SleepSensei

Krishna, Amrith; Mallick, Madhumita; Mitra, Bivas

doi:10.1145/2933566.2933570

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Finally, the system does not require a wearable device, it does not track activities during the day and the user can sleep in a natural environment. Low Obtrusiveness (6/7) Medium Obtrusiveness (5/7) Obtrusive (4/7) Hao et al [13] Fahim et al [16] Daskalova et al [17] Huang et al [27] Bai et al [18] Bauer et al [22] Krishna et al [11] Chen et al [15] Pombo & Garcia [19] Paalasmaa et al [20] Papakostas et al [21] Han et al [25] Ren et al [14] Min et al [23] Lawson et al [24] Kay et al [26]…”

Section: Resultsmentioning

confidence: 99%

“…In this paper we analyze systems that involve using smartphones. Krishna et al [11] argue that achieving high accuracy in sleep measurements has been prioritized over non-intrusiveness. They also imply that obtrusive has to do with wearable devices that are too engaging and track user activities continuously.…”

Section: Approaches To Unobtrusive Systems For Sleep Healthmentioning

confidence: 99%

“…The time when the user wakes up is indicated by the moment at which the user stops the application in the morning. Another app in the same paradigm of SleepfulApp is SleepSensei, [11], which is a sleep duration estimator designed to be unobtrusive. SleepSensei emits an auditory tone every 15 minutes during the sleep period to find out whether the user is in deep or light sleep.…”

Section: Systems With User Interactionmentioning

confidence: 99%

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Obtrusiveness of smartphone applications for sleep health

Vazquez-Briseno¹,

Laflor-Hernandez²,

Murillo-Muñoz³

et al. 2019

IJECE

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<p>Unobtrusiveness is one of the main issues concerning health-related systems. Many developers affirm that their systems do not burden users; however, this is not always achieved. This article evaluates the obtrusiveness of various systems developed to improve sleep quality. The systems analyzed are related to sleep hygiene, since it has become an interesting topic for researchers, physicians and people in general, mainly because it has become part of the methods used to estimate a persons’ health status A set of design elements are presented as keys to achieving unobtrusiveness. We propose a scale to measure the level of unobtrusiveness and use it to evaluate several systems, with a focus on smartphone applications. </p>

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Section: Resultsmentioning

confidence: 99%

Section: Approaches To Unobtrusive Systems For Sleep Healthmentioning

confidence: 99%

Section: Systems With User Interactionmentioning

confidence: 99%

See 1 more Smart Citation

Obtrusiveness of smartphone applications for sleep health

Vazquez-Briseno¹,

Laflor-Hernandez²,

Murillo-Muñoz³

et al. 2019

IJECE

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“…The detection accuracy of the Sleep Hunter proposed in this work [41] was 64.55%. Krishna et al [121] proposed SleepSensei, an automated sleep quality monitor that estimates the sleep duration for the user. It uses (1) the built-in web camera and microphone of a personal computer connected to a power source, and custom software to collect environmental features and (2) the accelerometer sensor of a smartphone to detect body movements.…”

Section: Xsl • Fomentioning

confidence: 99%

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Pan¹,

Brulin²,

Campo³

2020

JMIR Biomed Eng

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Background Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. Objective This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. Methods This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. Results By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Conclusions Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

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