Development of Distraction Limit Estimation Index Using Posture Change Monitoring System

Noh, Yun-Hong; Seo, Ji-Yun; Jeong, Do-Un

doi:10.1007/978-981-13-1056-0_3

Cited by 2 publications

(7 citation statements)

References 3 publications

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“…In some papers [ 14 , 15 , 19 , 21 , 23 , 24 ], sensors were sparsely placed, varying from 7 to 17. In previous studies [ 15 , 19 , 23 ], the sensors were placed such that the ischial tuberosity, the thigh region, the lumbar region of the spine, and the scapula had better contact with the sensors.…”

Section: Resultsmentioning

confidence: 99%

“…These placements were based on the classes and features extracted from the pressure sensors. A total of 7 studies used commercial sensors in their experiments [ 1 , 14 - 16 , 20 , 21 , 24 ]. These commercial pressure sensors were either from Tekscan, Interlink, Sensomative, or Honeywell.…”

Section: Resultsmentioning

confidence: 99%

“…Support vector machines [ 17 , 21 , 30 ] and random forest (RF) [ 1 , 14 , 16 ] were the second most used models. Furthermore, algorithms such as K-means [ 30 ], multimodal regression [ 14 ], boosting [ 14 ], dynamic time warping [ 13 ], k-nearest neighbors [ 20 ], sliced inverse regression (SIR) [ 20 ], decision tree [ 23 ], Naive Bayes [ 21 ], SimpleLogistic [ 21 ], linear discriminant analysis [ 20 ], principal component analysis (PCA) [ 20 ], and triangle center [ 24 ] were deployed in the papers to classify sitting postures. In this study, 6 papers [ 14 , 15 , 18 , 20 , 21 , 30 ] compared the performance of classification algorithms using more than one classifier.…”

Section: Resultsmentioning

confidence: 99%

“…Wang et al [ 23 ] used the average and SD of the pressure of the sensors as features. Noh et al [ 24 ] used the distance between the center points, intensity, and frequency size of the pressure sensor movements between the current frame center and the previous to train the algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…They can assess and classify sitting postures more objectively and accurately. In the last decade, studies have used miniaturized pressure sensors made from air bladders, piezoelectric materials, fibers coated with yarn materials, force sensors, and force-sensing resistors in the form of cushions, sensor array sheets, and mats or just as individual sensors to provide the necessary signals to classify sitting postures [ 1 , 13 - 24 ]. Such classifications should also preferably include limb movements, as these are suspected to be associated with musculoskeletal discomfort and pain [ 25 - 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Kappattanavar¹,

Steckhan²,

Sachs³

et al. 2021

JMIR Biomed Eng

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Background A majority of employees in the industrial world spend most of their working time in a seated position. Monitoring sitting postures can provide insights into the underlying causes of occupational discomforts such as low back pain. Objective This study focuses on the technologies and algorithms used to classify sitting postures on a chair with respect to spine and limb movements. Methods A total of three electronic literature databases were surveyed to identify studies classifying sitting postures in adults. Quality appraisal was performed to extract critical details and assess biases in the shortlisted papers. Results A total of 14 papers were shortlisted from 952 papers obtained after a systematic search. The majority of the studies used pressure sensors to measure sitting postures, whereas neural networks were the most frequently used approaches for classification tasks in this context. Only 2 studies were performed in a free-living environment. Most studies presented ethical and methodological shortcomings. Moreover, the findings indicate that the strategic placement of sensors can lead to better performance and lower costs. Conclusions The included studies differed in various aspects of design and analysis. The majority of studies were rated as medium quality according to our assessment. Our study suggests that future work for posture classification can benefit from using inertial measurement unit sensors, since they make it possible to differentiate among spine movements and similar postures, considering transitional movements between postures, and using three-dimensional cameras to annotate the data for ground truth. Finally, comparing such studies is challenging, as there are no standard definitions of sitting postures that could be used for classification. In addition, this study identifies five basic sitting postures along with different combinations of limb and spine movements to help guide future research efforts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Kappattanavar¹,

Steckhan²,

Sachs³

et al. 2021

JMIR Biomed Eng

View full text Add to dashboard Cite

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Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review (Preprint)

Kappattanavar¹,

Steckhan²,

Sachs³

et al. 2020

Preprint

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BACKGROUND Background: Prolonged sitting postures have been reported to increase the probability of developing low back pain. Moreover, the majority of employees in the industrial world work ninety percent of their time in a seated position. OBJECTIVE This review focuses on the technologies and algorithms that have been used to classify seating postures on a chair with respect to spine and limb movements. METHODS Three electronic literature databases have been surveyed to identify the studies classifying sitting posture in adults. Fourteen articles have been finally shortlisted. These articles were categorized into low, medium, and high quality. Most of the articles were categorized as medium quality (12/14). RESULTS The majority of the studies used pressure sensors (13/14) to classify sitting postures. Neural Networks were the most frequently (6/14) used approaches for classifying sitting postures. CONCLUSIONS Based on the current study the classification of sitting posture is still in the nascent stage and hence, we would suggest personalized sitting posture analysis. Furthermore, the review emphasizes identifying at least five basic postures along with different limb and spine movements in a free-living environment. It is essential to annotate the data set with ground truths for subsequent training of the classifier to solve the sitting posture classification problem.

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Development of Distraction Limit Estimation Index Using Posture Change Monitoring System

Cited by 2 publications

References 3 publications

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review

Monitoring of Sitting Postures With Sensor Networks in Controlled and Free-living Environments: Systematic Review (Preprint)

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