Activity recognition is one of the most active topics within computer vision. Despite its popularity, its application in real life scenarios is limited because many methods are not entirely automated and consume high computational resources for inferring information. In this work, we contribute two novel algorithms: (a) one for automatic video sequence segmentation - elsewhere referred to as activity spotting or activity detection - and (b) a second one for reducing activity representation computational cost. Two Bag-of-Words (BoW) representation schemas were tested for recognition purposes. A set of experiments was performed, both on publicly available datasets of activities of daily living (ADL), but also on our own ADL dataset with both healthy subjects and people with dementia, in realistic, life-like environments that are more challenging than those of benchmark datasets. Our method is shown to provide results better than, or comparable with, the SoA, while we also contribute a realistic ADL dataset to the community.