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Convolutional Neural Networks (CNN) are becomin deeper and deeper. It is challenging to deploy the networks directly to embedded devices be- cause they may have different computational capacities. When deploying CNNs, the trade-off between the two objectives: accuracy and inference speed, should be considered. NSGA-II (Non-dominated Sorting Genetic Algorithm II) algorithm is a multi-objective optimiza- tion algorithm with good performance. The network architecture has a significant influence on the accuracy and inference time. In this paper, we proposed a con- volutional neural network optimization method using a modified NSGA-II algorithm to optimize the network architecture. The NSGA-II algorithm is employed to generate the Pareto front set for a specific convolutional neural network, which can be utilized as a guideline for the deployment of the network in embedded devices. The modified NSGA-II algorithm can help speed up the training process. The experimental results show that the modified NSGA-II algorithm can achieve similar results as the original NSGA-II algorithm with respect to our specific task and saves 46.20% of the original training time. 

Older adults who age in place face many health problems and need to be taken care of. Fall is a serious problem among elderly people. In this paper, we present the design and implementation of collaborative fall detection using a wearable device and a companion robot. First, we developed a wearable device by integrating a camera, an accelerometer and a microphone. Second, a companion robot communicates with the wearable device to conduct collaborative fall detection. The robot is also able to contact caregivers in case of emergency. The collaborative fall detection method consists of motion data based preliminary detection on the wearable device and video-based final detection on the companion robot. Both convolutional neural network (CNN) and long short-term memory (LSTM) are used for video-based fall detection. The experimental results show that the overall accuracy of video-based algorithm is 84%. We also investigated the relation between the accuracy and the number of image frames. Our method improves the accuracy of fall detection while maximizing the battery life of the wearable device. In addition, our method significantly increases the sensing range of the companion robot.