In-bed posture classification using pressure sensor data and spiking neural network

Dam, Hoang Phuong; Pham, N.; Pham, Manh Hung; Doan, Ngoc Phu; Nguyen, Duc Minh; Nguyễn, Huy Hoàng

doi:10.1109/nics54270.2021.9701531

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…However, the 2.6% difference may not justify excluding this body position, since this position is a part of the four most common sleeping postures. Based As highlighted in Figure 1a,b, the study in [40] provided a maximum accuracy of 99.97% with 13 subjects and 3 body postures. The study in [31] examined cases where the prone position was included and excluded, resulting in accuracies of 97.1% and 99.7%, respectively.…”

Section: Body Position and Movement Monitoring Using A Smart Mat Or B...mentioning

confidence: 86%

“…The gray region shows the region of interest, which has large sample size and high accuracy values in Figure 1a or a high quantity of positions in Figure 1b. As highlighted in Figure 1a,b, the study in [40] provided a maximum accuracy of 99.97% with 13 subjects and 3 body postures. The study in [31] examined cases where the prone position was included and excluded, resulting in accuracies of 97.1% and 99.7%, respectively.…”

Section: Body Position and Movement Monitoring Using A Smart Mat Or B...mentioning

confidence: 87%

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Recent Advances and Applications of Textile Technology in Patient Monitoring

Stern,

Roshan Fekr

2023

Healthcare

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Sleep monitoring has become a prevalent area of research where body position and physiological data, such as heart rate and respiratory rate, are monitored. Numerous critical health problems are associated with poor sleep, such as pressure sore development, sleep disorders, and low sleep quality, which can lead to an increased risk of falls, cardiovascular diseases, and obesity. Current monitoring systems can be costly, laborious, and taxing on hospital resources. This paper reviews the most recent solutions for contactless textile technology in the form of bed sheets or mats to monitor body positions, vital signs, and sleep, both commercially and in the literature. This paper is organized into four categories: body position and movement monitoring, physiological monitoring, sleep monitoring, and commercial products. A detailed performance evaluation was carried out, considering the detection accuracy as well as the sensor types and algorithms used. The areas that need further research and the challenges for each category are discussed in detail.

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Section: Body Position and Movement Monitoring Using A Smart Mat Or B...mentioning

confidence: 86%

Section: Body Position and Movement Monitoring Using A Smart Mat Or B...mentioning

confidence: 87%

Recent Advances and Applications of Textile Technology in Patient Monitoring

Stern,

Roshan Fekr

2023

Healthcare

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“…This dataset can be used for assessing the viability of sensors placed under a mattress, and all its layers can be used for classification algorithms similar to the work researchers conducted in [2][3][4][5], as well as health studies related to pressure ulcers [6], sleep disorders [3], and fall prevention, among others.…”

Section: Discussionmentioning

confidence: 99%

PoPu-Data: A Multilayered, Simultaneously Collected Lying Position Dataset

Fonseca,

Ribeiro,

Metrôlho

et al. 2023

Data

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This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets—one placed over and one placed under a mattress; furthermore, a segmentation data layer was added where different body parts are identified using the pressure data from the sensors over the mattress. The data included were gathered from 60 healthy volunteers distributed among the different gathered characteristics: namely sex, weight, and height. This dataset can be used for position classification, assessing the viability of sensors placed under a mattress, and in applications regarding bedded or lying people or sleep related disorders.

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“…In-bed posture recognition plays a vital role in sleep studies [1]. Indeed, doctors can diagnose esophagus problems earlier by monitoring a patient's sleeping positions for a period.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2022

JST: SSAD

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Many chronic diseases, such as cardiovascular disease and sleep disorders, can be diagnosed and treated by assessing the sleeping quality. Sleep posture recognition is an important part of determining sleep quality in sleep research. While most recent studies focus on the classification problem with the number of sleeping postures being equal to or less than ten, this study aims to achieve state-of-the-art results on 17 in-bed position classification. To do this, a spatial pyramid pooling module was added to the top of the EfficientNet B0 model and the contrastive loss and cross-entropy loss functions are combined to act as the main loss function. The random 10% salt and pepper noise are utilized to augment the training data. Experimental results confirm that the proposed approach achieves the best accuracy of 96.01% and outperforms the existing methods. Additionally, we also provide an estimation related to the impact of various combinations of backbone models and loss functions on the performance of the classifier.

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In-bed posture classification using pressure sensor data and spiking neural network

Cited by 9 publications

References 23 publications

Recent Advances and Applications of Textile Technology in Patient Monitoring

Recent Advances and Applications of Textile Technology in Patient Monitoring

PoPu-Data: A Multilayered, Simultaneously Collected Lying Position Dataset

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