Unobtrusive Sleep Monitoring using Smartphones

Chen, Zhenyu; Lin, Mu; Chen, Fanglin; Lane, Nicholas D.; Cardone, Giuseppe; Wang, Rui; Li, Tianxing; Chen, Yiqiang; Choudhury, Tanzeem; Campbell, Andrew T.

doi:10.4108/icst.pervasivehealth.2013.252148

Cited by 238 publications

(161 citation statements)

References 11 publications

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“…They evaluated their approach on 10 healthy pilgrims for 18 days. Zhenyu Chen et al [4] presented an approach for measuring sleep duration based on a novel best effort sleep (BES) model. The BES model infers sleep using smartphones in a completely unobtrusive way.…”

Section: Related Workmentioning

confidence: 99%

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Eldib

Deboeverie

Philips

et al. 2015

Human Behavior Understanding

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Abstract. Nearly half of the senior citizens report difficulty initiating and maintaining sleep. Frequent visits to the bathroom in the middle of the night is considered as one of the major reasons for sleep disorder. This leads to serious diseases such as depression and diabetes. In this paper, we propose to use a network of cheap low-resolution visual sensors (30 × 30 pixels) for long-term activity analysis of a senior citizen in a service flat. The main focus of our research is on elderly behaviour analysis to detect health deterioration. Specifically, this paper treats the analysis of sleep patterns. Firstly, motion patterns are detected. Then, a rule-based approach on the motion patterns is proposed to determine the wake up time and sleep time. The nightly bathroom visit is identified using a classification-based model. In our evaluation, we performed experiments on 10 months of real-life data. The ground truth is collected from the diaries in which the senior citizen wrote down his sleep time and wake up time. The results show accurate extraction of the sleep durations with an overall Mean Absolute Error (MAE) of 22.91 minutes and Spearman correlation coefficient of 0.69. Finally, the nightly bathroom visits analysis indicate sleep disorder in several nights.

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Section: Related Workmentioning

confidence: 99%

Sleep Analysis for Elderly Care Using a Low-Resolution Visual Sensor Network

Eldib

Deboeverie

Philips

et al. 2015

Human Behavior Understanding

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“…Two very different scenarios, which suggest that "sleeping" should be separated into a distinct AC. Arguments against that are again raised through the smartphones sensor possibilities, where several studies have found objective ways to infer sleep (e.g., [32,33]). …”

Section: Activity Categoriesmentioning

confidence: 99%

Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study (Preprint)

Rohani¹,

Tuxen²,

Lopategui³

et al. 2018

Preprint

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Background: Behavioral Activation (BA) is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance BA. Objective: The objectives of this paper were to 1) automatically identify behavioral patterns through statistical analysis of the paper-based activity schedules, and 2) determine whether it is feasible to move the BA therapy format to a digital solution. Methods: We collected activity schedules from seven patients, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into seven activity categories, and statistical analyses were conducted. Results: Across all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a non-parametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z = -2.045, p = .041). Through a within-subject ANCOVA, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm ( = .265, p = .0288). Furthermore, a second-order trend indicated that ρ two hours of daily social activity was the optimal for the patients (β 2 = -0.08, t(63) = -1.22, p = .23). Conclusions:The data-driven statistical approach was able to find patterns within the behavioral traits that could help to assist, the therapist in a future application.

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“…Devices such as Fitbit [84,85] and the Jawbone UP [86,87] are wearable technologies used to allow users to monitor and record their activity levels during exercise and sleep. These devices have no display and no direct connection to the internet, the user can only access the information stored by using the companion app on a tethered mobile device [88,89,90].…”

Section: User Interactionmentioning

confidence: 99%