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In this paper, we aimed to study the energy consumption problem in a collaborative activity monitoring system (CAMS) that consists of a compan- ion robot and a wearable device. First, we tested the energy consumption in different operation modes of the system. Based on that, we analyzed the effect of band- width on the time cost and energy consumption which allowed us to combine WiFi and Bluetooth together for data transmission to improve the performance of the system. Second, we preprocessed the image data on the wearable device to reduce the size of images before sending them to the robot, and analyzed the time and energy consumption cost by local computing and data transmission. Third, based on the bandwidth of WiFi and Bluetooth, the requirement of time and energy consumption, we proposed an optimization problem on image sizes in which the wearable device decides how to send the data to the robot to reduce the energy and time cost. The results showed that the relations between the bandwidth, time cost, image resolutions and energy consumption could be used to improve the performance of CAMS. 

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.