Infrared Motion Sensors Research Articles

BoomBox: An Automated Behavioural Response (ABR) camera trap module for wildlife playback experiments

Abstract Camera traps (CTs) are a valuable tool in ecological research, amassing large quantities of information on the behaviour of diverse wildlife communities. CTs are predominantly used as passive data loggers to gather observational data for correlational analyses. Integrating CTs into experimental studies, however, can enable rigorous testing of key hypotheses in animal behaviour and conservation biology that are otherwise difficult or impossible to evaluate. We developed the 'BoomBox', an open‐source Arduino‐compatible board that attaches to commercially available CTs to form an Automated Behavioural Response (ABR) system. The modular unit connects directly to the CT’s passive infrared (PIR) motion sensor, playing audio files over external speakers when the sensor is triggered. This creates a remote playback system that captures animal responses to specific cues, combining the benefits of camera trapping (e.g. continuous monitoring in remote locations, lack of human observers, large data volume) with the power of experimental manipulations (e.g. controlled perturbations for strong mechanistic inference). Our system builds on previous ABR designs to provide a cheap (~100USD) and customizable field tool. We provide a practical guide detailing how to build and operate the BoomBox ABR system with suggestions for potential experimental designs that address a variety of questions in wildlife ecology. As proof‐of‐concept, we successfully field tested the BoomBox in two distinct field settings to study species interactions (predator–prey and predator–predator) and wildlife responses to conservation interventions. This new tool allows researchers to conduct a unique suite of manipulative experiments on free‐living species in complex environments, enhancing the ability to identify mechanistic drivers of species' behaviours and interactions in natural systems.

IoT-Based Unobtrusive Physical Activity Monitoring System for Predicting Dementia

Mental health-related disorders are common in elderly populations. Among the various mental health disorders, one most significant threat is dementia, and prediction of dementia has become an important issue related to well-being in old age, because the disease progression of dementia can be slowed by early diagnosis and disease control. In this paper, we propose an unobtrusive dementia-prediction system for monitoring physical activities of elderly persons either living alone or as a couple in different house structures, achieved through passive infrared (PIR) motion sensors combined with data processing. The proposed feature extraction algorithm extracts feature values related to physical activities from simple passive infrared sensors located in each room space. We then apply a variety of common popular classification models, including Deep Neural Networks (DNNs), to predict the risk of dementia in a sensor-enabled home. We implemented and validated algorithms on data collected for over a month from 18 participants who were engaged with a variety of living conditions. The proposed system was effective in predicting dementia risk, with up to an 0.99 area under the curve (AUC) using DNN with principal component analysis (PCA) and a quantile transformer scaler. In terms of the result based on leave-one-subject-out (LOSO) analysis, an accuracy of 63.38% was achieved using DNN with PCA and a standard scaler. The proposed methodology is non-invasive and cost-effective, and can be used for a variety of long-term monitoring and early symptom detection systems, helping caregivers provide optimal interventions to elderly individuals at risk for dementia.

Emerging Biotechnology Markers of Cognitive Impairment

The early detection of cognitive impairment is among the National Institute on Aging’s (NIA) current research priorities. Sensor-based technologies have exploded in recent years allowing remote, continuous measurement of older adults’ free-living activity. This highly granular data has stimulated exciting new research exploring how change in health can be detected remotely using novel “biotechnology” markers. Yet, this area of research is in its infancy as it relates to predicting cognitive function. This symposium will provide an overview of the sensor-cognition research landscape and will feature 5 new studies exploring the relationship between biotechnology markers and cognitive function, each with unique sensors, cognitive measures and samples. The first three presentations will report associations between accelerometry-based activity measures (chest or wrist devices) and cognitive function (assessed by diagnosis, a neurocognitive assessment, or microstructural changes on DTI) in the Baltimore Longitudinal Study on Aging, a large, NIA-funded epidemiologic dataset. The fourth presentation will report the significance of free-living hip accelerometry activity measures beyond clinically-available information in a random forest prediction model of 1-year change in Montreal Cognitive Assessment scores among urban, predominantly African-American older adults without moderate-severe dementia residing in the community. The final presentation will report associations between room-to-room transitions as detected by in-home, infrared motion sensors and mild cognitive impairment using data from a community-dwelling sample of older adults residing alone. This symposium will provide a substantial expansion of current knowledge in this research space and will be relevant to clinicians or researchers with an interest in sensor technology or dementia.

