A study of automatic classification of sleeping position by a pressure-sensitive sensor

Mineharu, Aya; Kuwahara, Noriaki; Morimoto, Kazunari

doi:10.1109/iciev.2015.7334059

Cited by 30 publications

(14 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Ref. [ 23 ], the authors extracted 55 types of features from the raw data, which are collected from pressure mat, and then applied four classification algorithms for sleep posture estimation. In Ref.…”

Section: Related Workmentioning

confidence: 99%

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Jeng

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

In the hospital, a sleep postures monitoring system is usually adopted to transform sensing signals into sleep behaviors. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost—a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve up to 85 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easy-to-wear wrist sensor can accurately quantify sleep postures after our designed training phase. It is our hope that the design concept of iSleePost can shed some lights on quantifying human sleep postures in the future.

show abstract

Section: Related Workmentioning

confidence: 99%

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Jeng

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Nishida et al proposed a system for monitoring respiration and sleeping posture using a pressure sensor with 221 measurement points [8]. Mineharu et al estimated nine sleeping postures with 77.1% accuracy using a pressure sensor with 32 × 54 measurement points [9]. Xu et al estimated six sleeping postures with 90.8% accuracy by a pressure sensor with 64 × 128 measurement points [10].…”

Section: Related Workmentioning

confidence: 99%

Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups

Iwase

Enokibori

Yoshida

et al. 2021

Journal of Information Processing

View full text Add to dashboard Cite

Estimating sleeping postures with body joint positions is critical for identifying potential sleeping problems and the risk of pressure ulcers. Many methods have estimated postures with body joint positions from camera images for general purposes. However, visual monitoring of sleeping contexts suffers from privacy and occlusion issues due to blankets, pillows, etc. An approach to solve those issues is the use of body pressure images obtained from bed surfaces. We have developed a textile-based sheet-type pressure sensor to avoid such issues. Unfortunately, its use raises other issues that are absent from camera images such as low resolution and noise caused by the wrinkling of sensor sheets. In this paper, we extend DNN-based joint estimation, called Convolutional Pose Machine (CPM), using body area and posture estimation mashups to improve the accuracy of joint estimation. The following are our evaluation results with cross-validation with 16 joints in six sleeping postures of 12 subjects: 7.15 cm accuracy in mean absolute error (MAE), which is a 33.7% improvement from the standard CPM, and 8.52 cm accuracy in MAE, which is a 37.4% improvement from CPM with camera images in situations using a pillow and a blanket.

show abstract

“…A lot of research adopted pressure mat as their primary monitoring equipment for sleep posture recognition. In [20], the authors extracted 55 types of features from the raw data, which are collected from pressure mat, and then applied 4 classification algorithms for sleep posture estimation. In [21], the authors developed a sleep posture recognition algorithm by using limbs' characteristics.…”

Section: Non-wearable Sub-classmentioning

confidence: 99%

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Jeng¹,

Wang²,

Hu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Sleep postures monitoring systems in the hospital aim at transforming sensing signals into quantitative data to characterize the sleep behaviors of the patient. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost - a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve 90 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easily-wear wrist sensor can accurately quantify sleep postures.

show abstract

A study of automatic classification of sleeping position by a pressure-sensitive sensor

Cited by 30 publications

References 4 publications

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Joint Position Estimation for Body Pressure Images during Sleep: An Extension for CPM Using Body Area and Posture Estimation Mashups

A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach

Contact Info

Product

Resources

About