IoT System for Real-Time Posture Asymmetry Detection

Mura, Monica La; Gregorio, Marco De; Lamberti, Patrizia; Tucci, Vincenzo

doi:10.3390/s23104830

Cited by 3 publications

(4 citation statements)

References 45 publications

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“…The sensor data was sent in real-time to a mobile application that alerted a user if an incorrect posture was being detected by displaying "green", "orange", and "red" for standard sitting, bad sitting, and heavy load on backbone, respectively. Other studies such as [53,55] similarly used IoT for bad posture detection and to provide valuable, real-time feedback to the end-user.…”

Section: Integration With the Internet Of Things (Iot)mentioning

confidence: 99%

“…The implementation of mobile applications was seen as the most used platform for alerting a user whenever an improper sitting posture was detected [27,36,43,54]. Another common method was the use of a desktop application, which was observed in some studies [40,45,48,53]. Ran et al [30], on the other hand, proposed the use of a haptic motor system that was integrated into a seating cushion and vibrated its motors whenever an incorrect sitting posture was detected.…”

Section: User Feedback Systemmentioning

confidence: 99%

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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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Section: Integration With the Internet Of Things (Iot)mentioning

confidence: 99%

Section: User Feedback Systemmentioning

confidence: 99%

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

Odesola,

Kulon,

Verghese

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Similarly Kundaliya et al [70] developed a smart sensing chair which used the Blynk 2.0 platform to stream the sensor data to the web. Other studies such as [64] and [66] similarly used IoT for bad postures detection as well as providing valuable real-time feedback to the end-user.…”

Section: Integration With (Internet Of Things) Iotmentioning

confidence: 99%

“…The implementation of mobile application was seen as the most used platform for alerting a user whenever an improper sitting posture is being detected [48,57,59,65,70]. Another common method was the use of a desktop application which was done by some studies [44,51,54,64]. Ran et al [49] on the other hand, proposed the use of a haptic motor system which was integrated into the seating cushion and would vibrate its motors whenever an incorrect sitting posture is being detected.…”

Section: User Feedback Systemmentioning

confidence: 99%

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

Odesola,

Kulon,

Verghese

et al. 2024

Preprint

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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. 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 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, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) 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 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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Intelligent systems for sitting posture monitoring and anomaly detection: an overview

Vermander,

Mancisidor,

Cabanes

et al. 2024

J NeuroEngineering Rehabil

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The number of people who need to use wheelchair for proper mobility is increasing. The integration of technology into these devices enables the simultaneous and objective assessment of posture, while also facilitating the concurrent monitoring of the functional status of wheelchair users. In this way, both the health personnel and the user can be provided with relevant information for the recovery process. This information can be used to carry out an early adaptation of the rehabilitation of patients, thus allowing to prevent further musculoskeletal problems, as well as risk situations such as ulcers or falls. Thus, a higher quality of life is promoted in affected individuals. As a result, this paper presents an orderly and organized analysis of the existing postural diagnosis systems for detecting sitting anomalies in the literature. This analysis can be divided into two parts that compose such postural diagnosis: on the one hand, the monitoring devices necessary for the collection of postural data and, on the other hand, the techniques used for anomaly detection. These anomaly detection techniques will be explained under two different approaches: the traditional generalized approach followed to date by most works, where anomalies are treated as incorrect postures, and a new individualized approach treating anomalies as changes with respect to the normal sitting pattern. In this way, the advantages, limitations and opportunities of the different techniques are analyzed. The main contribution of this overview paper is to synthesize and organize information, identify trends, and provide a comprehensive understanding of sitting posture diagnosis systems, offering researchers an accessible resource for navigating the current state of knowledge of this particular field.

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IoT System for Real-Time Posture Asymmetry Detection

Cited by 3 publications

References 45 publications

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

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

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

Intelligent systems for sitting posture monitoring and anomaly detection: an overview

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