Passive Infrared Motion Sensors Improved the Detection Accuracy of Nocturnal Agitation

Actigraphy has been used to detect agitation in persons with dementia, although this technology must be worn by participants. Another promising sensing methodology is passive infrared (PIR) motion, which provides continuous, low-cost, and unobtrusive data, and may also improve the detection of agitated periods. Using data from the MODERATE (Monitoring Dementia-Related Agitation Using Technology Evaluation) study, we compared the predictive value of detecting agitation in a male participant, who was 64 years old with Alzheimer’s disease (AD), living in a memory care unit, and monitored with actigraphy on his wrist and four PIR motion sensors within his living quarters. The participant’s medical record indicated that he experienced agitation during 17 nights over 96 consecutive days. 929,037 data points were captured for analysis. From each night, the features extracted from the actigraphy wearable included total and standard deviation of activity counts, activity counts in the most and the least active hours, and median activity counts in one hour. Features extracted from the PIR motion sensors included dwell durations in the areas around bed, sofa, front door and bathroom, and the number of transitions between these areas. Using logistic regression to predict agitated periods, comparable classification performances were achieved using these two sets of features (AUC = 0.74 for wearable and AUC = 0.71 for PIR motion sensors). When these two sets of features were combined, the classification performance showed notable improvement (AUC = 0.83). This study points to the value of utilizing PIR motion sensors for detecting dementia-related agitation.

Stability and Variability of Indoor Room Transitions and Mild Cognitive Impairment in Older Adults

Indoor room transition is an underexplored real-world activity outcome. We estimated the stability and variability of indoor room transitions and their associations with mild cognitive impairment (MCI) in older adults. Older adults living-alone (n=159, age=78.3±8.8 years, 14% MCI) from the Oregon Center for Aging & Technology (ORCATECH) and the Minority Aging Research Study (MARS) were included. Room transitions were detected using passive infrared motion sensors in bathroom, bedroom, kitchen, and living room. The hourly number of room transitions was used to calculate the interdaily stability and intradaily variability of room transitions. MCI was operationalized by the Clinical Dementia Rating equaled 0.5. Generalized estimating equations models adjusted for demographics, health, and environmental factors revealed that older adults with MCI had a lower interdaily stability of room transitions than cognitive healthy peers (z=-2.06,p=0.03). A pervasive-sensing system deployed in homes can obtrusively measure room transition activities to inform cognitive health in older adults.

Evaluating changes in gait and activity associated with cognitive impairment using a home‐based technology platform

AbstractBackgroundChanges in mobility are associated with cognitive decline in older adults. Mobility is frequently assessed in the clinic setting at episodic intervals. Passive sensors within a home‐based technology platform allow for unobtrusive collection of mobility and gait information over an extended time period. This method of high‐frequency data collection could be sensitive to early changes in mobility associated with cognitive decline.MethodWe present data for a cohort of 32 participants living alone and enrolled in the Collaborative Aging Research Using Technology (CART) Initiative aging study with a pervasive sensing and computing system deployed in their homes. Sixteen individuals with a Clinical Dementia Rating (CDR) scale score of 0.5 were matched by age, sex and education to 16 participants with a CDR of 0; 80% lived in low‐income senior apartments; 6% were non‐white. Mobility and gait data is derived from a gait line, consisting of four ceiling mounted, field‐of‐view restricted passive infrared motion sensors placed 61 cm apart in a line, in each home. The number of walking events, defined as all four sensors being triggered, was analyzed between the two groups.ResultParticipants with a CDR score of 0 had a mean age of 72.1 years and a mean MoCA score of 26.1 (range 21 – 30). Participants with a CDR score of 0.5 had a mean age of 72.2 and a mean MoCA score of 22.1 (range 15 – 27). Fifty‐six percent of participants in each group were female. Preliminary data from the gait line from a combined 1400 days for CDR 0 participants and a combined 1250 days for CDR 0.5 participants is presented here. The mean number of daily walking events detected by the gait line was significantly greater for CDR 0 participants (101.4) compared to CDR 0.5 participants (92.8, p=0.03).ConclusionAmbient sensors are able to collect longitudinal data on gait and activity levels in individuals with a technology platform deployed within their home. This unobtrusive remote mobility assessment methodology identifies individuals experiencing cognitive impairment. Further evaluation of other gait metrics and home‐based activity patterns is ongoing to explore their association with changes in cognition.

