SleepSense: Non-invasive sleep event recognition using an electromagnetic probe

Zhuang, Yan; Song, Chen; Wang, Aosen; Lin, Feng; Li, Yiran; Gu, Changzhan; Li, Changzhi; Xu, Wenyao

doi:10.1109/bsn.2015.7299364

Cited by 9 publications

(10 citation statements)

References 11 publications

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“…Physical activity recognition 17 --10 7 -- [8] Physical activity recognition 14 ------ [9] Physical activity recognition 4 - For completeness, the remaining 13 articles not listed in Table 2 and 3 were distributed over eight article categories: Asthma/COPD [63], Cardiovascular diseases [64], Gait and fall [65][66][67], Neurological diseases [68], Physical activity recognition [69], Rehabilitation [70], Stress and sleep [71], and Additional [72][73][74][75]. Six articles report on systems where studies are upcoming [63,64,[72][73][74][75].…”

Section: Qualitative Synthesismentioning

confidence: 99%

“…The continuation of the qualitative study [70] is reported upon in [48]. The evaluation in [65] is not clearly presented and the system developed in [71] uses wearable body sensors only to collect ground truth data for a contactless sleep monitoring system. Therefore, [71] was excluded from further qualitative analysis.…”

Section: Qualitative Synthesismentioning

confidence: 99%

“…The evaluation in [65] is not clearly presented and the system developed in [71] uses wearable body sensors only to collect ground truth data for a contactless sleep monitoring system. Therefore, [71] was excluded from further qualitative analysis. Table 2 reports on the four research designs identified while analyzing the research articles: case-control, crossover, randomized control and observational.…”

Section: Qualitative Synthesismentioning

confidence: 99%

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A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.

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Section: Qualitative Synthesismentioning

confidence: 99%

Section: Qualitative Synthesismentioning

confidence: 99%

Section: Qualitative Synthesismentioning

confidence: 99%

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A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

Kristoffersson

Lindén

2020

Sensors

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“…RF sensing has been used for analysing walking patterns or gait [ 36 , 37 , 38 ], tracking sleep quality and breathing patterns [ 39 , 40 ], and recognising movements of body parts such as hands for interactive purposes [ 31 , 35 , 41 , 42 , 43 , 44 , 45 ]. The radars used in these studies operated at various frequencies, ranging from 2.4 GHz [ 40 , 42 ] to 24 GHz [ 39 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Attygalle

Leiva

Kljun

et al. 2021

Sensors

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Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

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“…This is probably due to the large size, high power consumption and significant computational demand required to generate and process radar signals until recently. As the first miniature impulse radars were developed in the 1990s [Azevedo and McEwan 1996] and radar technology has become more accessible, driven primarily by the proliferation of radar in automotive applications, there have been a few explorations of radar for presence detection, recognizing walking patterns, and detecting breathing and sleep patterns [Rahman et al 2015;Zhuang et al 2015;Otero 2005;Wang and Fathy 2011].…”

Section: Background and Related Workmentioning

confidence: 99%

Soli

et al. 2016

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This paper presents Soli, a new, robust, high-resolution, low-power, miniature gesture sensing technology for human-computer interaction based on millimeter-wave radar. We describe a new approach to developing a radar-based sensor optimized for human-computer interaction, building the sensor architecture from the ground up with the inclusion of radar design principles, high temporal resolution gesture tracking, a hardware abstraction layer (HAL), a solidstate radar chip and system architecture, interaction models and gesture vocabularies, and gesture recognition. We demonstrate that Soli can be used for robust gesture recognition and can track gestures with sub-millimeter accuracy, running at over 10,000 frames per second on embedded hardware.

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SleepSense: Non-invasive sleep event recognition using an electromagnetic probe

Cited by 9 publications

References 11 publications

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring: A Qualitative Synthesis

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing

Soli

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