NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset

Pai, Yun Suen; Bait, Marsel L.; Lee, Juyoung; Peiris, Roshan Lalintha; Woo, Woontack; Billinghurst, Mark; Kunze, Kai

doi:10.1007/s10055-021-00571-w

Cited by 8 publications

(4 citation statements)

References 65 publications

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“…EOG can contain either horizontal data or vertical data, and in some cases, both depending on the electrode configuration of data acquisition. Nowadays, modified elctrode configuartion [29], eyeglasses [20], EOG sensors [30], optical sensors [31], and contact lenses [32] are also available for EOG data acquisition. The EOG data suffers from some noises that introduce inaccuracies in diagnosis and other applications.…”

Section: Electrooculogrammentioning

confidence: 99%

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Das,

Chowdhury,

Chowdhury

et al. 2023

JLPEA

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The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

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Section: Electrooculogrammentioning

confidence: 99%

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Das,

Chowdhury,

Chowdhury

et al. 2023

JLPEA

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show abstract

“…Beach et al achieved comfortable wear of eye movement detection devices by integrating EOG sensors in an eye patch through fabric sensor electrodes made of nylon and graphene. Though, with EOG alone, the accuracy of sleep time calculation is only about 70% [ 104 ]. However, EOG can be included in the sleep cycle analysis as an important item in sleep polysomnography.…”

Section: Bioelectrical Signal Monitoringmentioning

confidence: 99%

Recent Progress in Long-Term Sleep Monitoring Technology

Yin

Ren

2023

Biosensors

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Sleep is an essential physiological activity, accounting for about one-third of our lives, which significantly impacts our memory, mood, health, and children’s growth. Especially after the COVID-19 epidemic, sleep health issues have attracted more attention. In recent years, with the development of wearable electronic devices, there have been more and more studies, products, or solutions related to sleep monitoring. Many mature technologies, such as polysomnography, have been applied to clinical practice. However, it is urgent to develop wearable or non-contacting electronic devices suitable for household continuous sleep monitoring. This paper first introduces the basic knowledge of sleep and the significance of sleep monitoring. Then, according to the types of physiological signals monitored, this paper describes the research progress of bioelectrical signals, biomechanical signals, and biochemical signals used for sleep monitoring. However, it is not ideal to monitor the sleep quality for the whole night based on only one signal. Therefore, this paper reviews the research on multi-signal monitoring and introduces systematic sleep monitoring schemes. Finally, a conclusion and discussion of sleep monitoring are presented to propose potential future directions and prospects for sleep monitoring.

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“…A mindfulness intervention therapy based on VR has been proposed by Navarro-Haro 33 et al for the treatment of generalized anxiety disorder, and the negative emotions of patients were improved. Pai et al 13 proposed a VR based sleep assistant-Nap Well. Lee et al 14 investigated the effect of VR meditation in critically ill patients.…”

Section: Vr-based Audiovisual Stimulationmentioning

confidence: 99%

“…VR systems provide the ability to design and simulate interactive environments that offer great advantages in providing audiovisual stimuli to users, and in September 2020, researchers from Stanford, Zambotti et al 12 suggested that VR technology could be applied as a therapeutic approach to improve people's sleep quality. Some VR-based sleep assistants have been developed to improve people's sleep, such as NapWell 13 , meditation 14 , and audiovisual scenarios 15 .…”

Section: Introductionmentioning

confidence: 99%

VR and DL Empowered Progressive Muscle Relaxation for Sleep Facilitation and Monitoring

Cai,

Liu,

Zou

et al. 2023

Preprint

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Insomnia is a widespread public health issue with global implications, garnering significant attention in the field of biomedicine. Sleep deprivation result in various problems, including affect one’s memory and cognitive abilities. With the development of modern technologies, such as deep learning (DL), virtual reality (VR), utilizing these technologies to improve sleep has become an open and hot research. In this paper, we developed the SleepSenseVR system with sleep behavior recognition algorithm based on deep learning and progressive muscle relaxation based on sleep state feedback (PMR-SSF) threapy to decrease sleep onset latency. The sleeping-aid system promotes progressive muscle relaxation (PMR) integrated DL, VR and multimedia technology for sleep assistance. We conducted user experiments, inviting 36 participants to test three different systems, including VR, VR combined with PMR, and VR combined with PMR-SSF. This paper empirically evaluates these three systems, and the experimental results show that PMR-SSF is an effective method for sleep promotion, providing theoretical and practical value for the rapid development of sleep medicine.

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NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset

Cited by 8 publications

References 65 publications

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Recent Progress in Long-Term Sleep Monitoring Technology

VR and DL Empowered Progressive Muscle Relaxation for Sleep Facilitation and Monitoring

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