An ergonomic wearable core body temperature sensor

Atallah, Louis; Ciuhu, Calina; Wang, Chao; Bongers, Edwin; Blom, Toon; Paulussen, Igor; Noordergraaf, Gerrit J.

doi:10.1109/bsn.2018.8329661

Cited by 18 publications

(10 citation statements)

References 10 publications

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“…Should water intake be adjusted according to body composition, daily max ambient temperature, Mean temperature, or a composite measure accounting for time spent outdoors, total amount of direct sunlight, temperature and wind speed? Wearable sensors capable of recording climatic parameters are becoming increasingly prevalent [68,69,70,71], and perhaps large population investigations will soon have access to more accurate estimations of internal body temperature [72,73], sweat rate [74], and physical activity due to an increasingly robust infrastructure to support analysis of pedometers [75]. Furthermore, we recommend reporting the model goodness of fit as is conventional in modeling [76,77,78].…”

Section: Discussionmentioning

confidence: 99%

Unexplained Variance in Hydration Study

Muñoz

Wininger

2019

Nutrients

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With the collection of water-intake data, the National Health and Nutrition Examination Survey (NHANES) is becoming an increasingly popular resource for large-scale inquiry into human hydration. However, are we leveraging this resource properly? We sought to identify the opportunities and limitations inherent in hydration-related inquiry within a commonly studied database of hydration and nutrition. We also sought to critically review models published from this dataset. We reproduced two models published from the NHANES dataset, assessing the goodness of fit through conventional means (proportion of variance, R2). We also assessed model sensitivity to parameter configuration. Models published from the NHANES dataset typically yielded a very low goodness of fit R2 < 0.15. A reconfiguration of variables did not substantially improve model fit, and the goodness of fit of models published from the NHANES dataset may be low. Database-driven inquiry into human hydration requires the complete reporting of model diagnostics in order to fully contextualize findings. There are several emergent opportunities to potentially increase the proportion of explained variance in the NHANES dataset, including novel biomarkers, capturing situational variables (meteorology, for example), and consensus practices for adjustment of co-variates.

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Section: Discussionmentioning

confidence: 99%

Unexplained Variance in Hydration Study

Muñoz

Wininger

2019

Nutrients

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“…On average, it is around 36 to 37 °C (97 to 99 °F), with variations depending on age, gender, emotions, activities, and the time of day. Several diseases, infections, and deteriorating conditions are identified by changes in body temperature; thus, measuring body temperature is considered an important parameter in medicine [ 106 ].…”

Section: Wearable Sensors In Health Monitoringmentioning

confidence: 99%

“Listen to Your Immune System When It’s Calling for You”: Monitoring Autoimmune Diseases Using the iShU App

Ortet

Costa

2022

Sensors

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The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.

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“…In particular, thermistor sensors are generally popular, cost-efficient and ideal for many wearables, yet their accuracy is influenced by a number of factors including the measurement’s location (e.g., wrist, forehead, etc. ), ambient temperature (e.g., hot or cold environment), sensor strain, sweating and the distance between the sensor and skin [ 71 , 72 , 73 , 74 , 75 ]. Optical methods, based on FBG or infrared technology, are quite similar to thermistors in terms of accuracy, comfortability and cost [ 76 ].…”

Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

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The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

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An ergonomic wearable core body temperature sensor

Cited by 18 publications

References 10 publications

Unexplained Variance in Hydration Study

Unexplained Variance in Hydration Study

“Listen to Your Immune System When It’s Calling for You”: Monitoring Autoimmune Diseases Using the iShU App

Sensors for Context-Aware Smart Healthcare: A Security Perspective

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