Clinical mobility tools have been shown to predict adverse outcomes in elderly patients, yet aren’t used often enough to inform hospital staff on patient health. Integrated computing has therefore become increasingly important and is predicted to improve traditional healthcare. This thesis details the design of an algorithmic system to partially automate a mobility tool. Three pressure sensitive mats were set-up on a hospital bed frame, underneath a mattress. Thirty volunteers enacted five movements on the hospital bed; each movement representative of a different mobility score. These
movements generated pressure data, and a system of algorithms was constructed in a decision tree to automatically classify data. The overall system yielded 96% accuracy, where the misclassifications were due largely to inconsistencies in volunteer performance. These results suggest that this algorithmic system is effective in distinguishing between the mobility enactments examined here, and emphasizes the potential for integrated computing to improve traditional healthcare.