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A Neurocognitive Robot Assistant for Robust Event Detection

2016; Springer Nature; Linguagem: Inglês

10.1007/978-3-319-30184-6_1

1860-9503

German I. Parisi, Stefan Wermter,

Human Pose and Action Recognition

Falls represent a major problem in the public health care domain, especially among the elderly population. Therefore, there is a motivation to provide technological solutions for assisted living in home environments. We introduce a neurocognitive robot assistant that monitors a person in a household environment. In contrast to the use of a static-view sensor, a mobile humanoid robot will keep the moving person in view and track his/her position and body motion characteristics. A learning neural system is responsible for processing the visual information from a depth sensor and denoising the live video stream to reliably detect fall events in real time. Whenever a fall event occurs, the humanoid will approach the person and ask whether assistance is required. The robot will then take an image of the fallen person that can be sent to the person's caregiver for further human evaluation and agile intervention. In this paper, we present a number of experiments with a mobile robot in a home-like environment along with an evaluation of our fall detection framework. The experimental results show the promising contribution of our system to assistive robotics for fall detection of the elderly at home.