Sleep Posture Classification Using Bed Sensor Data and Neural Networks

Enayati, Moein; Skubic, Marjorie; Keller, James M.; Popescu, Mihail; Farahani, Nasibeh Zanjirani

doi:10.1109/embc.2018.8512436

Cited by 42 publications

(23 citation statements)

References 16 publications

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“…Our in-home sensor system automatically generates health messages that indicate early signs of health change, thereby allowing for very early treatment, which has been shown to produce better health outcomes [20,21]. Several parameters are tracked currently, including in-home gait patterns [58], overall activity level [58][59][60], heart rate and respiration rate [13,61], blood pressure [18], and sleep patterns [62,63]. The proposed model will be used to enhance the data analysis systems for in-home sensors and improve the accuracy in the detection of deteriorating health conditions, in a continuing effort to provide better healthcare options for our aging population.…”

Section: Discussionmentioning

confidence: 99%

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Guidoboni

Sala

Enayati

et al. 2019

IEEE Trans. Biomed. Eng.

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Objective: to develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: a closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: the proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: this work provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present study considers an average human body and can potentially be extended to include variability among individuals.

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Section: Discussionmentioning

confidence: 99%

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Guidoboni

Sala

Enayati

et al. 2019

IEEE Trans. Biomed. Eng.

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“…The articles that were included in our analysis were published between 2007 and 2020 and were undertaken in the United States [25][26][27][28][29][30][31][32][33][34][35], China [36][37][38][39][40][41][42][43][44], Spain [45][46][47][48][49][50], Japan [51,52], Italy [53,54], Korea [55], and Greece [56]. According to the applied area of the included studies, we divided the articles into three components: predictive model (12 studies), posture recognition (11 studies), and image analysis (9 studies).…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Jiang¹,

Ma²,

Guo³

et al. 2021

JMIR Med Inform

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Background Pressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. Objective The objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. Methods We conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results A total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. Conclusions There is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

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“…[ 24 ], the authors developed a sleep posture recognition algorithm by using limbs’ characteristics. Furthermore, [ 25 ] adopted hydraulic sensors to substitute pressure sensors for improving the convenience of the sleep monitoring system. Ref.…”

Section: Related Workmentioning

confidence: 99%

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

Jeng

Wang

et al. 2021

Sensors

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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.

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Sleep Posture Classification Using Bed Sensor Data and Neural Networks

Cited by 42 publications

References 16 publications

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Cardiovascular Function and Ballistocardiogram: A Relationship Interpreted via Mathematical Modeling

Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

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

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