| Type of Sensing | Sensors used | Algorithm type | Classification of falls | Disadvantages |
| Camera based Sensing | Camera | Detection based on human skeleton, Falling angle Vertical projection Histogram No feedback is considered | High false rate, Falls can be classified but not implemented | High Computing resources to process data continuously, Not portable, limited sensing area |
| Ambient Sensing | Pressure sensors, Floor vibration detectors, Bed exit detection sensors | Altitude change, vibrations (threshold) No feedback is considered | Not classified | Low fall detection accuracy for pressure sensor, High cost for other and portability |
| Werable sensors | 3-D Accelerometres, Xbee modules | Threshold based | Not classified | No feedback is considered Can be enhanced through efficient algorithms |
| Fall prevention system s | Educate people on preventing falls and reduce the costs due to falls |
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| Framework must be constructed based on data acquired from various scenarios surrounding fall-related events but it is not possible to have a standard data |