Life activity patterns and mild cognitive impairment in older adults

AbstractBackgroundShifting and diminished circadian and activity rhythms characterize older adults with cognitive impairment, but studies rarely consider both temporal and spatial aspects of life activity patterns. We examined hourly in‐home and out‐of‐home activity patterns using latent group trajectory models to characterize older adults with and without mild cognitive impairment (MCI).MethodOne hundred and seven older adults (19 with MCI) living alone (age=81.1±7.8, 76.6% women) from the Oregon Center for Aging & Technology (ORCATECH) were included. MCI was determined by the Clinical Dementia Rating (CDR) score = 0.5. In‐home and out‐of‐home activities were collected via passive infrared motion sensors in each room and contact sensors on the doors to the home for one month. Daily life activity patterns were estimated based on: 1) the hourly average amount of time the participant spent in each major life space (bathroom; bedroom; kitchen; living room) and out‐of‐home, and 2) the hourly average number of trips the participant made to each room and out‐of‐home.ResultLatent trajectory models identified two distinct patterns for three features: time spent out‐of‐home (high/low outdoor), time spent in the kitchen (high/low kitchen occupancy), and trips to the bathroom (more/fewer bathroom trips) from 5 am to 1 pm. Older adults with MCI were more likely to follow a pattern of low outdoor time (β=‐2.65; p=0.03), high kitchen occupancy (β=1.68; p=0.01), and more bathroom trips (β=1.60; p=0.02) than those with intact cognition. There were no differences in the bedroom patterns or time spent in the bathroom in older adults with and without MCI from 5 am to 1 pm. Potential covariates were controlled in the models (age, gender, years of education, sleep duration, gait speed, and the number of rooms in the house).ConclusionDifferent social and mealtime activity patterns from early morning to early afternoon may signal cognitive changes and contraction of life‐space. These may be caused by environmental, as well as chronobiological changes accompanying MCI. Unobtrusive measurement of in‐home and out‐of‐home activity patterns may be useful for integrating with chronotherapies in developing strategies to mitigate potential daily mal‐entrainment for older adults at risk for MCI and dementia.

Development of an Intelligent Smart Home Automation System

Generally speaking, security has been a major concern in every nook and cranny of our nation -Nigeria. Recently, cases of vandalisation, stolen vehicles, and related issues have been on the increase. The security personnel has done lots of work to curb this menace but most of their actions have not yielded the expected results. Therefore, there is need to use technology to create a safer society. Such technology will use GSM and camera for detections. The technology is not going to replace the security personnel and usage of appliances such as gates, and doors locks, electric wires, etc, but it will be used as an alternative method for prevention/detection. Based on this background, a digital light-dependent resistor (LDR) sensor coupled with a microcontroller, relay, camera, and GSM technology was used to develop a smart home security system for automatic detection. The LDR sensor converts the light intensity to a digital format for the microcontroller to control the security lights automatically by using the relay as a switch. The passive infrared motion sensor perceives human movement and converts it to digital format for the microcontroller to trigger the GSM module and alert the user for live streaming of what is happening on the mobile app by establishing a connection between the camera and android smart device. The capturing and videoing are done on the mobile android device.

Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment.

