A Real-Time Patient-Specific Sleeping Posture Recognition System Using Pressure Sensitive Conductive Sheet and Transfer Learning

Hu, Qisong; Tang, Xiaochen; Tang, Wei

doi:10.1109/jsen.2020.3043416

Cited by 33 publications

(30 citation statements)

References 37 publications

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“…The system may be combined with weather systems to have an early preparation of weather changes such as heavy rains or high temperatures. Furthermore, the drinking behavior data such as visit time and visit duration of the domestic animal could be applied to train machine learning algorithms to identify the abnormal features of animal health status [47][48][49][50][51]. Early detection and intervention could help save costs related to animal health issues.…”

Section: Discussionmentioning

confidence: 99%

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Tang

Biglari

Ebarb

et al. 2021

Sensors

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This paper presents a water intake monitoring system for animal agriculture that tracks individual animal watering behavior, water quality, and water consumption. The system is deployed in an outdoor environment to reach remote areas. The proposed system integrates motion detectors, cameras, water level sensors, flow meters, Radio-Frequency Identification (RFID) systems, and water temperature sensors. The data collection and control are performed using Arduino microcontrollers with custom-designed circuit boards. The data associated with each drinking event are water consumption, water temperature, drinking duration, animal identification, and pictures. The data and pictures are automatically stored on Secure Digital (SD) cards. The prototypes are deployed in a remote grazing site located in Tucumcari, New Mexico, USA. The system can be used to perform water consumption and watering behavior studies of both domestic animals and wild animals. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch.

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Section: Discussionmentioning

confidence: 99%

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Tang

Biglari

Ebarb

et al. 2021

Sensors

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“…The sleeping postures considered in various levels include supine, prone, log, fetal, and under four blankets, and the participants include 40 men and 26 women. Image processing and feature addition are done using affine transformation and data fusion techniques thereby enhancing the collected blanket conditions without modifying the collected data [ 10 ]. The log and fetal postures were merged into the side-lying posture, and the prone posture was pooled in coarse classification.…”

Section: Literature Surveymentioning

confidence: 99%

“…Such a system can be used for better heartbeat and sleep monitoring and with body posture detection [ 9 ]. Ultrawideband radar s/m is used for the classification of human sleep postures, where a multiview learning model sleep sent with time-series data augmentation is used for data classification [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Posture Prediction-Bayesian Network Algorithm for Sleep Posture Recognition in Wireless Body Area Networks

Roshini

Kiran

2022

International Journal of Telemedicine and Applications

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Wireless body area networks have taken their unique recognition in providing consistent facilities in health monitoring. Several studies influence physiological signal monitoring through a centralized approach using star topology in regular activities like standing, walking, sitting, and running which are considered active postures. Unlike regular activities like walking, standing, sitting, and running, the in-bed sleep posture monitoring of a subject is highly necessary for those who have undergone surgery, victims of breathing problems, and victims of COVID-19 for whom oxygen imbalance is a major issue as the mortality rate in sleep is high due to unattended patients. Suggestions from the medical field state that the patients with the above-mentioned issues are highly suggested to follow the prone sleep posture that enables them to maintain the oxygen level in the human body. A distributed model of communication is used where mesh topology is used for the data packets to be carried in a relay fashion to the sink. Heartbeat rate (HBR) and image monitoring of the subject during sleep are closely monitored and taken as input to the proposed posture prediction-Bayesian network (PP-BN) to predict the consecutive postures to increase the accuracy rate of posture recognition. The accuracy rate of the model outperforms the existing classification and prediction algorithms which take the cleaned dataset as input for better prediction results.

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“…To avoid the inaccuracy of these methods, several technological advances have made it possible to perform gait analysis through specialized equipment. Previous studies, for example, have consisted on the use of walkways or wearable sensors [4,5] to obtain important features and perform gait analysis [6,7]. Still, although these techniques provide a rich quantitative examination that can produce relevant data not observed by the eye, they are generally considered impractical, as they often require costly additional equipment that can be inconvenient to both the examiners and the patient [2,8,9].…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data

Chavez

Tang

2022

Sensors

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Parkinson’s disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96–99% accuracy in evaluating the prognosis of Parkinsonian gaits.

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A Real-Time Patient-Specific Sleeping Posture Recognition System Using Pressure Sensitive Conductive Sheet and Transfer Learning

Cited by 33 publications

References 37 publications

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

An Enhanced Posture Prediction-Bayesian Network Algorithm for Sleep Posture Recognition in Wireless Body Area Networks

A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data

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