Non-wearable Devices Research Articles

Feasibility of Monitoring Heart and Respiratory Rates Using Nonwearable Devices and Consistency of the Measured Parameters: Pilot Feasibility Study.

As Japan is the world's fastest-aging society with a declining population, it is challenging to secure human resources for care providers. Therefore, the Japanese government is promoting digital transformation and the use of nursing care equipment, including nonwearable devices that monitor heart and respiratory rates. However, the feasibility of monitoring heart and respiratory rates with nonwearable devices and the consistency of the rates measured have not been reported. In this study, we focused on a sheet-type nonwearable device (Safety Sheep Sensor) introduced in many nursing homes. We evaluated the feasibility of monitoring heart rate (HR) and respiratory rate (RR) continuously using nonwearable devices and the consistency of the HR and RR measured. A sheet-type nonwearable device that measured HR and RR every minute through body vibrations was placed under the mattress of each participant. The participants in study 1 were healthy individuals aged 20-60 years (n=21), while those in study 2 were older adults living in multidwelling houses and required nursing care (n=20). The HR was measured using standard methods by the nurse and using the wearable device (Silmee Bar-type Lite sensor), and RR was measured by the nurse. The primary outcome was the mean difference in HR and RR between nonwearable devices and standard methods. The mean difference in HR was -0.32 (SD 3.12) in study 1 and 0.04 (SD: 3.98) in study 2; both the differences were within the predefined accepted discrepancies (<5 beats/min). The mean difference in RR was -0.98 (SD 3.01) in study 1 and -0.49 (SD 2.40) in study 2; both the differences were within the predefined accepted discrepancies (3 breaths/min). HR and RR measurements obtained using the nonwearable devices and the standard method were similar. Continuous monitoring of vital signs using nonwearable devices can aid in the early detection of abnormal conditions in older people.

SmartLog: A Smart TV-Based Lifelogging System for Capturing, Storing, and Visualizing Watching Behavior

The modern smart TV can record viewers’ daily life-watching experiences called digital logs. These logs can be used for various purposes, including but not limited to recommendations, monitoring viewers, watching behavior, memory augmentation, e-learning, viewers’ privacy, and designing and developing adaptive User Interfaces (UI). Much work is available on lifelogging technologies related to smartphones, fitness gadgets, and wearable and non-wearable devices. However, little attention has been given to smart TV-based lifelogging systems. In this paper, we proposed a TV-based SmartLog framework for logging specific information about watching experiences. The proposed work is implemented and installed on Android-based Smart TV. We logged various activities of individual viewers and other household members and their environmental contexts. We empirically evaluated the proposed solution by analyzing data from 75 household viewers of different age groups. These groups include teenagers, homemakers, senior citizens, literate, and educated. We carried out various tests on the collected data, ie, descriptive tabulation, Kendall’s tau-b, Cronbach alpha, and Principal Component Factor Analysis (PCFA). The findings suggest that the SmartLog app received a positive perception regarding attitude, user satisfaction, ease of use, and intention to use. The proposed solution can be used to assist household viewers in terms of personalization, usability, accessibility, user experience, learnability, and reducing cognitive overload while interacting with smart TV.

Measuring Sleep Quality in the Hospital Environment with Wearable and Non-Wearable Devices in Adults with Stroke Undergoing Inpatient Rehabilitation

Sleep disturbances are common after stroke and may affect recovery and rehabilitation outcomes. Sleep monitoring in the hospital environment is not routine practice yet may offer insight into how the hospital environment influences post-stroke sleep quality while also enabling us to investigate the relationships between sleep quality and neuroplasticity, physical activity, fatigue levels, and recovery of functional independence while undergoing rehabilitation. Commonly used sleep monitoring devices can be expensive, which limits their use in clinical settings. Therefore, there is a need for low-cost methods to monitor sleep quality in hospital settings. This study compared a commonly used actigraphy sleep monitoring device with a low-cost commercial device. Eighteen adults with stroke wore the Philips Actiwatch to monitor sleep latency, sleep time, number of awakenings, time spent awake, and sleep efficiency. A sub-sample (n = 6) slept with the Withings Sleep Analyzer in situ, recording the same sleep parameters. Intraclass correlation coefficients and Bland-Altman plots indicated poor agreement between the devices. Usability issues and inconsistencies were reported between the objectively measured sleep parameters recorded by the Withings device compared with the Philips Actiwatch. While these findings suggest that low-cost devices are not suitable for use in a hospital environment, further investigations in larger cohorts of adults with stroke are needed to examine the utility and accuracy of off-the-shelf low-cost devices to monitor sleep quality in the hospital environment.

