A sitting posture recognition system based on 3 axis accelerometer

Ma, SangYong; Cho, Woo-Hyeong; Quan, Cheng-Hao; Lee, Sang‐Min

doi:10.1109/cibcb.2016.7758131

Cited by 37 publications

(28 citation statements)

References 6 publications

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“…On the one hand, the existing methods overlooked the understanding of scene relevance. The auxiliary-equipment-based methods [ 14 , 17 , 18 ] and the wearing-equipment-based methods [ 12 , 13 ] determined the unhealthy sitting postures with sensor devices such as pressure sensors and speed sensors. The vision-based methods [ 19 , 20 ] judged unhealthy sitting postures by visual information such as face information and skin color.…”

Section: Resultsmentioning

confidence: 99%

“…Mattmann et al [ 13 ] used a thermoplastic elastomer strain sensor to measure strain in clothes, and then identified different postures by Naïve Bayes classification. Ma et al [ 14 ] proposed a system that classified sitting posture using a 3-axis accelerometer and Support Vector Machine (SVM). Ma et al [ 15 ] proposed a cushion-based posture recognition system which was used to process pressure sensor signals for the detection of user’s posture in a wheelchair.…”

Section: Introductionmentioning

confidence: 99%

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A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

Min

Cui

Han

et al. 2018

Sensors

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Behavior analysis through posture recognition is an essential research in robotic systems. Sitting with unhealthy sitting posture for a long time seriously harms human health and may even lead to lumbar disease, cervical disease and myopia. Automatic vision-based detection of unhealthy sitting posture, as an example of posture detection in robotic systems, has become a hot research topic. However, the existing methods only focus on extracting features of human themselves and lack understanding relevancies among objects in the scene, and henceforth fail to recognize some types of unhealthy sitting postures in complicated environments. To alleviate these problems, a scene recognition and semantic analysis approach to unhealthy sitting posture detection in screen-reading is proposed in this paper. The key skeletal points of human body are detected and tracked with a Microsoft Kinect sensor. Meanwhile, a deep learning method, Faster R-CNN, is used in the scene recognition of our method to accurately detect objects and extract relevant features. Then our method performs semantic analysis through Gaussian-Mixture behavioral clustering for scene understanding. The relevant features in the scene and the skeletal features extracted from human are fused into the semantic features to discriminate various types of sitting postures. Experimental results demonstrated that our method accurately and effectively detected various types of unhealthy sitting postures in screen-reading and avoided error detection in complicated environments. Compared with the existing methods, our proposed method detected more types of unhealthy sitting postures including those that the existing methods could not detect. Our method can be potentially applied and integrated as a medical assistance in robotic systems of health care and treatment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

Min

Cui

Han

et al. 2018

Sensors

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“…28 Ma et al established a sitting posture recognition system based on triaxial accelerometers, transformed the acceleration data into feature vectors for component analysis, and used SVM and K-means clustering to classify sitting postures, and experimentally demonstrated the superiority of the SVM algorithm on sitting posture classification. 29 Arya and Kumar designed a home monitoring and assessment model for the activities of the elderly. 10 A back-propagation (BP) neural network-based model was designed using the triaxial accelerometer and pressure sensor data, and the validity of the model was experimentally demonstrated.…”

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

“…Wang et al proposed an adaptive neural control strategy by considering a quantization control approach, which is able to analyze the stability in time and eliminate the quantization error of a stochastic nonlinear system with finite time 28 . Ma et al established a sitting posture recognition system based on triaxial accelerometers, transformed the acceleration data into feature vectors for component analysis, and used SVM and K ‐means clustering to classify sitting postures, and experimentally demonstrated the superiority of the SVM algorithm on sitting posture classification 29 . Arya and Kumar designed a home monitoring and assessment model for the activities of the elderly 10 .…”

Section: Related Workmentioning

confidence: 99%

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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“…Based on our previous work [15] and the findings presented in [16,17], we defined 9 different sitting postures for further examination. Figure 10 shows the defined sitting postures.…”

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confidence: 99%

A Smart System for Sitting Posture Detection Based on Force Sensors and Mobile Application

Matúška

Paralic

Hudec

2020

Mobile Information Systems

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The employees’ health and well-being are an actual topic in our fast-moving world. Employers lose money when their employees suffer from different health problems and cannot work. The major problem is the spinal pain caused by the poor sitting posture on the office chair. This paper deals with the proposal and realization of the system for the detection of incorrect sitting positions. The smart chair has six flexible force sensors. The Internet of Things (IoT) node based on Arduino connects these sensors into the system. The system detects wrong seating positions and notifies the users. In advance, we develop a mobile application to receive those notifications. The user gets feedback about sitting posture and additional statistical data. We defined simple rules for processing the sensor data for recognizing wrong sitting postures. The data from smart chairs are collected by a private cloud solution from QNAP and are stored in the MongoDB database. We used the Node-RED application for the whole logic implementation.

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A sitting posture recognition system based on 3 axis accelerometer

Cited by 37 publications

References 6 publications

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

A Smart System for Sitting Posture Detection Based on Force Sensors and Mobile Application

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