Sitting posture recognition using screen printed large area pressure sensors

Ahmad, Jawad; Andersson, Henrik; Sidén, Johan

doi:10.1109/icsens.2017.8233944

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…As a summary of all the mentioned related literature and studies in Appendix A.1, the study was able to identify relevant gaps and useful methodology and implementations. Many studies (Estrada and Vea, 2017;Kappattanavar et al, 2020;Ahmad et al, 2017) used direct types of measurement (intrusive devices) that vary from the use of pressure sensors, accelerometer sensors, and notch sensors. Due to the availability of sensors, the captured feature points were limited from 1 up to 3 only, therefore it may result in to increase accuracy rate due to a smaller number of samples.…”

Section: Review Of Related Literature and Studiesmentioning

confidence: 99%

Computer Users Sitting Posture Classification Using Distinct Feature Points and Small Scale Convolutional Neural Network for Humana Computer Intelligent Interactive System During COVID-19

Estrada¹,

Vea²

2023

Journal of Computer Science

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The sudden change in our workplace practices from face-to-face work to work from home setup due to the pandemic has brought positive and negative impacts on our overall health. In literature, the use of deep learning and specialized cameras in the estimation of the human pose is popular even if there is a need for high computational resources and complex models. For this purpose, this study developed an intelligent and interactive system utilizing a human estimation model with the use of distinct keypoint such as thoracic, thoraco lumbar, and lumbar points in the spine. An objective type of a dataset captured in a work from home environment with the knowledge and guidance of Licensed Physical Therapists to assess proper and improper sitting posture was developed. The study developed and implemented a small-scale convolutional network and low-cost smartphone camera to recognize body key points. Once all the feature points' locations were extracted, additional features such as cosine similarity and point distances were calculated. Next, feature selection and optimization were utilized to classify proper and improper sitting postures. As a result, the study developed (2) datasets and (2) models with an accuracy of 85.18 and 92.07% and kappa of 0.691 and 0.838 respectively.

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Section: Review Of Related Literature and Studiesmentioning

confidence: 99%

Computer Users Sitting Posture Classification Using Distinct Feature Points and Small Scale Convolutional Neural Network for Humana Computer Intelligent Interactive System During COVID-19

Estrada¹,

Vea²

2023

Journal of Computer Science

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“…At present, there are three main ways of sitting posture recognition, which are based on machine vision [ 2 , 3 , 4 , 5 ], wearable motion sensors [ 6 , 7 , 8 , 9 ] and external pressure sensors [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Although machine vision technology has achieved great success in the field of posture recognition [ 26 ], it is difficult to work normally in situations with many obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…Roh et al [ 12 ] took the pressure values of four force measuring units as features and used support vector machine (SVM) to classify six types of sitting posture, obtaining 97.20% experimental classification accuracy. Ahmad et al [ 13 ] used a 4 × 4 matrix pressure sensor to classify four types of sitting postures by decision tree algorithm, obtaining about 80% experimental classification accuracy. Kim et al [ 24 ] used an 8 × 8 matrix pressure sensor and convolutional neural network algorithm to classify five types of sitting postures, obtaining 95.3% experimental classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Hip Positioning and Sitting Posture Recognition Based on Human Sitting Pressure Image

Wan

Zhao

et al. 2021

Sensors

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Bad sitting posture is harmful to human health. Intelligent sitting posture recognition algorithm can remind people to correct their sitting posture. In this paper, a sitting pressure image acquisition system was designed. With the system, we innovatively proposed a hip positioning algorithm based on hip templates. The average deviation of the algorithm for hip positioning is 1.306 pixels (the equivalent distance is 1.50 cm), and the proportion of the maximum positioning deviation less than three pixels is 94.1%. Statistics show that the algorithm works relatively well for different subjects. At the same time, the algorithm can not only effectively locate the hip position with a small rotation angle (0°–15°), but also has certain adaptability to the sitting posture with a medium rotation angle (15°–30°) or a large rotation angle (30°–45°). Using the hip positioning algorithm, the regional pressure values of the left hip, right hip and caudal vertebrae are effectively extracted as the features, and support vector machine (SVM) with polynomial kernel is used to classify the four types of sitting postures, with a classification accuracy of up to 89.6%.

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“…There are other systems in development for posture monitoring. For instance in [19], a flexible three layers printed pressure sensing system is proposed, classifying four leaning sitting angles (forward, backward, right and left). The top layer is screen printed to form conductive interdigital electrodes on a polyethylene terephthalate sheet, a sensing layer is printed on the bottom sheet for pressure sensing and an adhesive layer is used in the middle for spacing and adhesion.…”

Section: Introductionmentioning

confidence: 99%