Simulation of IoT-oriented Fall Detection Systems Architectures for In-home Patients

Fall detection (FD) systems enable rapid detection and intervention for people who experience falls, a leading threat to the elderlys health and autonomy. Most of these systems conform to an IoT reference architecture which may include multiple sensing mechanisms to balance the advantages and drawbacks of each alternative. However, developing such a heterogeneous system may be costly and quite resource and time-demanding. This paper presents a Discrete Event System Specification (DEVS) simulation model for FD systems that compares the accuracy of nine different systems architectures that combine traditional wearable and non-wearable sensing devices in the acquisition layer. We perform simulations for each architectural arrangement using four public datasets of FD systems, totaling 36 simulations. Results reveal that an FD accuracy of 96.67% is possible with an investment of almost 6,000 US. Besides, spending 36 times less (around 150 US), designers and clients could acquire an FD system composed of wearable and non-wearable devices with an accuracy of 91%, i.e., only 5% less than the most expensive alternative.

Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review

Cardiovascular diseases (CVD) are the leading cause of mortality globally. Despite improvement in therapies, people with CVD lack support for monitoring and managing their condition at home and out of hospital settings. Smart Home Technologies have potential to monitor health status and support people with CVD in their homes. We explored the Smart Home Technologies available for CVD monitoring and management in people with CVD and acceptance of the available technologies to end-users. We systematically searched four databases, namely Medline, Web of Science, Embase, and IEEE, from 1990 to 2020 (search date 18 March 2020). “Smart-Home” was defined as a system using integrated sensor technologies. We included studies using sensors, such as wearable and non-wearable devices, to capture vital signs relevant to CVD at home settings and to transfer the data using communication systems, including the gateway. We categorised the articles for parameters monitored, communication systems and data sharing, end-user applications, regulations, and user acceptance. The initial search yielded 2462 articles, and the elimination of duplicates resulted in 1760 articles. Of the 36 articles eligible for full-text screening, we selected five Smart Home Technology studies for CVD management with sensor devices connected to a gateway and having a web-based user interface. We observed that the participants of all the studies were people with heart failure. A total of three main categories—Smart Home Technology for CVD management, user acceptance, and the role of regulatory agencies—were developed and discussed. There is an imperative need to monitor CVD patients’ vital parameters regularly. However, limited Smart Home Technology is available to address CVD patients’ needs and monitor health risks. Our review suggests the need to develop and test Smart Home Technology for people with CVD. Our findings provide insights and guidelines into critical issues, including Smart Home Technology for CVD management, user acceptance, and regulatory agency’s role to be followed when designing, developing, and deploying Smart Home Technology for CVD.

Automated Systems for Estrous and Calving Detection in Dairy Cattle

Purpose: The objective of this review is to describe the main technologies (automated activity monitors) available commercially and under research for the detection of estrus and calving alerts in dairy cattle. Sources: The data for the elaboration of the literature review were obtained from searches on the Google Scholar platform. This search was performed using the following keywords: reproduction, dairy cows, estrus detection and parturition, electronic devices. After the search, the articles found with a title related to the objective of the review were read in full. Finally, the specific articles chosen to be reported in the review were selected according to the method of identification of estrus and parturition, seeking to represent the different devices and technologies already studied for both estrus and parturition identification. Synthesis: Precision livestock farming seeks to obtain a variety of information through hardware and software that can be used to improve herd management and optimize animal yield. Visual observation for estrus detection and calving is an activity that requires labor and time, which is an increasingly difficult resource due to several others farm management activities. In this way, automated estrous and calving monitoring devices can increase animal productivity with less labor, when applied correctly. The main devices available currently are based on accelerometers, pedometers and inclinometers that are attached to animals in a wearable way. Some research efforts have been made in image analysis to obtain this information with non-wearable devices. Conclusion and applications: Efficient wearable devices to monitor cows’ behavior and detect estrous and calving are available on the market. There is demand for low cost with easy scalable technology, as the use of computer vision systems with image recording. With technology is possible to have a better reproductive management, and thus increase efficiency.

