Collaborative Fall Detection using a Wearable Device and a Companion Robot

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.