Sitting Posture Recognition based on data fusion on pressure cushion

Bao, Junrong; Li, Wenfeng; Li, Jian; Ge, Yanhong; Bao, Chongzhi

doi:10.11591/telkomnika.v11i4.2329

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…In one type, the sensor was attached to the seat only [26,28,29], and in the other type the sensor was attached to both the seat and backrest [9,[30][31][32]. In addition to the pressure sensor, Benocci et al [8] used additional sensors such as kinetic related and temperature sensors to classify the sitting postures.…”

Section: Sitting Posture Classificationmentioning

confidence: 99%

“…In the case of the classification of various other postures, the postures in which the legs are crossed were segmented (e.g., one leg over the other, one leg over the other with one foot on the other, one foot on the seat under the other leg's thigh) [32]. In addition, there were also cases wherein the movement pattern of the upper body and whether the user was seated were predicted [28,31]. Various methods have been adopted for classifying the sitting postures, and they include the use of PCA [32], Naïve Bayes classifier [10], hybrid cascade sitting posture classifier [30], SVM [29,33], kNN [9], dynamic time warping-based classification [26], and density-based methods [28].…”

Section: Sitting Posture Classificationmentioning

confidence: 99%

“…In addition, there were also cases wherein the movement pattern of the upper body and whether the user was seated were predicted [28,31]. Various methods have been adopted for classifying the sitting postures, and they include the use of PCA [32], Naïve Bayes classifier [10], hybrid cascade sitting posture classifier [30], SVM [29,33], kNN [9], dynamic time warping-based classification [26], and density-based methods [28]. Ma et al [30] have compared the classification performance obtained on applying DT, SVM, and multilayer perception.…”

Section: Sitting Posture Classificationmentioning

confidence: 99%

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Sitting Posture Classificationmentioning

confidence: 99%

Section: Sitting Posture Classificationmentioning

confidence: 99%

Section: Sitting Posture Classificationmentioning

confidence: 99%

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Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…Benocci et al [ 36 ] proposed a method using five pressure sensors and k-Nearest Neighbour (kNN) was used to classify six different postures. Bao et al [ 37 ] used a pressure cushion to recognize sitting postures by means of a density-based clustering method. Diego et al [ 38 ] proposed a non-invasive system for monitoring pressure changes and tilts during daily use of the wheelchair.…”

Section: Related Workmentioning

confidence: 99%

Posture Detection Based on Smart Cushion for Wheelchair Users

Gravina

et al. 2017

Sensors

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The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user’s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques—Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)—are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment.

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“…Sensing module is composed of four pressure sensor; processing module is used AVR single-International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013) © 2013. The authors -Published by Atlantis Press chip microprocessor; and transmitting module is collected raw pressure data to a central coordinator with Wifi[13] , depicted inFig.2 and Fig.3.…”

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confidence: 99%