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Article

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors

1
Ecole Polytechnique Federale de Lausanne (EPFL), Laboratory of Movement Analysis and Measurement (LMAM), CH-1015 Lausanne, Switzerland
2
Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, N-7491 Trondheim, Norway
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Ryan S. McGinnis and Ellen W. McGinnis
Sensors 2022, 22(3), 1117; https://doi.org/10.3390/s22031117
Received: 5 January 2022 / Revised: 27 January 2022 / Accepted: 28 January 2022 / Published: 1 February 2022
(This article belongs to the Special Issue Application of Wearables in Digital Medicine)
Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear. View Full-Text
Keywords: activity monitoring; wearable devices; non-wearing time; accelerometer; temperature sensor; event-based detection algorithms activity monitoring; wearable devices; non-wearing time; accelerometer; temperature sensor; event-based detection algorithms
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MDPI and ACS Style

Pagnamenta, S.; Grønvik, K.B.; Aminian, K.; Vereijken, B.; Paraschiv-Ionescu, A. Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors. Sensors 2022, 22, 1117. https://doi.org/10.3390/s22031117

AMA Style

Pagnamenta S, Grønvik KB, Aminian K, Vereijken B, Paraschiv-Ionescu A. Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors. Sensors. 2022; 22(3):1117. https://doi.org/10.3390/s22031117

Chicago/Turabian Style

Pagnamenta, Sara, Karoline B. Grønvik, Kamiar Aminian, Beatrix Vereijken, and Anisoara Paraschiv-Ionescu. 2022. "Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors" Sensors 22, no. 3: 1117. https://doi.org/10.3390/s22031117

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