A Fall Detection System Based on Infrared Array Sensors with Tracking Capability for the Elderly at Home
| Autor: | Chen, Wei-Han, 陳維漢 |
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| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 As the need of the elderly assistance increases, the healthcare monitoring systems are designed for improving the quality of life. The development strategy is aimed at comfortability, privacy, and intelligence. In this thesis, a low resolution privacy preserved infrared array sensor are adopted. The sensor is composed of a 16×4 thermopile array with the corresponding 60˚×16.4˚ field of view. To capture the infrared image, each pixel of infrared sensor contains the temperature value seen by the sensor. By using the infrared array sensors, our healthcare monitoring system is developed for the applications of tracking and fall detection. In our system, two infrared sensors are attached to the wall at different places and are used for capturing the three dimensional image information. Before the tracking process, the foreground of human body is determined by subtracting the image with the background model using the temperature difference characteristic. The background model would update with the background temperature adaptively. Using the temperature value within the foreground region, the angle of arrival (AOA) from each sensor is obtained. The location is then estimated by the AOA based positioning algorithm. The estimated position is passed to the regression model to reduce the positioning error. As a result, the mean error of our tracking algorithm is 13.39 cm. In addition to the tracking application, the fall detection algorithm is implemented by extracting the features from the falling action. Two sensors would capture the action at the same time. The sensor with larger foreground region is chosen for the feature extraction process, in which 7 features are included. These extracted features are applied to the k-nearest neighbor (k-NN) classification model for the fall detection. To build the k-NN model, 80 fall actions and 80 normal actions are collected from five subjects for estimating the performance. Using the 10-fold cross-validation method, the k value of k-NN model and the feature selection are determined by scanning the k value and every possible combination of feature subsets. Finally, 95.25% sensitivity, 90.75% specificity and 93% accuracy are achieved in our fall detection system. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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