Abstract
During the last years, the mobility of people with upper limb disabilities and constrained on power wheelchairs is empowered by robotic arms. Nowadays, even though modern manipulators offer a high number of functionalities, some users cannot exploit all those potentialities due to their reduced manual skills, even if capable of driving the wheelchair by means of proper Human–Machine Interface (HMI). Owing to that, this work proposes a low-cost manipulator realizing only simple tasks and controllable by three different graphical HMI. The latter are empowered using a You Only Look Once (YOLO) v2 Convolutional Neural Network that analyzes the video stream generated by a camera placed on the robotic arm end-effector and recognizes the objects with which the user can interact. Such objects are shown to the user in the HMI surrounded by a bounding box. When the user selects one of the recognized objects, the target position information is exploited by an automatic close-feedback algorithm which leads the manipulator to automatically perform the desired task. A test procedure showed that the accuracy in reaching the desired target is 78%. The produced HMIs were appreciated by different user categories, obtaining a mean score of 8.13/10.
Highlights
During the last years, modern electronic power wheelchairs have been equipped by manipulators to compensate the deficit in the manuals skill of users due to accidents or disabling diseases [1].Such robotic arms are designed to perform simple operations, such as knocking on a door or pressing buttons in a lift panel, turning on the light in a room, etc
Owing to the benefits introduced by such manipulators in terms of increment of mobility and autonomy, the research has been focusing on the realization of robotic arms able to perform very complex tasks such as interaction with small objects [2]
The prototype was characterized both for its accuracy in interacting with the required object and for the ability of the Human–Machine Interface (HMI) to provide good user experiences
Summary
Modern electronic power wheelchairs have been equipped by manipulators to compensate the deficit in the manuals skill of users due to accidents or disabling diseases [1]. Such robotic arms are designed to perform simple operations, such as knocking on a door or pressing buttons in a lift panel, turning on the light in a room, etc. Various Human–Machine Interfaces (HMIs) have been developed for people with different disabilities. There are several typologies of HMIs, which are optimized for specific classes of disease
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