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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.