually requires data collected in target situations, which is difficult in detection-of-danger situations. We therefore employ a machine learning-based anomaly-detection method that requires a small amount of data in anomaly conditions, minimizing the required training data collected in dangerous conditions. We first derive the amplitude and phase shift from Wi-Fi channel state information (CSI) to extract low-frequency components that are related to human activities. We then separately extract static and dynamic features from the CSI changes in time. Finally, the static and dynamic features are fed into a one-class support vector machine (SVM), which is used as an anomaly-detection method, to classify whether a user is not in bathtub, bathing safely, or in dangerous conditions. We conducted experimental evaluations and demonstrated that our danger-pose detection system achieved a high detection performance in a non-line-of-sight (NLOS) scenario

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

How\u27s My Network - Incentives and Impediments of Home Network Measurements

Gathering meaningful information from Home Networking (HN) environments has presented researchers with measurement strategy challenges. A measurement platform is typically designed around the process of gathering data from a range of devices or usage statistics in a network that are specifically behind the HN firewall. HN studies require a fine balance between incentives and impediments to promote usage and minimize efforts for user participation with the focus on gathering robust datasets and results. In this dissertation we explore how to gather data from the HN Ecosystem (e.g. devices, apps, permissions, configurations) and feedback from HN users across a multitude of HN infrastructures, leveraging low impediment and low/high incentive methods to entice user participation. We look to understand the trade-offs of using a variety of approach types (e.g. Java Applet, Mobile app, survey) for data collections, user preferences, and how HN users react and make changes to the HN environment when presented with privacy/security concerns, norms of comparisons (e.g. comparisons to the local environment and to other HNs) and other HN results. We view that the HN Ecosystem is more than just “the network” as it also includes devices and apps within the HN. We have broken this dissertation down into the following three pillars of work to understand incentives and impediments of user participation and data collections. These pillars include: 1) preliminary work, as part of the How\u27s My Network (HMN) measurement platform, a deployed signed Java applet that provided a user-centered network measurement platform to minimize user impediments for data collection, 2) a HN user survey on preference, comfort, and usability of HNs to understand incentives, and 3) the creation and deployment of a multi-faceted How\u27s My Network Mobile app tool to gather and compare attributes and feedback with high incentives for user participation; as part of this flow we also include related approaches and background work. The HMN Java applet work demonstrated the viability of using a Web browser to obtain network performance data from HNs via a user-centric network measurement platform that minimizes impediments for user participation. The HMN HN survey work found that users prefer to leverage a Mobile app for HN data collections, and can be incentivized to participate in a HN study by providing attributes and characteristics of the HN Ecosystem. The HMN Mobile app was found to provide high incentives, with minimal impediments, for participation with focus on user Privacy and Security concerns. The HMN Mobile app work found that 84\% of users reported a change in perception of privacy and security, 32\% of users uninstalled apps, and 24\% revoked permissions in their HN. As a by-product of this work we found it was possible to gather sensitive information such as previously attached networks, installed apps and devices on the network. This information exposure to any installed app with minimal or no granted permissions is a potential privacy concern