Processing of wearable sensor data on the cloud - a step towards scaling of continuous monitoring of health and well-being

2010 ◽  
Author(s):  
Jit Biswas ◽  
Jayachandran Maniyeri ◽  
Kavitha Gopalakrishnan ◽  
Louis Shue ◽  
Phua Jiliang Eugene ◽  
...  
Keyword(s):  
Continuous Monitoring ◽  
Well Being ◽  
Sensor Data ◽  
Wearable Sensor ◽  
Health And Well Being

Internet of Things in E-Health

2017 ◽  
pp. 191-197 ◽  
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

Internet of Things in E-Health

2018 ◽  
pp. 880-885
Author(s):  
Branka Rodić Trmčić ◽  
Aleksandra Labus ◽  
Svetlana Mitrović ◽  
Vesna Buha ◽  
Gordana Stanojević
Keyword(s):  
Internet Of Things ◽  
Health Behavior ◽  
Well Being ◽  
Sensor Data ◽  
Main Task ◽  
Preventive Actions ◽  
Framework Model ◽  
The Impact ◽  
Health And Well Being ◽  
Improve Health

The main task of Internet of Things in eHealth solutions is to collect data, connect people, things and processes. This provides a wealth of information that can be useful in decision-making, improving health and well-being. The aim of this study is to identify framework of sensors and application health services to detect sources of stress and stressors and make them visible to users. Also, we aim at extracting relationship between event and sensor data in order to improve health behavior. Evaluation of the proposed framework model will be performed. Model is based on Internet of Things in eHealth and is going to aim to improve health behavior. Following the established pattern of behavior realized through wearable system users will be proposed a preventive actions model. Further, it will examine the impact of changing health behavior on habits, condition and attitudes in relation to well-being and prevention.

scholarly journals A Smart System for Sitting Posture Detection Based on Force Sensors and Mobile Application

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Slavomir Matuska ◽  
Martin Paralic ◽  
Robert Hudec
Keyword(s):  
Mobile Application ◽  
Well Being ◽  
Spinal Pain ◽  
Sensor Data ◽  
Force Sensors ◽  
Smart System ◽  
Sitting Posture ◽  
To Receive ◽  
The Internet Of Things ◽  
Health And Well Being

The employees’ health and well-being are an actual topic in our fast-moving world. Employers lose money when their employees suffer from different health problems and cannot work. The major problem is the spinal pain caused by the poor sitting posture on the office chair. This paper deals with the proposal and realization of the system for the detection of incorrect sitting positions. The smart chair has six flexible force sensors. The Internet of Things (IoT) node based on Arduino connects these sensors into the system. The system detects wrong seating positions and notifies the users. In advance, we develop a mobile application to receive those notifications. The user gets feedback about sitting posture and additional statistical data. We defined simple rules for processing the sensor data for recognizing wrong sitting postures. The data from smart chairs are collected by a private cloud solution from QNAP and are stored in the MongoDB database. We used the Node-RED application for the whole logic implementation.

scholarly journals Monitoring beliefs and physiological measures in students at risk for COVID-19 using wearable sensors and smartphone technology: Protocol for a mobile health study (Preprint)

2021 ◽  
Author(s):  
Christine Cislo ◽  
Caroline Clingan ◽  
Kristen Gilley ◽  
Michelle Rozwadowski ◽  
Izzy Gainsburg ◽  
...  
Keyword(s):  
Mental Health ◽  
At Risk ◽  
Mobile Health ◽  
Wearable Sensors ◽  
Well Being ◽  
Student Health ◽  
Wearable Sensor ◽  
Students At Risk ◽  
College Student Population ◽  
Health And Well Being

BACKGROUND The COVID-19 pandemic has impacted lives significantly and greatly affected an already vulnerable population, college students, in relation to mental health and public safety. Social distancing and isolation have brought about challenges to student’s mental health. Mobile health apps and wearable sensors may help to monitor students at risk for COVID-19 and support their mental well-being. OBJECTIVE Through the use of a wearable sensor and smartphone-based survey completion, this study aimed to monitor students at risk for COVID-19. METHODS We conducted a prospective study of students, undergraduate and graduate, at a public university in the Midwest. Students were instructed to download the Fitbit, Social Rhythms, and Roadmap 2.0 apps onto their personal mobile devices (Android or iOS). Subjects consented to provide up to 10 saliva samples during the study period. Surveys were administered through the Roadmap 2.0 app at five timepoints - at baseline, 1-month later, 2-months later, 3-months later, and at study completion. The surveys gathered information regarding demographics, COVID-19 diagnoses and symptoms, and mental health resilience, with the aim of documenting the impact of COVID-19 on the college student population. RESULTS This study enrolled 2158 college students between September 2020 and January 2021. Subjects are currently being followed on-study for one academic year. Data collection and analysis are ongoing. CONCLUSIONS This study examined student health and well-being during the COVID-19 pandemic. While data collection and analyses are ongoing, the study will assess the feasibility of wearable sensor use and survey completion in a college student population, which may inform the role of our mobile health tools on student health and well-being. Finally, using wearable sensor data, biospecimen collection, and self-reported COVID-19 diagnosis, our results may provide key data towards the development of a model for the early prediction and detection of COVID-19. CLINICALTRIAL ClinicalTrials.gov NCT04766788

