Posture classification of lying down human bodies based on pressure sensors array

Cruz-Santos, William; Beltrán-Herrera, Alberto; Vazquez-Santacruz, Eduardo; Gamboa-Zúñiga, Mariano

doi:10.1109/ijcnn.2014.6889886

Cited by 10 publications

(13 citation statements)

References 16 publications

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“…People have proposed different approaches for in-bed posture detection or full-body pose estimation. These methods can be categorized into two main groups: (1) posture detection based on feature descriptors, such as [ 11 , 12 , 14 , 15 ]; (2) pose estimation based on skeleton methods [ 16 , 17 , 20 , 21 , 23 ]. In this work we classify the posture into supine and left and right lateral.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Ostadabbas et al [ 13 ] used Gaussian mixture model (GMM)-based clustering approaches for posture classification and limb identification. Some researchers used descriptors such as histogram of oriented gradients (HOG) and scale invariant feature transform (SIFT) with support vector machines (SVM) and other classifiers to classify the in-bed postures [ 14 , 15 ]. Skeletonization-based pose estimation is another technique used by [ 16 , 17 ].…”

Section: Related Workmentioning

confidence: 99%

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Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

Hajari

Lastre-Dominguez

et al. 2021

Sensors

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Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient’s in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person’s pose, and locate and track high-risk regions over time so that necessary attention can be provided.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

Hajari

Lastre-Dominguez

et al. 2021

Sensors

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“…Also, a video camera is unacceptable for the elderly because of privacy concerns. As a result, a noncontact sensing device is a proper approach for continuously monitoring elderly activity [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. There are some reports of using an ultrasonic sensor, air pressure sensor, and vibration sensor [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…To prevent bedsores, the duration of the same position of lying can be observed by monitoring movement. Some previous studies used commercial pressure mat systems to detect the bed position [13][14][15][16][17][18][19][20][21]. However, their proposed pressure mat systems need a large number of sensors which are not practical and are costly in actual practice.…”

Section: Introductionmentioning

confidence: 99%

Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

Viriyavit

Sornlertlamvanich

2020

Journal of Sensors

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Falls from a bed often occur when an elderly patient attempts to get out of bed or comes close to the edge of a bed. These mishaps have a high possibility of serious injuries, such as bruises, soreness, and bone fractures. Moreover, a lack of repositioning the body of a bedridden elderly person may cause bedsores. To avoid such a risk, a continuous activity monitoring system is needed for taking care of the elderly. In this study, we propose a bed position classification method based on the sensor signals collected from only four sensors that are embedded in a panel (composed of two piezoelectric sensors and two pressure sensors). It is installed under the mattress on the bed. The bed positions considered are classified into five different classes, i.e., off-bed, sitting, lying center, lying left, and lying right. To collect the training dataset, three elderly patients were asked for consent to participate in the experiment. In our approach, a neural network combined with a Bayesian network is adopted to classify the bed positions and put a constraint on the possible sequences of the bed positions. The results from both the neural network and Bayesian network are combined by the weighted arithmetic mean. The experimental results have a maximum accuracy of position classification of 97.06% when the proportion of coefficients for the neural network and the Bayesian network is 0.3 and 0.7, respectively.

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“…Intelligent sensors that detect forces beneath the skin have achieved flexible and safe HRCs [18,19]. Pressure sensors that recognize various human motion postures-sitting, standing, and lying-have been processed into pressure arrays and embedded in cushions, carpets, and mattresses [20][21][22][23]. Human-based assembly operations have also been monitored through machine learning [24].…”

Section: Introductionmentioning

confidence: 99%

Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory

Guan

Liu

Cai

et al. 2020

Sensors

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During human–robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The pressure data of nine standing postures were obtained from each participant. The human standing postures were discriminated by seven classification algorithms. The results of the best three algorithms were fused using the Dempster–Shafer evidence theory to improve the accuracy and robustness. In a cross-validation test, the best method achieved an average accuracy of 99.96%. The convolutional neural network classifier and data-fusion algorithm can feasibly classify the standing postures of human workers.

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Posture classification of lying down human bodies based on pressure sensors array

Cited by 10 publications

References 16 publications

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring

Bed Position Classification by a Neural Network and Bayesian Network Using Noninvasive Sensors for Fall Prevention

Standing-Posture Recognition in Human–Robot Collaboration Based on Deep Learning and the Dempster–Shafer Evidence Theory

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