Population aging has been motivating academic research and industry to develop technologies for the improvement of older people's quality of life, medical diagnosis, and support on frailty cases. Most of available research prototypes for older people monitoring focus on fall detection or gait analysis and rely on wearable, environmental, or video sensors. We present an evaluation of a research prototype of a video monitoring system for event recognition of older people. The prototype accuracy is evaluated for the recognition of physical tasks (e.g., Up and Go test) and instrumental activities of daily living (e.g., watching TV, writing a check) of participants of a clinical protocol for Alzheimer's disease study (29 participants). The prototype uses as input a 2D RGB camera, and its performance is compared to the use of a RGB-D camera. The experimentation results show the proposed approach has a competitive performance to the use of a RGB-D camera, even outperforming it on event recognition precision. The use of a 2D-camera is advantageous, as the camera field of view can be much larger and cover an entire room where at least a couple of RGB-D cameras would be necessary.