A Meal-Assistance Robot System for Asian Food and Its Food Acquisition Point Estimation and User Interface Based on Face Recognition

Abstract

Various meal-assistance robot (MAR) systems are being studied, and several products have already been commercialized to alleviate the imbalance between the rising demand and diminishing supply of meal care services. However, several challenges remain. First, most of these services can serve limited types of western food using a predefined route. Additionally, their spoon or fork sometimes makes it difficult to acquire Asian food that is easy to handle with chopsticks. In addition, their limited user interface, requiring physical contact, makes it difficult for people with severe disabilities to use MARs alone. This paper proposes an MAR system that is suitable for the diet of Asians who use chopsticks. This system uses Mask R-CNN to recognize the food area on the plate and estimates the acquisition points for each side dish. The points become target points for robot motion planning. Depending on which food the user selects, the robot uses chopsticks or a spoon to obtain the food. In addition, a non-contact user interface based on face recognition was developed for users with difficulty physically manipulating the interface. This interface can be operated on the user’s Android OS tablet without the need for a separate dedicated display. A series of experiments verified the proposed system’s effectiveness and feasibility.

Keywords

• meal-assistance robot
• deep learning
• instance segmentation
• human–robot interaction
• face recognition