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  2. Life Sciences und Facility Management
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  4. Publikationen Life Sciences und Facility Management
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dc.contributor.authorRerabek, Martin-
dc.contributor.authorSchiboni, Giovanni-
dc.contributor.authorDurrer, Lukas-
dc.contributor.authorOliveras, Ruben-
dc.contributor.authorEib, Philippe-
dc.contributor.authorRouchat, Fabien-
dc.contributor.authorProbst, Anja-
dc.contributor.authorSchmidt, Markus-
dc.contributor.authorKryszczuk, Krzysztof-
dc.date.accessioned2023-02-09T13:49:59Z-
dc.date.available2023-02-09T13:49:59Z-
dc.date.issued2022-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26865-
dc.description.abstractThe circadian rhythm (CR) in a healthy individual is controlled by the suprachiasmatic nuclei. CR controls physiological functions as a response to the natural day-night cycle hence it has a period close to 24 hours. A disruption of the healthy CR is often associated with pathologies on physiological and psychological levels, ranging from mood shifts, sleep disorders, to neurodegenerative diseases and tumorigenesis. This work aims at accurate, non-invasive, continuous tracking of the changes in core body temperature as a prominent manifestation of the CR. To date, the legacy methods of tracking the core body temperature are either inaccurate (such as skin temperature measurements), or excessively invasive for a long-term deployment (such as digestible thermometer pills, or rectal measurements). We use machine learning techniques to estimate the core body temperature from peripheral physiological signals such as skin temperature, heat flux, acceleration, and heart rate. To acquire data necessary for an estimation of the core body temperature under free living conditions, we used a novel, proprietary, wrist-wearable device featuring temperature, heat flux and accelerometer sensors developed at GreenTEG AG, as well off-the-shelf hardware equipped with a PPG sensor for heart rate monitoring. We created a unique database containing continuous 3-day recordings of peripheral physiological signals from 58 subjects. The reference core body temperature data, necessary for development and evaluation of the proposed algorithms, has been recorded using digestible pills for core body temperature measurements. In this poster, we demonstrate accurate core body temperature estimation from peripheral physiological signals with average rms=0.31 degrees Celsius. The accuracy of core body temperature estimation using the proposed method exceeds that of alternative devices reported in literature. We also evaluated the estimated CR trajectory in comparison to reference using correlation coefficient (ρ=0.71) and bias measure (Δ =-0.03). Proposed novel sensing technology enables accurate, non-invasive and continuous core body temperature estimation, which is crucial for tracking and detecting minute changes of the circadian rhythm. It holds a great potential to non-invasively provide relevant information about health conditions in healthy individual and clinical patients.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCircadian rhythm trackingde_CH
dc.subjectCore body temperaturede_CH
dc.subjectWearable sensorde_CH
dc.subject.ddc610: Medizin und Gesundheitde_CH
dc.titleCircadian rhythm tracking using core body temperature estimates from wearable sensor datade_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.conference.details7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.funding.zhawSensor for a wearable device for early detection of symptoms of possible neurodegenerative diseasesde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Rerabek, M., Schiboni, G., Durrer, L., Oliveras, R., Eib, P., Rouchat, F., Probst, A., Schmidt, M., & Kryszczuk, K. (2022). Circadian rhythm tracking using core body temperature estimates from wearable sensor data. 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022.
Rerabek, M. et al. (2022) ‘Circadian rhythm tracking using core body temperature estimates from wearable sensor data’, in 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022.
M. Rerabek et al., “Circadian rhythm tracking using core body temperature estimates from wearable sensor data,” in 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022, 2022.
RERABEK, Martin, Giovanni SCHIBONI, Lukas DURRER, Ruben OLIVERAS, Philippe EIB, Fabien ROUCHAT, Anja PROBST, Markus SCHMIDT und Krzysztof KRYSZCZUK, 2022. Circadian rhythm tracking using core body temperature estimates from wearable sensor data. In: 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022. Conference presentation. 2022
Rerabek, Martin, Giovanni Schiboni, Lukas Durrer, Ruben Oliveras, Philippe Eib, Fabien Rouchat, Anja Probst, Markus Schmidt, and Krzysztof Kryszczuk. 2022. “Circadian Rhythm Tracking Using Core Body Temperature Estimates from Wearable Sensor Data.” Conference presentation. In 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022.
Rerabek, Martin, et al. “Circadian Rhythm Tracking Using Core Body Temperature Estimates from Wearable Sensor Data.” 7th International Conference on Human Interaction and Emerging Technologies (IHIET), Lausanne, Switzerland, 23-25 April 2022, 2022.


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