Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.
Pressure ulcers (PUs) result from localised injury to the skin and underlying tissue and usually occur over a bony prominence as a result of pressure, often in combination with shear forces. Both pressure magnitude and duration are thought to be key risk factors in the occurrence of PUs, thus exposing wheelchair-bound subjects to high risk of PU development. As a result, wheelchairs that incorporate tilt-in-space and recline functions are routinely prescribed to redistribute pressure away from their ischial tuberosities. The goal of this study was to analyse the role of full-body tilt and recline angles in governing sitting interface pressure and blood circulation parameters in elderly subjects and thereby investigate the efficacy of tilt-in-space wheelchairs for aiding pressure relief activity. Sitting interface pressure and ischial blood flow parameters were examined in 20 healthy elderly subjects while seated in a tilt-in-space and recline wheelchair. Five different angles of seat tilt (5°, 15°, 25°, 35°, and 45°) were assessed in combination with three different angles of backrest recline (5°, 15°, and 30°). The results of the study show that when compared to the upright reference posture, every position (except 15°T/5°R) resulted in a significant decrease in sitting interface pressure. Ischial blood flow also showed significant increases at four different positions (45°T/15°R, 15°T/30°R, 35°T/30°R, and 45°T/30°R) but only at larger tilt-in-space and recline angles. The results therefore suggest that small tilt-in-space and recline angles are indeed able to reduce sitting interface pressures, whereas changes in ischial blood flow only occur at larger angles. In the literature, cell deformation is thought to be dominant over tissue ischemia in the development of tissue necrosis and PUs. Therefore, together with our findings it can be concluded that frequently undertaking small adjustments in tilt-in-space and recline angle might be important for preventing cell deformation and any associated cell necrosis. Larger angles of tilt-in-space and recline seem to support blood flow returning to the tissues, which is likely to play a positive role in healing damaged tissue.
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