SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

Cuttone, Andrea; Bækgaard, Per; Sekara, Vedran; Jönsson, Håkan; Larsen, Jakob Eg; Lehmann, Sune

doi:10.1371/journal.pone.0169901

Cited by 43 publications

(49 citation statements)

References 25 publications

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“…The accuracy, precision, recall, and F1 global and histogram scores show that this method is also in line with and even slightly better than the expected results from an unsupervised one [19].…”

Section: Discussionsupporting

confidence: 78%

“…SensibleSleep [19] is also a discrete event based method for extracting sleep patterns. The paper uses this method in conjunction with smartphone events.…”

Section: Discussionmentioning

confidence: 99%

“…Some are based on heartbeat monitoring [15,16] or accelerometers (actigraphybased solutions) [17], while others use simpler features like smartphone events (screen activations [18,19], audio features [20], app usage [21]. .…”

Section: Related Workmentioning

confidence: 99%

“…Unsupervised methods are mostly model based inference algorithms that can work on a small amount of data. They range from simple rule-based algorithms [18] to Monte-Carlo Markov Chain setups [19].…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Sleep Activity Recognition Using Binary Motion Sensors

El-Khadiri

Corona²,

Rose³

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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Early detection of frailty signs is important for the senior population that prefers to keep living in their homes instead of moving to a nursing home. Sleep quality is a good predictor for frailty monitoring. Thus we are interested in tracking sleep parameters like sleep wake patterns to predict and detect potential sleep disturbances of the monitored senior residents. We use an unsupervised inference method based on actigraphy data generated by ambient motion sensors scattered around the senior's apartment. This enables our monitoring solution to be flexible and robust to the different types of housings it can equip while still attaining accuracy of 0.94 for sleep period estimates.

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Section: Discussionsupporting

confidence: 78%

“…SensibleSleep [19] is also a discrete event based method for extracting sleep patterns. The paper uses this method in conjunction with smartphone events.…”

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Sleep Activity Recognition Using Binary Motion Sensors

El-Khadiri

Corona²,

Rose³

et al. 2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

show abstract

“…With the prevalent usage of smartphones and wearables, personal, quantifiable, and accurate data on everyday phenomena has become broadly available. Such data has been applied for health tracking within QS and covers a vast range of phenomena, including menstrual tracking [23], mental health in students [24,25], Post-traumatic stress disorder (PTSD) effects [26], sleep patterns [27] and diabetes management [28], to mention only a few. The examples illustrate that such data can lead to new personal discoveries, insights and improved health in terms of quality of life.…”

Section: Making User-generated Data An Essential Part Of Hearing Healmentioning

confidence: 99%

Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

et al. 2017

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The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium "one size fits all" program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users. reasons for not using a hearing aid were that the devices did not provide sufficient benefits in noisy situations and there was a perceived poor quality of sound [9].One may ask, why is it that people do not choose to use hearing aids, given the evidence of a high risk of incident dementia, and knowing that these could potentially alleviate cognitive decline? Studies analyzing outcome measures capturing the user satisfaction indicate that this is largely determined by two factors: (1) whether the user perceives an improved quality of life through use of the devices, and (2) to what degree they help overcome limitations when interacting with others around the user. The degree to which the user feels involved in the traditional clinical fitting process highly impacts the overall satisfaction [10]. Alternative models for selling hearing aids over the counter based on do-it-yourself audiometry tests may technically provide the same fitting as provided in a clinical setting [11]. However, the lack of dialogue and hearing care counseling has been shown to result in lower satisfaction. Actively involving the user in shaping the listening experience when adapting to the devices appears to be crucial.

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Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies

2017

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Unlike most other health conditions, the treatment of mental illness relies on subjective measurement. In addition, the criteria for diagnosing mental illnesses are based on broad categories of symptoms that do not account for individual deviations from these criteria. The increasing availability of personal digital devices, such as smartphones that are equipped with sensors, offers a new opportunity to continuously and passively measure human behavior in situ. This promises to lead to more precise assessment of human behavior and ultimately individual mental health. More refined modeling of individual mental health and a consideration of individual context, assessed through continuous monitoring, opens the way for more precise and personalized digital interventions that may help increase the number of positive clinical outcomes in mental healthcare. In this paper, we provide a conceptual review of such techniques for measuring, modeling, and treating mental illness and maintaining mental health.

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SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events

Cited by 43 publications

References 25 publications

Sleep Activity Recognition Using Binary Motion Sensors

Sleep Activity Recognition Using Binary Motion Sensors

Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

Sensing behavioral symptoms of mental health and delivering personalized interventions using mobile technologies

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