Posture transitions identification based on a triaxial accelerometer and a barometer sensor

Abstract

Posture transitions (PT) are important movements among the activities performed in daily life of older adults. Their analysis provides information related to the amount of activity performed by a patient over a day and, furthermore, they are useful for assessing symptoms in some movement disorders such as Parkinson’s disease. Many research works have attempted to automatically identify PT relying on the use of machine learning algorithms and light and small accelerometers, since they might be embedded into wearable systems, being unobtrusive for the users. However, distinguishing PTs through a single sensor results in complex classifiers requiring high computational resources, since some PT (such as Stand-to-Sit and Sit-to-Stand PT) may provide very similar acceleration signals. In this paper, we propose a barometer sensor with the aim of complementing the information provided by accelerometers. In addition, a hierarchical algorithm is presented, which is based on Support Vector Machines to detect PT including falls and Lying-to-Stand PT through a single sensor device. Results in 14 users show that the use of a barometer sensor enables the hierarchical algorithm to distinguish Sit-to-Stand from Stand-to-Sit transitions, and Falls from Lying-to-Stand with accuracies over 99%.