Contactless remote monitoring of sleep: evaluating the feasibility of an under-mattress sensor mat in a real-life deployment

ABSTRACT Sleep is so important, particularly for the elderly. The lack of sleep may increase the risk of cognitive decline. Similarly, it may also increase the risk of Alzheimer’s disease. Nonetheless, many people underestimate the importance of getting enough rest and sleep. In-laboratory polysomnography is the gold-standard method for assessing the quality of sleep. This method is considered impractical in the clinical environment, seen as labour-intensive and expensive owing to its specialised equipment, leading to long waiting lists. Hence, user-friendly (remote and non-intrusive) devices are being developed to help patients monitor their sleep at home. In this paper, we first discuss commercially-available non-wearable devices that measure sleep, in which we highlight the features associated with each device, including sensor type, interface, outputs, dimensions, power supply, and connectivity. Second, we evaluate the feasibility of a non-wearable device in a free-living environment. The deployed device comprises a sensor mat with an integrated micro-bending multimode fibre. Raw sensor data were gathered from five senior participants living in a senior activity centre over a few to several weeks. We were able to analyse the participants’ sleep quality using various sleep parameters deduced from the sensor mat. These parameters include the wake-up time, bedtime, the time in bed, nap time. Vital signs, namely heart rate, respiratory rate, and body movements, were also reported to detect abnormal sleep patterns. We have employed pre-and post-surveys reporting each volunteer’s sleep hygiene to confirm the proposed system’s outcomes for detecting the various sleep parameters. The results of the system were strongly correlated with the surveys for reporting each sleep parameter. Furthermore, the system proved to be highly effective in detecting irregular patterns that occurred during sleep.

Review of applications and user perceptions of smart home technology for health and environmental monitoring

Abstract In recent decades, smart home technology has advanced, improving the well-being and quality of life of its users. Thus, its applications have expanded, particularly in health and environmental monitoring. Numerous devices have been developed to accommodate user requirements of monitoring; however, the adoption of monitoring devices is closely related to user perception. User perception can be considered from different perspectives. One method of understanding different user perceptions is comparing wearable and nonwearable devices, owing to the differences in their obtrusiveness. The aim of this study was to systematically review the applications and user perceptions of health and environmental monitoring devices, emphasizing on the wearable and nonwearable distinction. We conducted a focused search of articles related to smart home technology and its user perceptions based on its applications. The inclusion criteria were original and peer-reviewed articles centered on health and environmental monitoring devices. We identified and analysed 159 of the 4476 relevant articles and divided the articles into two categories. The first category comprised health and environmental monitoring and their applications by the type of device. The second category comprised user perceptions of monitoring devices. The devices were grouped into wearable and nonwearable devices for our analysis. We identified user perceptions based on usefulness, ease of use, and privacy. Because wearable and nonwearable devices complement their limitations, we recommend their integration for improving user perception.

Reprint of: Vibration sensing-based human and infrastructure safety/health monitoring: A survey

Current sensor technologies enable the passive and continuous monitoring of human behaviors as well as infrastructures to ensure personal safety and assess individual health state. One passive technology that has the potential of gathering personal data is the vibration sensor. In this paper, we carry out an extensive survey of the current vibration-based sensing technologies for human and infrastructure safety as well as health monitoring. These technologies utilize structural and bodies vibration as a source of data, and they can be incorporated in wearable or non-wearable devices. Furthermore, the vibration sensing technology utilizes low-cost and low-power sensors, which make it attractive for indoor and outdoor monitoring. We have classified the technologies into five categories: vibration-based sensing for assessing human health, recognizing personal behavior, inferring occupancy information, evaluating personal safety, and monitoring infrastructure health. In each category, we also classify the approaches that utilize single and multiple sensors. Moreover, we discuss the different types of signal processing and machine learning techniques that are applied to each approach.

