Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar

Contactless monitoring to prevent self-harm and suicide in custodial settings: Protocol for a global scoping review

INTRODUCTION

Self-harm and suicide are major contributors to the global burden of disease and people in custodial settings are at a markedly increased risk of these adverse outcomes. Contactless monitoring technology is emerging as a possible solution to prevent self-harm and suicide by detecting and predicting vulnerabilities among people at increased risk in custodial settings in realtime, however no reviews to date have synthesized the evidence base, in the custodial context, regarding (a) the extent to which this technology has been implemented; and (b) the acceptability and feasibility of its application among custodial staff, specifically in relation to maintaining the wellbeing and safety of both incarcerated people and custodial professionals.

METHODS AND ANALYSIS

Our scoping review will be reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. We searched key electronic health and social science databases (MEDLINE, PubMed, Scopus, Web of Science, ProQuest and Google Scholar) on 5 February 2024 for peer-reviewed studies, which report on the use of contactless monitoring in custodial settings. Any type of study design was eligible, and the publication format was not limited. We included quantitative peer-reviewed journal articles, all types of reviews (narrative, systematic and meta-analysis) and did not apply study eligibility restrictions on country of origin. We will also search grey literature. Inclusion of publications will be restricted to the English language.

ETHICS AND DISSEMINATION

This review does not require institutional ethics review or approval as it is a review of studies that have already been granted relevant ethics approval. Our dissemination strategy includes a peer-reviewed publication and presentations at relevant national and international academic conferences. A plain language summary will be distributed through consumers and professional networks.

A high precision vital signs detection method based on millimeter wave radar

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology

The use of radar technology for non-contact measurement of vital parameters is increasingly being examined in scientific studies. Based on a systematic literature search in the PubMed, German National Library, Austrian Library Network (Union Catalog), Swiss National Library and Common Library Network databases, the accuracy of heart rate and/or respiratory rate measurements by means of radar technology was analyzed. In 37% of the included studies on the measurement of the respiratory rate and in 48% of those on the measurement of the heart rate, the maximum deviation was 5%. For a tolerated deviation of 10%, the corresponding percentages were 85% and 87%, respectively. However, the quantitative comparability of the results available in the current literature is very limited due to a variety of variables. The elimination of the problem of confounding variables and the continuation of the tendency to focus on the algorithm applied will continue to constitute a central topic of radar-based vital parameter measurement. Promising fields of application of research can be found in particular in areas that require non-contact measurements. This includes infection events, emergency medicine, disaster situations and major catastrophic incidents.

Incorporation of Digital Modulation into Vital Sign Detection and Gesture Recognition Using Multimode Radar Systems

The incorporation of digital modulation into radar systems poses various challenges in the field of radar design, but it also offers a potential solution to the shrinking availability of low-noise operating environments as the number of radar applications increases. Additionally, digital systems have reached a point where available components and technology can support higher speeds than ever before. These advancements present new avenues for radar design, in which digitally controlled phase-modulated continuous wave (PMCW) radar systems can look to support multiple collocated radar systems with low radar-radar interference. This paper proposes a reconfigurable PMCW radar for use in vital sign detection and gesture recognition while utilizing digital carrier modulation and compares the radar responses of various modulation schemes. Binary sequences are used to introduce phase modulation to the carrier wave by use of a field programable gate array (FPGA), allowing for flexibility in the modulation speed and binary sequence. Experimental results from the radar demonstrate the differences between CW and PMCW modes when measuring the respiration rate of a human subject and in gesture detection.

Design and Implementation of Human Motion Monitoring System on Account of Intelligent Computing of Internet of Things

The era of big data network represented mainly by the Internet of Things is limited by various factors such as various environments, volumes, and calculations. This paper proposes and studies a human motion detection system based on the intelligent computing of the Internet of Things, which can effectively detect the daily motion of the human body. In the era of the Internet of Things, this paper designs and develops a human motion detection system based on the Internet of Things technology and intelligent computing and explores the law of normal human motion. This paper also analyzes the design ideas and technical advantages of the human motion detection system from many aspects. This research is a human motion detection system designed and developed under the comprehensive use of Internet of Things technology and intelligent computing technology, which can effectively detect the actual situation of the human body in the state of motion. Experimental results show that the overall accuracy of the system for monitoring and recognizing human walking is 96.91%, and the overall accuracy of monitoring and recognizing human jogging is 97.18%. It monitors and recognizes human jogging with an overall accuracy rate of 97.96%. It has great practical significance.

