Prolonged sitting detection for office workers syndrome prevention using kinect

Paliyawan, Pujana; Nukoolkit, Chakarida; Mongkolnam, Pornchai

doi:10.1109/ecticon.2014.6839785

Cited by 23 publications

(22 citation statements)

References 10 publications

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“…Therefore, to make the classification more effective, the amount of usage time should also be considered. According to the recommendation for avoiding office syndrome , making a movement every 30 min could prevent severe pain in the back and neck. We make use of this recommendation in our classification by dividing the 30‐min length into five periods, each period lasting 7.5 min.…”

Section: Methodsmentioning

confidence: 99%

Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept

Lawanont

Inoue

Mongkolnam

et al. 2018

IEEJ Transactions Elec Engng

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Since the beginning of the smartphone era, the number of smartphone users has increased dramatically. Consequently, several aspects of their use have provoked concerns from researchers. From a health-related perspective, text neck syndrome is a consequence of excessive smartphone use. Therefore, we introduce a neck posture monitoring system to help prevent text neck syndrome in smartphone users. The system operates on smartphones using the embedded rotation vector sensors and the camera for image detection. Data from the sensors and camera are used to calculate the neck angle. The results of an experiment show that the calculation algorithm is not significantly different from the photogrammetric method, which was proposed earlier as a method to measure the neck angle from a side view image. In summary, we propose a system that includes the detection and classification of the user's neck posture to raise awareness and help promote better posture to prevent text neck syndrome among smartphone users.

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Section: Methodsmentioning

confidence: 99%

Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept

Lawanont

Inoue

Mongkolnam

et al. 2018

IEEJ Transactions Elec Engng

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“…In 2014, Paliyawan et al [17] presented a system for monitoring office workers in order to prevent Office Workers Syndrome using Kinect camera. The system can alert a user when it is time to relax and provide a daily summary report used to track working behavior of the user.…”

Section: B Gestural and Postural Detectionmentioning

confidence: 99%

“…2) Decision Tree D-tree is used to classify data from class label, which yields output as a flowchart-like tree structure [14,17,[22][23]. The J48 is used in our work to classify the data as a set of decision nodes and leaf nodes.…”

Section: A Classification Models 1) Neural Networkmentioning

confidence: 99%

Postural classification using Kinect

Visutarrom

Mongkolnam

Chan

2014

2014 International Computer Science and Engineering Conference (ICSEC)

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This research focuses on the comparison of posture recognition, using a data mining classification approach on the skeleton data stream obtained from Kinect camera. We classified four standard postures including Stand, Sit, Sit on floor and Lie Down. We compared six classifiers, namely, decision tree, neural network, naïve Bayes, support vector machine, logistic regression and random forest in order to find a suitable classifier. Our best results can correctly classify the postures with 97.88% accuracy, 97.40% sensitivity, and 0.991 ROC area under curve using Max-Min normalization with a decision tree classifier on four transformed attributes. Our future work will use the knowledge obtained to classify a wider range of postures of the elderly while watching television, to be a part of a bigger effort to monitor and study elderly behavior at home.

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“…However, this method is sensitive to light contrast and its use is largely restricted to office environments. Paliyawan et al [ 17 ] focused on the detection of sitting posture for office workers by performing data mining classification on the real-time skeleton data stream captured by a single Kinect camera. This complex and relatively expensive system could effectively monitor the user’s posture with an accuracy of 98%.…”

Section: Introductionmentioning

confidence: 99%

Inverse Piezoresistive Nanocomposite Sensors for Identifying Human Sitting Posture

Zhang

Bowden

Zhang

et al. 2018

Sensors

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Sitting posture is the position in which one holds his/her body upright against gravity while sitting. Poor sitting posture is regarded as an aggravating factor for various diseases. In this paper, we present an inverse piezoresistive nanocomposite sensor, and related deciphering neural network, as a new tool to identify human sitting postures accurately. As a low power consumption device, the proposed tool has simple structure, and is easy to use. The strain gauge is attached to the back of the user to acquire sitting data. A three-layer BP neural network is employed to distinguish normal sitting posture, slight hunchback and severe hunchback according to the acquired data. Experimental results show that our method is both realizable and effective, achieving 98.75% posture identification accuracy. This successful application of inverse piezoresistive nanocomposite sensors reveals that the method could potentially be used for monitoring of diverse physiological parameters in the future.

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Prolonged sitting detection for office workers syndrome prevention using kinect

Cited by 23 publications

References 10 publications

Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept

Neck posture monitoring system based on image detection and smartphone sensors using the prolonged usage classification concept

Postural classification using Kinect

Inverse Piezoresistive Nanocomposite Sensors for Identifying Human Sitting Posture

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