scholarly journals Monitoring and Predicting Occupant’s Sleep Quality by Using Wearable Device OURA Ring and Smart Building Sensors Data (Living Laboratory Case Study)

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 459
Author(s):  
Elena Malakhatka ◽  
Anas Al Al Rahis ◽  
Osman Osman ◽  
Per Lundqvist
Keyword(s):  
Sleep Quality ◽  
Ambient Air ◽  
Wearable Devices ◽  
Wearable Device ◽  
Physiological Data ◽  
Smart Meters ◽  
Smart Building ◽  
Good Potential ◽  
Indoor Comfort

Today’s commercially-off-the-shelf (COST) wearable devices can unobtrusively capture several important parameters that may be used to measure the indoor comfort of building occupants, including ambient air temperature, relative humidity, skin temperature, perspiration rate, and heart rate. These data could be used not only for improving personal wellbeing, but for adjusting a better indoor environment condition. In this study, we have focused specifically on the sleeping phase. The main purpose of this work was to use the data from wearable devices and smart meters to improve the sleep quality of residents living at KTH Live-in-Lab. The wearable device we used was the OURA ring which specializes in sleep monitoring. In general, the data quality showed good potential for the modelling phase. For the modelling phase, we had to make some choices, such as the programming language and the AI algorithm, that was the best fit for our project. First, it aims to make personal physiological data related studies more transparent. Secondly, the tenants will have a better sleep quality in their everyday life if they have an accurate prediction of the sleeping scores and ability to adjust the built environment. Additionally, using knowledge about end users can help the building owners to design better building systems and services related to the end-user’s wellbeing.

Comparisons of physical activity and sedentary behavior between owners and non-owners of commercial wearable devices

2021 ◽  
Vol 141 (2) ◽  
pp. 89-96
Author(s):  
Hsin-Yen Yen ◽  
Hao-Yun Huang
Keyword(s):  
Physical Activity ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Sedentary Time ◽  
Short Form ◽  
Wearable Devices ◽  
Wearable Device ◽  
World Health ◽  
World Health Organization Quality ◽  
Quality Of Life Scale

Aims: Wearable devices are a new strategy for promoting physical activity in a free-living condition that utilizes self-monitoring, self-awareness, and self-determination. The main purpose of this study was to explore health benefits of commercial wearable devices by comparing physical activity, sedentary time, sleep quality, and other health outcomes between individuals who used and those that did not use commercial wearable devices. Methods: The research design was a cross-sectional study using an Internet survey in Taiwan. Self-administered questionnaires included the International Physical Activity Questionnaire–Short Form, Pittsburgh Sleep Quality Index, Health-Promoting Lifestyle Profile, and World Health Organization Quality-of-Life Scale. Results: In total, 781 participants were recruited, including 50% who were users of wearable devices and 50% non-users in the most recent 3 months. Primary outcomes revealed that wearable device users had significantly higher self-reported walking, moderate physical activity, and total physical activity, and significantly lower sedentary time than non-users. Wearable device users had significantly better sleep quality than non-users. Conclusion: Wearable devices inspire users’ motivation, engagement, and interest in physical activity through habit formation. Wearable devices are recommended to increase physical activity and decrease sedentary behavior for promoting good health.

scholarly journals A demand for wearable devices in health care

2018 ◽  
Vol 7 (1.7) ◽  
pp. 1 ◽  
Author(s):  
K. Vijayalakshmi ◽  
S. Uma ◽  
R. Bhuvanya ◽  
A. Suresh
Keyword(s):  
Digital Health ◽  
Wearable Devices ◽  
Physiological Data ◽  
Wireless Data ◽  
Telecommunication System ◽  
Future Directions ◽  
Research Survey ◽  
Wireless Data Transmission ◽  
Body Activity

With the popularity of wearable devices, along with the development of telecommunication system there is a need for obtaining the health and fitness outcomes. So the recent advances in data analysis techniques have opened up new possibilities for using wearable technology in the digital health ecosystem. In past, it’s too difficult to use the wearable devices for healthcare system because of the size of those sensors. But now with front end amplification and wireless data transmission, the wearable devices are deployed in health monitoring systems. Although the devices are continuously monitoring the human’s body activity and collect various physiological data to increase the quality of human’s life. In this paper first we provide a research survey on available wearable or gadgets. Also we conclude with future directions in wearable research and market.

scholarly journals Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study

10.2196/24604 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e24604
Author(s):  
Yuezhou Zhang ◽  
Amos A Folarin ◽  
Shaoxiong Sun ◽  
Nicholas Cummins ◽  
Rebecca Bendayan ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptom ◽  
Sleep Quality ◽  
Symptom Severity ◽  
Wearable Devices ◽  
Wearable Device ◽  
Z Score ◽  
Depressive Symptom Severity ◽  
Study Sites ◽  
Entire Dataset

