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

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.

3D posture visualisation from body shape measurements using physics simulation, to ascertain the orientation of the pelvis and femurs in a seated position.

BACKGROUND AND OBJECTIVE: The paper presents a novel technique for the visualisation and measurement of anthropometric features from patients with severe musculoskeletal conditions. During a routine postural assessment, healthcare professionals use anthropometric measurements to infer internal musculoskeletal configuration and inform the prescription of Custom Contoured Seating systems tailored to individual needs. Current assessment procedures are not only time consuming but also do not readily facilitate the communication of musculoskeletal configuration between healthcare professionals nor the quantitative comparison of changes over time. There are many techniques measuring musculoskeletal configurations such as MRI, CT or X-ray. However, most are very resource intensive and do not readily lend themselves to widespread use in, for example, community based services. Due to the low volume of patient data and hence small datasets modern machine learning techniques are also not feasible and a bespoke solution is required. METHODS: The technique outlined in this paper uses physics simulation to visualise the orientation of the pelvis and femurs when seated in a custom contoured cushion. The input to the algorithm is a body shape measurement and the output is a visualised pelvis and femurs. The algorithm was tested by also outputting a multi-label classification of posture (specific to the pelvis and femurs). RESULTS: The physics simulation has a classification accuracy of 72.9% when labelling all 9 features of the model; when considering 6 features (excluding rotations about the x-axis) the accuracy is increased to 92.8%. CONCLUSIONS: This study has shown that a mechanical shape sensor can be used to capture the unsupported seated posture of an individual during a clinic. The results have demonstrated the potential of the physics simulation to be used for anthropometric feature extraction from body shape measurements leading to a better posture visualization. Capturing and visualising the seated posture in this way should enable clinicians to more easily compare the effects of clinical interventions over time and document postural changes. Overall, the algorithm performed well, however, in order to fully evaluate its clinical benefit, it needs to be tested in the future using data from patients with severe musculoskeletal conditions and complex body shapes.

Rule-based algorithm for the classification of sitting postures in the sagittal plane from the Cardiff Body Match measurement system.

The purpose of this paper is to present the design and implementation of a novel rule-based algorithm for the classification of sitting postures in the sagittal plane. The research focused on individuals with severe musculoskeletal problems and, thus, specific requirements for posture and pressure management. Clients' body shapes were captured using the Cardiff Body Match system developed by the Rehabilitation Engineering Unit, Cardiff and Vale University Health Board. The algorithm consists of four main steps: the first step is the symmetry line detection, the second step involves the mathematical analysis of the curvature of the backrest profile, the third step is the sitting posture classification and the fourth step is the extraction of the geometric parameters from the curve. The results show the classification system was successful in identifying four types of curves characterizing sitting postures using local derivatives as curve descriptors with an overall accuracy of 93.9%.

Pelvis feature extraction and classification of Cardiff body match rig base measurements for input into a knowledge-based system.

The purpose of this paper is to determine whether it is possible to use an automated measurement tool to clinically classify clients who are wheelchair users with severe musculoskeletal deformities, replacing the current process which relies upon clinical engineers with advanced knowledge and skills. Clients' body shapes were captured using the Cardiff Body Match (CBM) Rig developed by the Rehabilitation Engineering Unit (REU) at Rookwood Hospital in Cardiff. A bespoke feature extraction algorithm was developed that estimates the position of external landmarks on clients' pelvises so that useful measurements can be obtained. The outputs of the feature extraction algorithms were compared to CBM measurements where the positions of the client's pelvis landmarks were known. The results show that using the extracted features facilitated classification. Qualitative analysis showed that the estimated positions of the landmark points were close enough to their actual positions to be useful to clinicians undertaking clinical assessments.