Using a Non-Wearable Actigraphy in Nursing Care for Dementia With Lewy Bodies.

People who have dementia with Lewy bodies often have sleep disorders. We used non-wearable devices to record and categorize the sleep patterns of patients with Lewy body dementia. Individual sleep data at a dementia-care unit in Japan were recorded using non-wearables. One week's worth of data from 18 patients was analyzed. Median metrics for all participants were the following: sleep efficiency, 68% (23-89); sleep duration at night, 6.8hours (1.6-11.1); times getting out of bed at night, 3.5 (0-13). We identified three types of abnormal sleep: extremely short sleep duration, excessive sleep duration at night, and excessive number of times getting out of bed at night. Sleep disturbances in Lewy body dementia patients are treated using various practices; staff must choose the most effective plan for each patient's situation. Monitoring patient sleep using non-wearable provides more objective data that can help staff better personalize nursing care.

Unobtrusive Monitoring of Sleep Cycles: A Technical Review

Polysomnography is the gold-standard method for measuring sleep but is inconvenient and limited to a laboratory or a hospital setting. As a result, the vast majority of patients do not receive a proper diagnosis. In an attempt to solve this issue, sleep experts are continually looking for unobtrusive and affordable alternatives that can provide longitudinal sleep tracking. Collecting longitudinal data on sleep can accelerate epidemiological studies exploring the effect of sleep on health and disease. These alternatives can be in the form of wearables (e.g., actigraphs) or nonwearable (e.g., under-mattress sleep trackers). To this end, this paper aims to review the several attempts made by researchers toward unobtrusive sleep monitoring, specifically sleep cycle. We have performed a literature search between 2016 and 2021 and the following databases were used for retrieving related articles to unobtrusive sleep cycle monitoring: IEEE, Google Scholar, Journal of Clinical Sleep Medicine (JCSM), and PubMed Central (PMC). Following our survey, although existing devices showed promising results, most of the studies are restricted to a small sample of healthy individuals. Therefore, a broader scope of participants should be taken into consideration during future proposals and assessments of sleep cycle tracking systems. This is because factors such as gender, age, profession, and social class can largely affect sleep quality. Furthermore, a combination of sensors, e.g., smartwatches and under-mattress sleep trackers, are necessary to achieve reliable results. That is, wearables and nonwearable devices are complementary to each other, and so both are needed to boost the field of at-home sleep monitoring.

P062 Sleep parameter validation of a home-based ballistocardiograph sleep tracker

Abstract Introduction Consumer home sleep trackers are gaining popularity for objective sleep monitoring. Amongst them, non-wearable devices have little disruption in daily routine and need little maintenance. However, the validity of their sleep outcomes needs further investigation. In this study, the accuracy of the sleep outcomes of EMFIT Quantified Sleep (QS), an unobtrusive and non-wearable ballistocardiograph sleep tracker, was evaluated by comparing it with polysomnography (PSG). Methods 62 sleep lab patients underwent a single clinical PSG and their sleep measures were simultaneously collected through PSG and EMFIT QS. Total Sleep Time (TST), Wake After Sleep Onset (WASO), Sleep Onset Latency (SOL) and average Heart Rate (HR) were compared using paired t-tests and agreement analysed using Bland-Altman plots. Results EMFIT QS data loss occurred in 47% of participants. In the remaining 33 participants (15 females, with mean age of 53.7±16.5), EMFIT QS overestimated TST by 177.5±119.4 minutes (p&amp;lt;0.001) and underestimated WASO by 44.74±68.81 minutes (p&amp;lt;0.001). It accurately measured average resting HR and was able to distinguish SOL with some accuracy. However, the agreement between EMFIT QS and PSG on sleep-wake detection was very low (kappa=0.13, p&amp;lt;0.001). Discussion A consensus between PSG and EMFIT QS was found in SOL and average HR. There was a significant discrepancy and lack of consensus between the two devices in other sleep outcomes. These findings indicate that while EMFIT QS is not a credible alternative to PSG for sleep monitoring in clinical and research settings, consumers may find some benefit from longitudinal monitoring of SOL and HR.

