In this paper, we propose a dense motion segmentation method for human daily activity recognition from a wearable device - "Smart Glasses". The glasses are embedded with a camera, which allows the system to automatically recognise the wearer's activities from a first-person perspective. This application can be broadly applied to patients, elderly, safety workers, e-health monitoring, or anyone requiring cognitive assistance or guidance on their activities of daily living (ADLs). We validate our system in challenging real-world scenarios, and compare two feature extraction approaches: averaged optical flow and a combined dense motion segmentation approach. We classify them using LogitBoost (on Decision Stumps) and Support Vector Machine (SVM). We also suggest the optimal settings of the classifiers through cross-validation over our ADLs database. The results show that the optical flow with average pooling has a good performance when classifying general locomotive activities. The results also indicate the benefits that dense motion segmentation features can have on reliably classify activities involving a moving object, such as hands. We achieve an overall accuracy of up to 69.76% on 12 ADLs using local classifiers, and with a Hidden Markov Model (HMM) process this accuracy improves to up to 89.59%.
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