Background Sleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. Objective The main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). Methods Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z score was used to evaluate the significance of the coefficient of each feature. Results We tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P<.05) associated with the PHQ-8 score on the entire dataset, among them awake time percentage (z=5.45, P<.001), awakening times (z=5.53, P<.001), insomnia (z=4.55, P<.001), mean sleep offset time (z=6.19, P<.001), and hypersomnia (z=5.30, P<.001) were the top 5 features ranked by z score statistics. Associations between sleep features and PHQ-8 scores varied across different sites, possibly due to differences in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. Conclusions We demonstrated that several derived sleep features extracted from consumer wearable devices show potential for the remote measurement of sleep as biomarkers of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

scholarly journals Wearable Devices for Biofeedback Rehabilitation: A Systematic Review and Meta-Analysis to Design Application Rules and Estimate the Effectiveness on Balance and Gait Outcomes in Neurological Diseases

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3444
Author(s):  
Thomas Bowman ◽  
Elisa Gervasoni ◽  
Chiara Arienti ◽  
Stefano Giuseppe Lazzerini ◽  
Stefano Negrini ◽  
...  
Keyword(s):  
Systematic Review ◽  
Positive Reinforcement ◽  
Neurological Diseases ◽  
Dynamic Balance ◽  
Meta Analysis ◽  
Wearable Devices ◽  
Wearable Device ◽  
Positive Effects ◽  
Body Parameters

Wearable devices are used in rehabilitation to provide biofeedback about biomechanical or physiological body parameters to improve outcomes in people with neurological diseases. This is a promising approach that influences motor learning and patients’ engagement. Nevertheless, it is not yet clear what the most commonly used sensor configurations are, and it is also not clear which biofeedback components are used for which pathology. To explore these aspects and estimate the effectiveness of wearable device biofeedback rehabilitation on balance and gait, we conducted a systematic review by electronic search on MEDLINE, PubMed, Web of Science, PEDro, and the Cochrane CENTRAL from inception to January 2020. Nineteen randomized controlled trials were included (Parkinson’s n = 6; stroke n = 13; mild cognitive impairment n = 1). Wearable devices mostly provided real-time biofeedback during exercise, using biomechanical sensors and a positive reinforcement feedback strategy through auditory or visual modes. Some notable points that could be improved were identified in the included studies; these were helpful in providing practical design rules to maximize the prospective of wearable device biofeedback rehabilitation. Due to the current quality of the literature, it was not possible to achieve firm conclusions about the effectiveness of wearable device biofeedback rehabilitation. However, wearable device biofeedback rehabilitation seems to provide positive effects on dynamic balance and gait for PwND, but higher-quality RCTs with larger sample sizes are needed for stronger conclusions.

scholarly journals Sleep in the Natural Environment: A Pilot Study

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1378
Author(s):  
Fayzan F. Chaudhry ◽  
Matteo Danieletto ◽  
Eddye Golden ◽  
Jerome Scelza ◽  
Greg Botwin ◽  
...  
Keyword(s):  
Pilot Study ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Large Scale ◽  
Physiological Data ◽  
Sleep Cycle ◽  
Recording Equipment ◽  
Digital Devices ◽  
Smart Watch

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (p < 0.05). For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at p = 0.016 and p = 0.013. We did not find any significant associations between SRSMs and PSQI or cognitive scores. Additionally, Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure was statistically significant at p = 0.004 and p = 0.033, respectively. These findings can hopefully be used to guide future sensor-based sleep research.

scholarly journals Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

10.2196/20738 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e20738
Author(s):  
Sylvia Cho ◽  
Ipek Ensari ◽  
Chunhua Weng ◽  
Michael G Kahn ◽  
Karthik Natarajan
Keyword(s):  
Data Quality ◽  
Wearable Devices ◽  
Heterogeneous Data ◽  
Wearable Device ◽  
Bring Your Own Device ◽  
Contributing Factors ◽  
Data Governance ◽  
Related Factors ◽  
Factors Affecting

Background There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse. Objective This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research. Methods The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well. Results A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy. Conclusions Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

scholarly journals Sleep in the Natural Environment: A Pilot Study

2020 ◽  
Author(s):  
Fayzan F. Chaudhry ◽  
Matteo Danieletto ◽  
Eddye Golden ◽  
Jerome Scelza ◽  
Greg Botwin ◽  
...  
Keyword(s):  
Pilot Study ◽  
Sleep Quality ◽  
Large Scale ◽  
Physiological Data ◽  
Sleep Cycle ◽  
Sleep Research ◽  
Research Setting ◽  
Recording Equipment ◽  
Digital Devices

Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains such as psychiatry and cardiology. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Recently, a number of commercial digital devices have been developed that record physiological data which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. In this pilot study of 21 participants, we investigated whether outputs from four sensors, specifically FitBit, Withings Aura, Hexoskin, and Oura Ring, were related to known cognitive and psychological metrics, including the PSQI and N-back test. We found that sleep metrics extracted from these devices did not predict cognitive and psychological metrics well in our pilot data. However, we did identify certain signification associations, specifically the Oura Ring&rsquo;s total sleep duration and efficiency in relation to the PSQI measure with p=0.004 and p=0.033, respectively. Additionally, correlation of various sleep features among the devices across the sleep cycle was almost uniformly low. These findings can hopefully be used to guide future sensor-based sleep research.

scholarly journals A Cancelable Template for the Low-Quality Fingerprints from Wearable Devices

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Sanghoon Lee ◽  
Ik Rae Jeong
Keyword(s):  
Wearable Devices ◽  
Wearable Device ◽  
Sensitive Information ◽  
Biometric Authentication ◽  
Cancelable Template ◽  
The Common ◽  
Excellent Accuracy

Biometric authentication in wearable devices is different from the common biometric authentication systems. First of all, sensitive information such as fingerprint and iris of a user is stored in a wearable device owned by the user rather than being stored in a remote database. Wearable devices are portable, and there is a risk that the devices might be lost or stolen. In addition, the quality of the extracted image from the wearable devices is lower than that of the common biometric acquisition sensor. In the paper, we propose a novel cancelable fingerprint template which is irreversible to the original biometrics and has excellent accuracy even in low quality images.

scholarly journals The Relationship between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multi-centre Longitudinal Observational Study (Preprint)

2020 ◽  
Author(s):  
Yuezhou Zhang ◽  
Amos A Folarin ◽  
Shaoxiong Sun ◽  
Nicholas Cummins ◽  
Rebecca Bendayan ◽  
...  
Keyword(s):  
Mental Health ◽  
Depressive Symptom ◽  
Sleep Quality ◽  
Symptom Severity ◽  
Wearable Devices ◽  
Wearable Device ◽  
Depression Symptom ◽  
Depressive Symptom Severity ◽  
Study Sites ◽  
Entire Dataset

BACKGROUND Sleep problems tend to vary accordingly to the course of the disorder in individuals with mental health problems. Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), is not suitable for long-term, continuous, monitoring of daily sleep, and methods such as sleep diaries rely on subjective recall, which is qualitative and inaccurate. Wearable devices, on the other hand, provide a low-cost and convenient means to monitor sleep in home settings. OBJECTIVE The main aim of this study was to devise and extract sleep features, from data collected using a wearable device, and analyse their correlation with depressive symptom severity and sleep quality, as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). METHODS Daily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every two weeks by the PHQ-8. The data used in this paper included 2,812 PHQ-8 records from 368 participants recruited from three study sites in the Netherlands, Spain, and the UK. We extracted 21 sleep features from Fitbit data which describe the participant’s sleep in the following five aspects: sleep architecture, sleep stability, sleep quality, insomnia, and hypersomnia. Linear mixed regression models were used to explore associations between sleep features and depressive symptom severity. The z-test was used to evaluate the significance of the coefficient of each feature. RESULTS We tested our models on the entire dataset and individually on the data of three different study sites. We identified 16 sleep features that were significantly (P < .05) correlated with the PHQ-8 score on the entire dataset, among them, awake proportion (z = 5.45, P < .001), awakening times (z = 5.53, P < .001), insomnia (z = 4.55, P < .001), mean sleep offset time (z = 6.19, P < .001) and hypersomnia (z = 5.30, P < .001) were the top 5 features ranked by z-test statistics. Associations between sleep features and the PHQ-8 score varied across different sites, possibly due to the difference in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires. CONCLUSIONS Although consumer wearable devices may not be a substitute for PSG to assess sleep quality accurately, we demonstrate that some derived sleep features extracted from these wearable devices show potential for remote measurement of sleep as a biomarker of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.

scholarly journals A dedicated wearable device integrated in textiles

2020 ◽  
Vol 71 (01) ◽  
pp. 68-72
Author(s):  
MONICA LEBA ◽  
ANDREEA CRISTINA IONICĂ ◽  
MARIUS NICOLAE RÎȘTEIU
Keyword(s):  
Quality Of Life ◽  
Technological Progress ◽  
Wearable Devices ◽  
Wearable Device ◽  
Persons With Disabilities ◽  
Textile Product

The wearable devices have a big role in increasing the quality of life that should take into account also the persons withdisabilities. Our research focuses on a certain category of persons with disabilities, namely those with visual impair-ments. Thus, once again the technological progress must be capitalized in favour of the less fortunate. We propose anoriginal device easy to use and integrate into any textile product. The device contains a microcontroller, sensors andactuators. The sensors collect information from the outside world and provide a “picture” of it by means of tactile andacoustic actuators. The research opens the possibility of designing of fabrics using nanotechnology to have sensors andactuators directly into the fabric

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