ArticleSensors (Basel, Switzerland)2019
Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- Noninvasive monitoring technologies to identify discomfort and distressing symptoms in persons with limited communication at the end of life: a scoping review.BMC palliative care · 2024Article
- Recent Progress in Long-Term Sleep Monitoring Technology.Biosensors · 2023Review
- Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.Sensors (Basel, Switzerland) · 2020Article
- Characterizing Behavioral Activity Rhythms in Older Adults Using Actigraphy.Sensors (Basel, Switzerland) · 2020Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.
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