Posture Prediction for Healthy Sitting Using a Smart Chair

Gelaw, Tariku Adane; Hagos, Misgina Tsighe

doi:10.1007/978-3-030-93709-6_26

Cited by 11 publications

(4 citation statements)

References 10 publications

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“…However, it is not popular for human posture estimation based on interaction forces. Raw data [51], statistics [50,52], sliding window [40,50], HOG [39,46] [14, 16,22,34,[39][40][41]46,[50][51][52][53][54][55][56][57][58][59][60] K-nearest neighbor (kNN) 78% [34]-98.52% [22] Raw data [61,62], statistics [50], sliding window [50] [ 13,22,34,40,41,47,50,54,55,[61][62][63][64][65] used for regression: [12] Convolutional Neural Networks (CNNs) The classification accuracies reach values above 90% for the most used algorithms (see Table 3). Data pre-processing is used in most of the studies to improve the quality of the data input to the algorithms.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Helmstetter,

Matthiesen

2023

Sensors

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Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Helmstetter,

Matthiesen

2023

Sensors

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“…Tariku Adane Gelaw [11] propose a sitting posture monitoring system. The system consists of a chair equipped with 32 by 32 pressure sensors placed on the seat and the backrest.…”

Section: Related Workmentioning

confidence: 99%

SPLIGN: Smart-belt Posture Monitoring System Based on AI-algorithms for Sitting Persons

Tlili,

Haddad,

Bouallegue

2024

IJAEMR

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The back pain is the main common health problems on this decade because of the sitting for a long time tilted on the computer for working, studying or playing. The back problems are widely spread for different ages from young to old people. Last medical research demonstrates that sitting with the posture align can prevent and remedy many spine problems. In this paper, we propose ’SPLIGN’ which a posture monitoring system designed to help maintaining the good posture during sitting. The SPLIGN is a smart belt equipped with inertial sensors. A mobile and web applications are developed for monitoring and remind the user to correct posture. The proposed system is based on a detailed study of the machine learning algorithms in order to choose the best accurate algorithm for posture prediction. The main studied algorithms are Convolutional neural network (CNN), The K-nearest Neighbors (KNN), Support-vector machines (SVM), Decision tree classification, Random forest, Naive Bayes Classifier and Boosting algorithm. The test results demonstrate that The Random Forest algorithm has the best accuracy 99.67% compared to the other algorithms with appropriate processing time 67.7 ms for real time posture monitoring system.

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“…Deep learning models [81], characterized by their multi-layered neural network structure that includes an input layer, several hidden layers, and an output layer, have been extensively employed in the classification of sitting postures in smart sensing chairs. Research has primarily focused on utilizing Convolutional Neural Networks (CNNs) [41,44,45,49,54], Artificial Neural Networks (ANNs) [25,30,34,36,42,47], Spiking Neural Networks (SNNs) [48], and Deep Neural Networks (DNNs) [59] for this purpose. Both CNNs and ANNs have emerged as particularly popular choices due to their robustness in handling complex pattern recognition tasks.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review

Odesola,

Kulon,

Verghese

et al. 2024

Sensors

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Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

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Posture Prediction for Healthy Sitting Using a Smart Chair

Cited by 11 publications

References 10 publications

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

SPLIGN: Smart-belt Posture Monitoring System Based on AI-algorithms for Sitting Persons

Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review

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