基于开源框架的人机交互危险姿态识别系统设计

Design of a human-computer interaction hazard posture recognition systembased on an open source framework

老年人因年龄增长、身体机能衰退和认知功能减弱而面临不同程度的生活危险。因此,为了及时发现、监测和处理老年人的危险姿势,从而保护老年人的安全和健康。研究提出一种融合端对端思想和卷积神经网络(Port to port convo-lutional neural network,PTP-CNN)的老年人危险位姿虚拟模型识别算法,从而做出预防性措施或及时的护理。研究结果表明,该系统在运用PTP-CNN算法时,Epochs的训练次数为15~30之间,MSE评价指标上PTP-CNN模型分别比SW-CNN、AlexNet降低25.33%、5.17%,说明PTP-CNN模型拥有更高的准确性和精确性,可以更好地进行图像识别任务,从而及时发现老年人的危险姿势。

Older people are exposed to different levels of life hazards due to ageing, physical deterioration and reduced cognitive function. Therefore, in order to detect, monitor and manage dangerous postures of the elderly in a timely manner, thus protecting the safety and health of the elderly. The study proposes a virtual model identification algorithm for the identification of hazardous postures of older people that incorporates end-to-end thinking and a port to port convolutional neural network (PTP-CNN) to provide preventive measures or timely care. The results of the study showed that the number of Epochs trained in the system using the PTP-CNN algorithm ranged from 15 to 30, and the MSE evaluation index of the PTP-CNN model was 25.33% and 5.17% lower than that of SW-CNN and AlexNet respectively, indicating that the PTP-CNN model has higher accuracy and precision and can better perform image recognition tasks and thus timely detection of dangerous postures of the elderly.