Multimodal Wearable Sensing for Fine-Grained Activity Recognition in Healthcare

State-of-the-art in-home activity recognition schemes with wearable devices are mostly capable of detecting coarse-grained activities (sitting, standing, walking, or lying down), but can´t distinguish complex activities (sitting on the floor versus the sofa or bed). Such schemes often aren´t effective for emerging critical healthcare applications -- for example, in remote monitoring of patients with Alzheimer´s disease, bulimia, or anorexia -- because they require a more comprehensive, contextual, and fine-grained recognition of complex daily user activities. Here, a novel approach for in-home, fine-grained activity recognition uses multimodal wearable sensors on multiple body positions, along with lightly deployed Bluetooth beacons in the environment. In particular, this solution exploits measuring user´s ambient environment and location context with wearable sensing and Bluetooth beacons, along with user movement captured with accelerometer and gyroscope sensors. The proposed algorithm is a two-level supervised classifier with both levels running on a server. In the first level, multisensor data from wearables on each body position are collected and analyzed using the proposed modified conditional random field (CRF)-based supervised activity classifier. The classified activity state from each of the wearables data are then fused for deciding the user´s final activity state. Preliminary experimental results are presented on the classification of 19 complex daily activities of a user at home.