Objective assessment of physical activity and sedentary time of older adults using ambient and wearable sensor technologies

This paper reports the description of a multi-sensor platform able to automatically assess the level of physical activity and sedentary time of older adults. The platform has a hierarchical network topology, compound by N detector nodes managing several ambient sensor nodes and one detector node that manages a wearable sensor node. The system provides also one coordinator node that receives high-level reports from detector nodes. The idea of using heterogeneous sensors is motivated by the fact that in this way we expands the number of end-users, as they may accept only a type of sensor technology. The objective assessment was conducted through two main algorithmic steps: (1) recognition of well-defined set of human activities, detected by a 3D vision sensor (ambient node) and a smart garment (wearable sensor node), and (2) estimation of a physiological measure, that is (MET)-minutes. Results obtained in terms of activity recognition (and subsequent physical activity/sedentary time assessment) showed that the integrated version of the platform performs better than each single sensor technology with an overall accuracy obtained using simultaneously data provided from both sensory technologies that is about 5% higher of single sub-system, thus confirming the advantage in using a coordinator node. Finally, an added value of this work is the capability of the platform in providing a sensing invariant interface (i.e., abstracted from any specific sensing technology), since the use of the activities enables the integration of a wide set of devices, providing that they are able to reproduce the same set of features.