Several new algorithms for camera-based fall detection have been proposed in the literature recently, with the aim to monitor older people at home so nurses or family members can be warned in case of a fall incident.It is based on background subtraction and simple measures extracted from the dominant foreground object such as aspect ratio, fall angle and head speed.Under such circumstances and given the large variability of the data in combination with the limited number of examples available to train the system, we posit that simple yet robust methods incorporating, where available, domain knowledge (e.g. the fact that the background is static or that a fall usually involves a downward motion) seem to be most promising.We investigated the variation in en- vironment parameters and context during the fall incidents.