Fall detection system based on infrared array sensor and multi-dimensional feature fusion

In recent years, with the acceleration of the aging of the population, the safety of the elderly living alone has attracted great attention, and the falls have become one of the main factors leading to elderly casualties. In order to obtain a high precision and low cost fall detection system for the elderly, a fall detection system based on infrared array sensor and multi-dimensional feature fusion is proposed in this paper. First, we propose a new data acquisition method using infrared array sensor, which effectively enlarges the detection area. Then the personnel positioning is performed before fall detection, which can ensure real-time detection while reducing computational complexity. In addition, a sliding window algorithm is developed and four representative features of a fall are extracted from the collected data, which is fitful to the online detection. Among them, the four characteristics include the change in the center of mass of the falling process, the change in the speed, the change in the area of the person, and the change in variance. Finally, based on the refined features, the support vector machine (SVM) classifier is introduced to identify falls and improve the classification accuracy. The experimental results validate that the proposed fall detection system shows good fall detection accuracy and great practicability.