The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI). One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory. Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03). The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

Room Security System Design using ESP32 CAM with Fuzzy Algorithm

At this time, security becomes an important thing that must be fulfilled. Especially a room that has valuables. Currently, the average room security only uses conventional methods, which are very easy to break. Therefore, An Intelligent security system is needed to maintain security in a room. An Intelligent security system is a system that can detect everything automatically and can be monitored remotely using the internet or better known as IoT (Internet of Things). This system uses a microcontroller as the primary part of running the system and is supported by several modules such as the PIR infrared motion sensor module, door window magnetic sensor module, camera module, and buzzer alarm module. The door & window magnetic sensor module serves as the first security, which, when the door/window is open, will give a warning via the buzzer alarm module. While the PIR infrared motion sensor module functions as second security which if motion is detected in a room, it will trigger the camera module to take pictures in the room. The results of this system will be sent directly to Android via the Telegram application using a bot. This Telegram bot will send data in text and images from the system to Android directly.

Automatic Mosquito Control Device Using Infrared Motion Sensor

Malaria is a disease transmitted by female Anopheles mosquitoes to humans. Mosquitoes are a carrier of malaria. There are many types of vector control strategies, including physical, chemical, and biological methods. Individuals, local communities, and more organized public health vector management programs often need to work together to effectively identify and control mosquito farms. Efforts should be made to destroy mosquito eggs and reduce the density of larvae and adults. The World Health Organization recommends integrated vector management (IVM) as a strategy to improve vector control. IVM combines good practices that maximize an effective, safe, environmentally friendly, and cost-effective approach to disease vector control. Effective and well-tested vector control strategies include: Biological control: larvae and biolarvicidal fish, such as mosquitoes in ditches and ponds or other predators to control mosquito larvae. Chemical control: larvae and adults (indoor spray residues, insecticide-treated bed nets, etc.). Adult mosquitoes are small vulnerable insects with slender bodies, a pair of slender wings and three pairs of thin long legs. Mosquito control can be improved by using an automatic mosquito control device with an infrared motion sensor. Suppression of malaria vectors is an important means of preventing and reducing malaria transmission. To reduce malaria, mosquitoes in and around our environment must be controlled. The introduction of an infrared motion sensor in an automatic mosquito control device would eliminate the reduction in the number of mosquitoes in our environment. Add an infrared motion sensor to the automatic mosquito control device for easier mosquito control in our homes and environments. To customize automatic mosquito control by adding an infrared motion sensor. DOI: 10.7176/ISDE/12-3-01 Publication date: September 30 th 2021

EVALUATION OF ECONOMIC EFFICIENCY OF MODERNIZATION OF ARTIFICIAL LIGHTING OF EDUCATIONAL INSTITUTIO

The analysis of technical characteristics and cost of modern light sources and motion sensors used in administrative premises is carried out. It has been established that lighting of corridor-type rooms using LED panels of the lower and middle price ranges with a luminous efficiency of 80-85 and 100 lm/W, accordingly, becomes less expensive in the second year of operation compared to fluorescent lighting. Installation of panels with a higher luminous efficiency, up to 120 lm/W, at the current level of prices on them has certain risks, as the payback period of the investment in this case is about 7 years, exceeding the warranty period of these panels. The payback period of investment in the automation of lighting control in corridor-type rooms using relatively inexpensive infrared motion sensors and the expected reduction of energy consumption by 30% does not exceed 5 years, demonstrating the feasibility of this event. Measures for replacing fluorescent lamps with LED panels in less loaded classrooms, up to 510 hours of operation per year, have a payback time of 4-6 years, which exceeds the warranty period of the panels. Therefore, according to the authors of the article, it is appropriate to first replace the lamps in the corridors, and then, after accumulating experience in operating the purchased light sources, to replace them in the classrooms. Due to their high cost in the premises of educational institutions, the use of presence sensors as well as automated lighting level control systems is not economically feasible. The urgency of saving energy by replacing fluorescent lamps with LED and automation of lighting control, due to the trend towards an increase in the cost of electricity, will also grow. The algorithms and formulas given in the article for determining the present value, payback periods are simple and can be applied to other types of light sources and design conditions.

