Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing

Masuda, Nagisa; Furukawa, Koichi; Yairi, Ikuko Eguchi

doi:10.2197/ipsjjip.30.718

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…In order to overcome the limitations of the above perception methods, such as intrusiveness, light dependence, and privacy leakage, researchers have focused on using wireless signals to implement contactless human motion perception. As early as 2010, researchers proposed to use ultrasonic signals emitted by acoustic sensors to sense the user's gait, behavioral actions, and breathing rate [7][8][9]. However, this method requires specialized acoustic equipment, which is expensive and difficult to deploy.…”

Section: Introductionmentioning

confidence: 99%

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

2023

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Detecting human head movement during sleep is important as it can help doctors to assess many physical or mental health problems, such as infantile eczema, calcium deficiency, insomnia, anxiety disorder, and even Parkinson’s disease, and provide useful clues for accurate diagnosis. To obtain the information of head movement during sleep, current solutions either use a camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes rely on light conditions and raise privacy concerns. Many people, including the elderly and infants, may be reluctant to wear wearable devices during sleep. In this paper, we propose Wi-Senser, a nonintrusive and contactless smart monitoring system for detecting head movement during sleep. Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. Specifically, we constructed a filtering channel including a Hampel filter, wavelet filter, and mean filter to remove outliers and noises. We propose a new metric of carrier sensitivity to select an optimal subcarrier for recording the change in targeted body movement from 30 candidate subcarriers. Finally, we designed a peak-finding algorithm to capture the real peak set recording the change in human head movement. We designed and implemented Wi-Senser with just one commercial off-the-shelf (COTS) router and one laptop equipped with an Intel 5300 network interface card (NIC). We evaluated the performance of Wi-Senser with 10 volunteers (6 adults and 4 children). Extensive experiments demonstrate that Wi-Senser can achieve 97.95% accuracy for monitoring head movement during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous, and accurate detection of minute human movement without any additional cost.

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Section: Introductionmentioning

confidence: 99%

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

2023

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Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?

Pasterkamp,

Melbye

2024

CHEST Pulmonary

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Wi-Senser: Contactless Head Movement Detection During Sleep Utilizing WiFi Signals

Fang¹,

Liu²,

Sun³

2023

Preprint

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Detecting human head movement during sleep is important as it can help doctor to access some physical or mental health problems like infantile eczema, calcium deficiency, insomnia, anxiety disorder and even Parkinson’s disease, and provide useful clues for scientific diagnosing. To obtain the information of sleep head movement, current solutions either use the camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes depend on the environmental light conditions and also bring privacy concerns. Some people including the elderly and infant may be reluctant to wear wearable devices during sleep. In this paper, we propose a novel system called Wi-Senser to address the issues mentioned above. Our Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. We design and implement our system with just one commercial off-the-shelf (COTS) router and one laptop equipped with Intel 5300 network interface card (NIC). We evaluate the performance of Wi-Senser with 6 volunteers (4 adults and 2 children). Extensive experiments demonstrate that Wi-Senser can achieve a 98.5% accuracy for head movement detection during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous and accurate human minute movement detection without any additional cost.

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Stetho Touch: Touch Action Recognition System by Deep Learning with Stethoscope Acoustic Sensing

Cited by 3 publications

References 36 publications

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals

Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?

Wi-Senser: Contactless Head Movement Detection During Sleep Utilizing WiFi Signals

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