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 on the sofa or bed). Such schemes 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.