Economic Efficiency Of Implementation Of Lighting control In Public Buildings

The article is devoted to the description of experimental studies of the possibility of increasing the energy efficiency of the lighting installation (LI) through the use of motion sensors. Data on possible energy savings are needed to conduct economic calculations – to estimate the payback period of capital costs for the implementation of a lighting control system (LCS). A quantitative and qualitative assessment of the economic feasibility of creating or developing an LCS is based on calculating and analyzing the costs required for its implementation and comparing the costs with the results obtained during its implementation. The results of the functioning of the LCS can be expressed by such a value as the percentage of possible energy savings, expressed in terms of the ratio of electricity consumption after the introduction of the LCS to the consumption of electricity before its implementation. In this work, this coefficient was determined experimentally. The experiment was carried out in public building – in the laboratory and educational building of the university. A corridor of one of the floors was chosen as the object of the study. For the research, a data collection system based on a digital microcontroller was used; infrared motion sensors were usedas presence sensors. The results of the experiment showed that the presence of people in the corridor varies during the day, by days of the week, from month to month. Studies have shown that, on average, energy savings when using motion sensors in the on / off mode of lamps is at least 50 %. With an increase in the delay time for turning off the luminaires after the motion sensor is triggered, the energy saving decreases from 66.5 % to 48.5 %. When using the standby lighting mode during the absence of people, the savings are reduced from 66.5 % (with the lamps turned off due to lack of movement) to 21.9 %.

Vehicle Anti-Theft Detection and Protection with Air Quality IoT Notification

The crime related to vehicle theft has been a tremendous rise with burglar’s becoming smarter every day. This generates an crucial need for an effective vehicle theft diagnosis system. In this paper, a compact, cheap and efficient system is studied, designed and explored using Raspberry Pi as the core processing unit of the whole system. Sensors data of Passive Infra-Red (PIR) motion sensor, pressure sensor, gas sensor, including Global Positioning System (GPS), Pi camera, buzzer and a Liquid Crystal Display (LCD) display are collected by embedded Linux system. The device functions in two modes: User mode and Theft mode. The gadget has ability of detecting intrusion and on detection will ring an alarm and send coordinates to the user’s assigned E-mail. The device will send the latitude and longitudinal details to the user’s e-mail along with the captured image using remote sensing whenever there is an intrusion or if the device is displaced after a certain threshold as in the case of bike theft wherein the complete vehicle can be effortlessly displaced without starting the engine or deactivating physical locks. It is easy to locate the vehicle and get accurate position on Google Maps using this design. This paper explores the possibility of a compact, versatile, viable, cheap and efficient vehicle theft detection system.

Modeling elderly behavioral patterns in single-person households

This paper proposes and describes an unsupervised computational model that monitors an elderly person who lives alone and issues alarms when a risk to the elderly person’s well-being is identified. This model is based on data extracted exclusively from passive infrared motion sensors connected to a ZigBee wireless network. The proposed monitoring system and model is non-intrusive, does not capture any images, and does not require any interaction with the monitored person. Thus, it is more likely to be adopted by members of the elderly population who might reject other more intrusive or complex types of technology. The developed computational model for activity discovery employs a kernel estimator and local outlier factor calculation, which are reliable and have a low computational cost. This model was tested with data collected over a period of 25 days from two elderly volunteers who live alone and have fairly different routines. The results demonstrate the model’s ability to learn relevant behaviors, as well as identify and issue alarms for atypical activities that can be suggestive of health problems. This low-cost, minimalistic sensor network approach is especially suited to the reality of underdeveloped (and developing) countries where assisted living communities are not available and low cost and ease of use are paramount.

An open-source non-contact thermometer using low-cost electronic components

Due to the spread of COVID-19 across the world and the increased need for non-contact thermometers to prevent the spread of disease, a new electronic thermometer has been designed and implemented for measuring human body temperature from a distance. This device is currently in use at building entrances to measure the body temperatures of employees, students, and customers. This system is designed using low-cost easy-to-assemble open-source electronic components. The system consists of seven main parts: an Arduino UNO microcontroller, an infrared (IR) thermometer for non-contact temperature measurements (GY-906 MLX90614ESF module), an IR motion sensor (TCRT 5000) for the purpose of contactless initiation of the system, a graphic LCD to display results, a DS3231 clock module for a real-time clock and calendar, and a micro-SD storage board to store device audio instructions.

