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Fall Detection Based on Body Part Tracking
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Fall Detection Based on Body Part Tracking

Category : GSM-GPS


Sub Category : GSM & GPS


Project Code : ITGP15


Project Abstract

            The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.

Existing System

Proposed System

In earlier system, the elder people live alone is too risk

 

Drawbacks:

      Ø   tracking is not present

    Ø  Other person’s help is needed to intimate the accident information.

 In proposed system, we can design a new monitoring and tracking system to provide the emergency information to the rescue section automatically by using GSM technology.

 

Advantage:

     Ø  Low cost

     Ø  Easy to locate the detection  place.

     Ø  High flexibility
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