Validation of a nonwearable device in healthy adults with normal and short sleep durations.

To determine the accuracy of early and newer versions of a nonwearable sleep tracking device relative to polysomnography and actigraphy, under conditions of normal and restricted sleep duration. Participants were 35 healthy adults (mean age = 18.97; standard deviation = 0.95 years; 77.14% female; 42.86% White). In a controlled sleep laboratory environment, we randomly assigned participants to go to bed at 10:30 pm (normal sleep) or 1:30 am (restricted sleep), setting lights-on at 7:00 am. Sleep was measured using polysomnography, wristband actigraphy (the Philips Respironics Actiwatch Spectrum Plus), self-report, and an early or newer version of a nonwearable device that uses a sensor strip to measure movement, heart rate, and breathing (the Apple, Inc. Beddit). We tested accuracy against polysomnography for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. The early version of the nonwearable device (Beddit 3.0) displayed poor reliability (intraclass correlation coefficient [ICC] < 0.30). However, the newer nonwearable device (Beddit 3.5) yielded excellent reliability with polysomnography for total sleep time (ICC = 0.998) and sleep efficiency (ICC = 0.98) across normal and restricted sleep conditions. Agreement was also excellent for the notoriously difficult metrics of sleep onset latency (ICC = 0.92) and wake after sleep onset (ICC = 0.92). This nonwearable device significantly outperformed clinical-grade actigraphy (ICC between 0.44 and 0.96) and self-reported sleep measures (ICC < 0.75). A nonwearable device showed better agreement than actigraphy with polysomnography outcome measures. Future work is needed to test the validity of this device in clinical populations. Hsiou DA, Gao C, Matlock RC, Scullin MK. Validation of a nonwearable device in healthy adults with normal and short sleep durations. J Clin Sleep Med. 2022;18(3):751-757.

A comprehensive survey on non-invasive wearable bladder volume monitoring systems

Measuring the volume of urine in the bladder is a significant issue in patients who suffer from the lack of bladder fullness sensation or have problems with timeliness getting to the restroom, such as spinal cord injury patients and some of the elderlies. Real-time monitoring of the bladder, therefore, can be highly helpful for urinary incontinence. Bladder volume monitoring technologies can be divided into two distinct categories of invasive and non-invasive. In invasive techniques, a catheter is directly inserted into the urethra to measure the amount of urine accurately. However, it is painful, limits the user's ordinary movements, and may hurt the urinary tract. Current non-invasive techniques measure the volume of the bladder from the skin using different stationary or portable apparatus at health centers. Both techniques have difficulties and are not cost-effective to use for a long period. Recently, both invasive and non-invasive methods have been attempted to be produced in the form of wearable devices utilizing different sensing and communication technologies. Wearable bladder monitoring devices can be easily used by patients with no or few clinical steps, making them much more affordable than non-wearable devices. While wearable devices seem to be a highly convenient and effective solution, they suffer from few drawbacks, such as relatively low precision. Hence, a great number of studies have been conducted to address these issues. In this article, we review and discuss non-invasive and minimally invasive methods for monitoring the bladder volume. We focus on the most practical and state-of-the-art methods employed in wearable devices, classify them by engineering and medical characteristics, and investigate their specifications, architectures, and measurement algorithms. This study aims to introduce the latest advances in this field to practitioners while comparing the advantages and disadvantages of existing approaches. Our study concludes with open problems and future trends in the area of bladder monitoring and measurement systems. Graphical abstract Wearable bladder monitoring system.