DESIGN OF AN AUTOMATIC STERILIZATION GATE TOOL USING PIR MOTION SENSOR

This automatic sterilization gate tool is a simple tool that changes the manual method to a systemized and more efficient way. This tool uses an infrared motion sensor in which there is a main component, namely the PIR (Pyroelectric Infra-Red) sensor, which is a material that reacts to radiation and movement in front of it. This tool also uses a 12v dc pump which is used to draw out the disinfectant liquid stored in the container. This gate tool is designed using the main material and some additional materials, the automatic gate can discharge about 11 seconds of liquid from objects or people that pass through the sensor range, 11 seconds is the minimum time for this tool to work while a distance of 6 meters is the range from where the sensor is located. From that time it proved that this gate tool has 100% accuracy and runs well. This gate tool is used every day, turned on from the morning, and turned off at night. Every 17.00 hours this gate tool is always replenished with supplies of disinfectant liquid. The automatic gate has helped the community to reduce the risk of transmission of the coronavirus, it also raises the awareness of the covid-19 pandemic. The whole system is implemented and is tested for real-time operation. It is found working satisfactorily. The gate tool can be further improved by adding a scanning device to perform tracing for passersby.

Wearable Based Calibration of Contactless In-home Motion Sensors for Physical Activity Monitoring in Community-Dwelling Older Adults.

Passive infrared motion sensors are commonly used in telemonitoring applications to monitor older community-dwelling adults at risk. One possible use case is quantification of in-home physical activity, a key factor and potential digital biomarker for healthy and independent aging. A major disadvantage of passive infrared sensors is their lack of performance and comparability in physical activity quantification. In this work, we calibrate passive infrared motion sensors for in-home physical activity quantification with simultaneously acquired data from wearable accelerometers and use the data to find a suitable correlation between in-home and out-of-home physical activity. We use data from 20 community-dwelling older adults that were simultaneously provided with wireless passive infrared motion sensors in their homes, and a wearable accelerometer for at least 60 days. We applied multiple calibration algorithms and evaluated results based on several statistical and clinical metrics. We found that using even relatively small amounts of wearable based ground-truth data over 7–14 days, passive infrared based wireless sensor systems can be calibrated to give largely better estimates of older adults' daily physical activity. This increase in performance translates directly to stronger correlations of measured physical activity levels with a variety of age relevant health indicators and outcomes known to be associated with physical activity.

Laser Alarm Security Systems

The project we have worked on is a wireless security system that can be controlled through a wireless remote or a touch tone phone from any place. The main component of this system are the infrared motion sensors and basic alarm unit. It works on detecting heat of any human body when there is any technical fault, in turn the alarm unit is triggered. The security makes the person monitoring security alert and may be the concerned local law enforcement body also, by sirening a high pitched sound. Here there are two types of laser beam that work: green beam, infrared beam.The infrared beam is not visible to naked to eyes whereas the green beam the visible. This project aims to improve security systems using real time monitoring and reporting by 4G/LTE or Bluetooth based technology.

Contact-free sensor signals as a new digital biomarker for cardiovascular disease: chances and challenges.

Multiple sensor systems are used to monitor physiological parameters, activities of daily living and behaviour. Digital biomarkers can be extracted and used as indicators for health and disease. Signal acquisition is either by object sensors, wearable sensors, or contact-free sensors including cameras, pressure sensors, non-contact capacitively coupled electrocardiogram (cECG), radar, and passive infrared motion sensors. This review summarizes contemporary knowledge of the use of contact-free sensors for patients with cardiovascular disease and healthy subjects following the PRISMA declaration. Chances and challenges are discussed. Thirty-six publications were rated to be of medium (31) or high (5) relevance. Results are best for monitoring of heart rate and heart rate variability using cardiac vibration, facial camera, or cECG; for respiration using cardiac vibration, cECG, or camera; and for sleep using ballistocardiography. Early results from radar sensors to monitor vital signs are promising. Contact-free sensors are little invasive, well accepted and suitable for long-term monitoring in particular in patient's homes. A major problem are motion artefacts. Results from long-term use in larger patient cohorts are still lacking, but the technology is about to emerge the market and we can expect to see more clinical results in the near future.