Mobeacon: An iBeacon-Assisted Smartphone-Based Real Time Activity Recognition Framework

Human activity recognition using multi-modal sensing technologies to automatically collect and classify daily activities has become an active field of research. Given the proliferation of smart and wearable devices and their greater acceptance in human lives, the need for developing real time lightweight activity recognition algorithms become a viable and urgent avenue. Although variants of online and offline lightweight activity recognition algorithms have been developed, realizing them on real time to recognize people's activities is still a challenging research problem due to the computational complexity of building, training, learning and storing activity models in resource constrained smart and wearable devices. To navigate the above challenges, we build Mobeacon: a mobile phone and iBeacon sensor-based smart home activity recognition system. We investigated the viability of extending Bagging Ensemble Learning (BEL) and Packaged Naive Bayes (PNB) classification algorithms for high-level activity recognition on smartphone. We incorporated the semantic knowledge of the testing environment and used that with the built-in adaptive learning models on smartphone to ease the ground truth data annotation. We demonstrated that Mobeacon outperforms existing lightweight activity recognition techniques in terms of accuracy (max. 94%) in a low resource setting and proves itself substantially efficient to reside on smartphones for recognizing ADLs in real time.