Immobility in older patients is a costly problem for both patients and healthcare workers. The Hierarchical Assessment of Balance and Mobility (HABAM) is a clinical tool able to assess immobile patients and predict morbidity, yet could become more reliable and informative through automation. This paper proposes an algorithm to automatically determine which of three enacted HABAM scores (associated with bedridden patients) had been performed by volunteers. A laptop was used to gather pressure data from three mats placed on a standard hospital bed frame while five volunteers performed three enactments each. A system of algorithms was created, consisting of three subsystems. The first subsystem used mattress data to calculate individual sensor sums and eliminate the weight of the mattress. The second subsystem established a baseline pressure reading for each volunteer and used percentage change to identify and distinguish between two enactments. The third subsystem used calculated weight distribution ratios to determine if the data represented the remaining enactment. The system was tested for accuracy by inputting the volunteer data and recording the assessment output (a score per data set). The system identified 13 of 15 sets of volunteer data as expected. Examination of these results indicated that the two sets of data were not misidentified; rather, the volunteers had made mistakes in performance. These results suggest that this system of algorithms is effective in distinguishing between the three HABAM score enactments examined here, and emphasizes the potential for pervasive computing to improve traditional healthcare.