With an ageing population, the healthcare sector struggles to cope worldwide. Home monitoring using technology is increasingly used to release the pressure on healthcare professionals and keep elderly at home for longer. However, current solutions present technical (i.e. low resolution and convenience) and ethical issues. In this paper, we used 2D LiDAR, as a sensor that can provide significant information on patient’s activities, whilst still ensuring their privacy (i.e. 2D LiDAR only produces anonymous point clouds). Particularly, we developed an algorithm that uses clustering on the raw 2D LiDAR data, object tracking on cluster centroids to identify a user in a room, and semantic enrichment using metadata about the room (i.e. areas of interest and furniture position) to associate stationary and non-stationary points with every day activities (e.g. relaxing on the couch, working at the desk, standing by the window, and walking). We tested our method across different users (N = 3) and two rooms for a total 60 randomly ordered activity sequences, with five activities per sequence and each activity performed for 30 s. We obtained an overall accuracy in identifying the activities of 0.88 (standard deviation [SD], 0.06). Walking was the activity with the highest F1 score, with values of 0.97 (SD, 0.04) and 1.00 (SD, 0.00). As expected, activities where occlusion from pieces of furniture might be in the way had worse performance with an F1 score of 0.81 (SD, 0.24). Although performed on a limited sample, our paper shows potential for 2D LiDAR to be used for remote monitoring of mobility and daily activities of elderly in their home.