A-Wristocracy: Deep Learning on Wrist-Worn Sensing for Recognition of User Complex Activities

In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex in-home activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily Living) but not large number of fine-grained and complex IADLs (Instrumental Activities of Daily Living). These are also not able to distinguish similar activities but with different context (such as sit on floor vs. sit on bed vs. sit on sofa). Our solution helps accurate detection of in-home ADLs/ IADLs and contextual activities, which are all critically important for remote elderly care in tracking their physical and cognitive capabilities. A-Wristocracy makes it feasible to classify large number of fine-grained and complex activities, through Deep Learning based data analytics and exploiting multi-modal sensing on wrist-worn device. It exploits minimal functionality from very light additional infrastructure (through only few Bluetooth beacons), for coarse level location context. A-Wristocracy preserves direct user privacy by excluding camera/ video imaging on wearable or infrastructure. The classification procedure consists of practical feature set extraction from multi-modal wearable sensor suites, followed by Deep Learning based supervised fine-level classification algorithm. We have collected exhaustive home-based ADLs and IADLs data from multiple users. Our designed classifier is validated to be able to recognize very fine-grained complex 22 daily activities (much larger number than 6-12 activities detected by state-of-the-art works using wearable and no camera/ video) with high average test accuracies of 90% or more for two users in two different home environments