254 Validation of a Non-Wearable Sleep Tracking Device in Healthy Adults Under Normal and Restricted Sleep Conditions

Abstract Introduction Polysomnography (PSG) is the gold standard for measuring sleep, but this method is cumbersome, costly, and sometimes does not reflect naturalistic sleep patterns. Leading technology companies have developed non-wearable sleep tracking devices that have attracted public interest. However, the accuracy of these devices has either been shown to be poor or the validation tests have not been conducted by independent laboratories without potential conflicts of interest. Relative to PSG and actigraphy, and under conditions of both normal and restricted sleep, we assessed the accuracy of early and newer versions of a non-wearable sleep tracking device (Beddit, Apple Inc.). Methods Participants were 35 healthy young adults (Mage=18.97, SD=0.95 years; 77.14% female; 42.86% Caucasian). We randomly assigned them to go to bed at 10:30pm (normal sleep) or 1:30am (restricted sleep) in a controlled sleep laboratory environment. Lights-on was 7:00am for all participants. Sleep was measured by the early version (3.0) or newer version (3.5) of a non-wearable device that uses a sensor strip to measure movement, heart rate, and breathing. We also measured PSG, wristband actigraphy, and self-report. For each device, we tested accuracy against PSG for total sleep time (TST), sleep efficiency (SE%), sleep onset latency (SOL), and wake after sleep onset (WASO). Results While the early version displayed poor reliability (ICCs&amp;lt;0.30), the newer version of the non-wearable device yielded excellent reliability with PSG under both normal and restricted sleep conditions. Not only was agreement excellent for TST (ICC=0.96) and SE% (ICC=0.98), but agreement was also excellent for the notoriously difficult metrics of SOL (ICC=0.92) and WASO (ICC=0.92). This newer version significantly outperformed clinical grade actigraphy (ICCs often in the 0.40 to 0.75 range), and self-reported sleep (ICCs often below 0.40). Conclusion Surprisingly, a non-wearable device demonstrated greater agreement with PSG than clinical grade actigraphy. Though the field has generally been skeptical of commercial non-wearable devices, this independent validation provides optimism that some such devices would be efficacious for research in healthy adults. Future work is needed to test the validity of this device in older adults and clinical populations. Support (if any) National Science Foundation (1920730 and 1943323)

Vibration sensing-based human and infrastructure safety/health monitoring: A survey

Current sensor technologies enable the passive and continuous monitoring of human behaviors as well as infrastructures to ensure personal safety and assess individual health state. One passive technology that has the potential of gathering personal data is the vibration sensor. In this paper, we carry out an extensive survey of the current vibration-based sensing technologies for human and infrastructure safety as well as health monitoring. These technologies utilize structural and bodies vibration as a source of data, and they can be incorporated in wearable or non-wearable devices. Furthermore, the vibration sensing technology utilizes low-cost and low-power sensors, which make it attractive for indoor and outdoor monitoring. We have classified the technologies into five categories: vibration-based sensing for assessing human health, recognizing personal behavior, inferring occupancy information, evaluating personal safety, and monitoring infrastructure health. In each category, we also classify the approaches that utilize single and multiple sensors. Moreover, we discuss the different types of signal processing and machine learning techniques that are applied to each approach.

Feasibility and Clinical Usefulness of a Novel Nonwearable Sheet-Type Monitor (Nemuri SCAN): Prognostic Value of Increased Respiratory Rate in Actively Dying Patients.

Objective: The objective of this study was to explore the feasibility of monitoring actively dying patients hospitalized in a palliative care unit using a nonwearable sheet-type monitor that measured the state of sleep and vital signs per minute. In addition, we aimed to clarify the incidence of increased respiratory rate and its relationship with survival time. Design and Measurement: This study was conducted at a 51-bed palliative care unit in Japan from April 2018 through October 2019. Actively dying patients hospitalized in the palliative care unit were eligible to participate. Increased respiratory rate was measured by Nemuri SCAN, and patient's information was extracted from their medical records. Results: In this study, 23 patients were monitored until death; 19 patients with an observational period of 7 days or longer (163 patient days in total) were included in this analysis. There were no adverse events due to use of the nonwearable device. The cumulative incidence of increased respiratory rate (defined as more than 30 respiratory rate per minute) was 63.16% during the observational period, and the mean time between appearance of increased respiratory rate and death was 4.17 ± 4.04 days. Conclusion: This study clearly shows that hospitalized actively dying patients can be monitored using a nonwearable sheet-type monitor that measures sleeping state and vital signs per minute. Further studies are needed to utilize these noninvasive continuous monitoring devices in daily clinical practice.