scholarly journals P122 Comparison of sleep estimation using Apple Watch accelerometry against polysomnography

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A61-A61
Author(s):  
S Roomkham ◽  
D Lovell ◽  
I Szollosi ◽  
D Perrin
Keyword(s):  
Lower Cost ◽  
Total Sleep Time ◽  
Ground Truth ◽  
Well Being ◽  
Sleep Time ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Respiratory Events ◽  
Sleep Parameters ◽  
Health And Well Being

Abstract Introduction Consumer wearables offer new ways to improve our health and well-being, including sleep. Researchers are interested in consumer wearables because their widespread adoption creates the potential for larger studies than could be run with clinically validated measurement methods, as those are more expensive or less convenient. This study investigates sleep tracking using sensor data from Apple Watch in comparison to the gold standard polysomnography (PSG). Method We used Apple Watch accelerometer data to establish both activity and heart rate (using ballistocardiography). Thirty participants (13 female, 17 male) wore the Apple Watch on their non-dominant wrist during clinical PSG. We compared predicted sleep status at the epoch level and overall sleep parameters, taking PSG as the ground truth. Results Our method achieved sleep-wake classification accuracy of 84%, sensitivity of 95%, and specificity of 47%. Apple Watch overestimated total sleep time (mean+SD) by 39.4 + 57.7 mins, underestimated WASO by 45.5 + 54.6 mins and the number of awakenings by 5.0 + 6.9. We observed worse performance for participants who had PSGs exhibiting frequent respiratory events. Discussion Accelerometry cannot replace PSG for diagnostic purposes. However, the Apple Watch results compare favourably to previously published Actiwatch-PSG comparisons. The performance we measured suggests that Apple Watch based accelerometry could be used in longitudinal studies to gather information similar to clinically validated accelerometers, potentially on a larger scale for lower cost. Further study is needed to understand how sleep disorders affect this kind of measurement.

Diet Quality, a Term Subject to Change over Time

2012 ◽  
Vol 82 (3) ◽  
pp. 144-147 ◽  
Author(s):  
Ibrahim Elmadfa ◽  
Alexa L. Meyer
Keyword(s):  
Diet Quality ◽  
Functional Foods ◽  
Disease Risk ◽  
Well Being ◽  
Industrialized Countries ◽  
Quality Factors ◽  
Long Time ◽  
Quality Diet ◽  
Important Quality ◽  
Health And Well Being

A high-quality diet is one of the foundations of health and well-being. For a long time in human history, diet was chiefly a source of energy and macronutrients meant to still hunger and give the strength for work and activities that were in general much harder than nowadays. Only few persons could afford to emphasize enjoyment. In the assessment of quality, organoleptic properties were major criteria to detect spoilage and oxidative deterioration of food. Today, food hygiene is a quality aspect that is often taken for granted by consumers, despite its lack being at the origin of most food-borne diseases. The discovery of micronutrients entailed fundamental changes of the concept of diet quality. However, non-essential food components with additional health functions were still barely known or not considered important until recently. With the high burden of obesity and its associated diseases on the rise, affluent, industrialized countries have developed an increased interest in these substances, which has led to the development of functional foods to optimize special body functions, reduce disease risk, or even contribute to therapeutic approaches. Indeed, nowadays, high contents of energy, fat, and sugar are factors associated with a lower quality of food, and products with reduced amounts of these components are valued by many consumers. At the same time, enjoyment and convenience are important quality factors, presenting food manufacturers with the dilemma of reconciling low fat content and applicability with good taste and appealing appearance. Functional foods offer an approach to address this challenge. Deeper insights into nutrient-gene interactions may enable personalized nutrition adapted to the special needs of individuals. However, so far, a varied healthy diet remains the best basis for health and well-being.

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