Privacy preserving getup detection
Jennifer Lumetzberger, Ali Raoofpour, Martin Kampel · 2021
The ageing population leads to an increase of people requiring long-term care. Assisting people when getting out of bed and fast reactions to falls can help to reduce costs and the risk of injury. We describe the possibility of detecting getting up behavior from a bed using different deep learning models and depth data as a proof of concept. The hereby used computer vision approach uses unobtrusive depth data in order to protect people’s privacy. We gather data from different subjects, postures, views and rooms, with which we then train the network. Both classification and object detection methods are able to reliably detect getting up behavior. Situations that could not be correctly classified were when a person was changing from lying to sitting or when the legs of the person were covered with a blanket. Our results show that using pretrained networks is the key contributor in training. We also demonstrate that convolutional neural networks are capable of extracting high-level task-dependent features from depth data which can be utilized in developing ambient intelligent systems. The practicality of the network can be adapted from getting up from a bed to sitting and walking inside the room, based on the purpose of the real-life application.