Automatic radar-based 2-D localization exploiting vital signs signatures

In light of the continuously and rapidly growing senior and geriatric population, the research of new technologies enabling long-term remote patient monitoring plays an important role. For this purpose, we propose a single-input-multiple-output (SIMO) frequency-modulated continuous wave (FMCW) radar system and a signal processing technique to automatically detect the number and the 2-D position (azimuth and range information) of stationary people (seated/lying down). This is achieved by extracting the vital signs signatures of each single individual, separating the Doppler shifts caused by the cardiopulmonary activities from the unwanted reflected signals from static reflectors and multipaths. We then determine the number of human subjects present in the monitored environment by counting the number of extracted vital signs signatures while the 2-D localization is performed by measuring the distance from the radar where the vital signs information is sensed (i.e., locating the thoracic region). We reported maximum mean absolute errors (MAEs) of 0.1 m and 2.29\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘ and maximum root-mean-square errors (RMSEs) of 0.12 m and 3.04\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }$$\end{document}∘ in measuring respectively the ranges and azimuth angles. The experimental validation demonstrated the ability of the proposed approach in monitoring paired human subjects in a typical office environment.

Monitoring Respiratory Motion during VMAT Treatment Delivery Using Ultra-Wideband Radar

The goal of this paper is to evaluate the potential of a low-cost, ultra-wideband radar system for detecting and monitoring respiratory motion during radiation therapy treatment delivery. Radar signals from breathing motion patterns simulated using a respiratory motion phantom were captured during volumetric modulated arc therapy (VMAT) delivery. Gantry motion causes strong interference affecting the quality of the extracted respiration motion signal. We developed an artificial neural network (ANN) model for recovering the breathing motion patterns. Next, automated classification into four classes of breathing amplitudes is performed, including no breathing, breath hold, free breathing and deep inspiration. Breathing motion patterns extracted from the radar signal are in excellent agreement with the reference data recorded by the respiratory motion phantom. The classification accuracy of simulated deep inspiration breath hold breathing was 94% under the worst case interference from gantry motion and linac operation. Ultra-wideband radar systems can achieve accurate breathing rate estimation in real-time during dynamic radiation delivery. This technology serves as a viable alternative to motion detection and respiratory gating systems based on surface detection, and is well-suited to dynamic radiation treatment techniques. Novelties of this work include detection of the breathing signal using radar during strong interference from simultaneous gantry motion, and using ANN to perform adaptive signal processing to recover breathing signal from large interference signals in real time.

Subject Count Estimation by Using Doppler Radar Occupancy Sensor

Occupancy information and occupant counts can save significant amount of energy for occupant related building automation systems. Doppler radar occupancy detection sensor not only can detect the human presence but also has the potential to count the number of occupants. Continuous wave Doppler radar monitoring system is employed for occupant count estimation. The received signal strength (RSS) which is directly related to radar cross section is used as a measure for occupant counts. The preliminary results show RSS is a promising tool for occupant number estimation.

Classification of Human Posture from Radar Returns Using Ultra-Wideband Radar

There is a great need for new technology that helps ensure the well-being of senior citizens who have compromised health and are at an elevated risk of injury due to falls. Being able to detect posture and postural changes may be helpful in prediction and prevention of impending falls. Ultra-Wideband (UWB) radar is an attractive means for patient monitoring because it is inexpensive, capable of penetrating obstacles, privacy preserving and it consumes little power. In this paper, classification of postures, namely sitting, standing and lying is presented using stand-off sensing using UWB radar in an indoor environment. It is found that using location specific classifiers, overall accuracy can be improved. In this paper, a decision tree classifier capable of achieving 85% overall accuracy is proposed. This classifier uses 33 features from 10 second data sample segments.

Ambient Sensors for Elderly Care and Independent Living: A Survey

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.