Pressure signal feature extraction for the differentiation of clinical mobility assessments

While clinical measures of mobility and balance are important for tracking disease progression in the elderly, most of these tools are based on what can be observed by the human eye, and many do not assess bedridden patients. This paper examines the potential for pressure sensitive mats to be used in conjunction with data processing to develop a system that automates a clinical tool used to assess balance and mobility in the elderly. A study was conducted in which pressure data were gathered while 30 non-patient volunteers performed partial in-bed clinical assessments. Data were then analyzed by grouping sensor data, calculating ratios, then extracting features from the analyzed signals. Pressure ratio signals representing each part of the simulated assessment, were consistent among volunteers and were visually and numerically distinguishable from another. These results indicate that the movement specific pressure signal features identified here are quantifiable and that algorithms may be written to identify and distinguish between certain movements and output the correct clinical assessment.