Performance of seven consumer sleep-tracking devices compared with polysomnography.

Study ObjectivesConsumer sleep-tracking devices are widely used and becoming more technologically advanced, creating strong interest from researchers and clinicians for their possible use as alternatives to standard actigraphy. We, therefore, tested the performance of many of the latest consumer sleep-tracking devices, alongside actigraphy, versus the gold-standard sleep assessment technique, polysomnography (PSG).MethodsIn total, 34 healthy young adults (22 women; 28.1 ± 3.9 years, mean ± SD) were tested on three consecutive nights (including a disrupted sleep condition) in a sleep laboratory with PSG, along with actigraphy (Philips Respironics Actiwatch 2) and a subset of consumer sleep-tracking devices. Altogether, four wearable (Fatigue Science Readiband, Fitbit Alta HR, Garmin Fenix 5S, Garmin Vivosmart 3) and three nonwearable (EarlySense Live, ResMed S+, SleepScore Max) devices were tested. Sleep/wake summary and epoch-by-epoch agreement measures were compared with PSG.ResultsMost devices (Fatigue Science Readiband, Fitbit Alta HR, EarlySense Live, ResMed S+, SleepScore Max) performed as well as or better than actigraphy on sleep/wake performance measures, while the Garmin devices performed worse. Overall, epoch-by-epoch sensitivity was high (all ≥0.93), specificity was low-to-medium (0.18–0.54), sleep stage comparisons were mixed, and devices tended to perform worse on nights with poorer/disrupted sleep.ConclusionsConsumer sleep-tracking devices exhibited high performance in detecting sleep, and most performed equivalent to (or better than) actigraphy in detecting wake. Device sleep stage assessments were inconsistent. Findings indicate that many newer sleep-tracking devices demonstrate promising performance for tracking sleep and wake. Devices should be tested in different populations and settings to further examine their wider validity and utility.

A non-invasive and non-wearable food intake monitoring system based on depth sensor

The food intake counting method showed a good significance that can lead to a successful weight loss by simply monitoring the food intake taken during eating. The device used in this project was Kinect Xbox One which used a depth camera to detect the motion of a person’s gesture and posture during food intake. Previous studies have shown that most of the methods used to count food intake device is worn device type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect camera and monitors the gesture of the user while eating. Then, the gesture data is collected to be recognized and it will start counting the food intake taken by the user. The system recognizes the patterns of the food intake from the user by following the algorithm to analyze the gesture of the basic eating type and the system get an average accuracy of 96.2%. This system can help people who are trying to follow a proper way to avoid being overweight or having eating disorders by monitoring their meal intake and controlling their eating rate.

Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication.

Nano-scaled structures, wireless sensing, wearable devices, and wireless communications systems are anticipated to support the development of new next-generation technologies in the near future. Exponential rise in future Radio-Frequency (RF) sensing systems have demonstrated its applications in areas such as wearable consumer electronics, remote healthcare monitoring, wireless implants, and smart buildings. In this paper, we propose a novel, non-wearable, device-free, privacy-preserving Wi-Fi imaging-based occupancy detection system for future smart buildings. The proposed system is developed using off-the-shelf non-wearable devices such as Wi-Fi router, network interface card, and an omnidirectional antenna for future body centric communication. The core idea is to detect presence of person along its activities of daily living without deploying a device on person’s body. The Wi-Fi signals received using non-wearable devices are converted into time–frequency scalograms. The occupancy is detected by classifying the scalogram images using an auto-encoder neural network. In addition to occupancy detection, the deep neural network also identifies the activity performed by the occupant. Moreover, a novel encryption algorithm using Chirikov and Intertwining map-based is also proposed to encrypt the scalogram images. This feature enables secure storage of scalogram images in a database for future analysis. The classification accuracy of the proposed scheme is 91.1%.