| Summary: | 碩士 === 大同大學 === 資訊工程學系(所) === 107 === Fall detection mechanism can be sorted by the position where the sensor is placed. One is “Placed in the environment” and the other is “Attached on the human body”. The sensor of the first method usually required to be placed in specific place, and the price of the sensor is expensive. In contrast, the sensor of the second method is not only affordable but also no restrictions on the place. The second method usually uses a smartphone as a sensor, because the smartphone can complete the detection, determination, and notification by itself. The initial research in detection methods of smartphone had to place smartphone somewhere specifically on the body. But in reality, phone users have different habits of smartphone placement. It is important to consider that users will place their smartphone in different positions instead of being placed a single position.
In view of this, this paper proposes a mechanism for using accelerometer and two neural networks for fall detection. First, using the first neural network to exclude non-fall event. The second neural network will confirm the fall event. In addition, the mechanism does not require users to place the smartphone on a single body part allowing users to place the smartphone in a garment, trousers or jacket pocket. According to experimental results
in this paper, the Specificity and Accuracy of this mechanism are better than the three other methods. The Sensitivity of this method is slightly lower than one of three method, but better than other two.
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