Grünerbel, LorenzLorenzGrünerbelHeinrich, FerdinandFerdinandHeinrichDiebolder, DominikDominikDiebolderRichter, MartinMartinRichter2022-10-062022-10-062022https://publica.fraunhofer.de/handle/publica/42736810.1109/PerComWorkshops53856.2022.97672652-s2.0-85130590533The causes of pressure ulcers are well known: A high skin surface pressure over a long period of time due to insufficient relief of the tissue and a lack of alarm signals from the nervous system. A healthy person would move and relieve the skin after a while. A medical wearable can warn a patient without natural alarm signals if a high risk of a pressure wound is detected. Clinical data to find a pressure injury predictor with machine learning is not available, yet. This work presents a medical wearable that is able to measure pressure load at wound risk areas as well as the skin temperature and the blood oxygen saturation in close proximity of these areas. The functionality of the medical wearable is demonstrated by acquiring raw data of a healthy test person during seventeen nights. The necessary steps to transform this raw data into a machine learning data set are described. A multivariate subsequence clustering algorithm that is able to cluster data with the same temporal evolution, is presented. The medical wearable combined with the data analysis pipeline are well suited for a larger clinical study with patients that have a risk of pressure injuries.endecubitus prophylaxismachine learningmedical wearablemultivariate subsequence clusteringulcer predictionconference paper