Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159638
Title: A voice activated bi-articular exosuit for upper limb assistance during lifting tasks
Authors: Kim, Yongtae G.
Little, Kieran
Noronha, Bernardo
Xiloyannis, Michele
Masia, Lorenzo
Accoto, Dino
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Kim, Y. G., Little, K., Noronha, B., Xiloyannis, M., Masia, L. & Accoto, D. (2020). A voice activated bi-articular exosuit for upper limb assistance during lifting tasks. Robotics and Computer-Integrated Manufacturing, 66, 101995-. https://dx.doi.org/10.1016/j.rcim.2020.101995
Project: M4062147
Journal: Robotics and Computer-Integrated Manufacturing
Abstract: Humans are favoured to conventional robotics for some tasks in industry due to their increased dexterity and fine motor skills, however, performance of these tasks can result in injury to the user at a cost to both the user and the employer. In this paper we describe a lightweight, upper-limb exosuit intended to assist the user during lifting tasks (up to 10kg) and while operating power tools, which are common activities for industrial workers. The exosuit assists elbow and shoulder flexion for both arms and allows for passive movements in the transverse plane. To achieve the design criteria an underactuated mechanism has been developed, where a single motor is used to assist two degrees of freedom per arm. In the intended application, the hands are generally busy and cannot be used to provide inputs to the robot, therefore, a voice-activated control has been developed that allows the user to give voice commands to operate the exosuit. Experiments were performed on 5 healthy subjects to assess the change in Muscular Activation (MA), inferred through Electromyography (EMG) signals, during three tasks: i) lifting and releasing a load; ii) holding a position and iii) manipulating a tool. The results showed that the exosuit is capable of reducing EMG activity (between 24.6% and 64.6%) and the recognition rate (94.8%) of the voice recognition module was evaluated.
URI: https://hdl.handle.net/10356/159638
ISSN: 0736-5845
DOI: 10.1016/j.rcim.2020.101995
Research Centres: Robotics Research Centre 
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:RRC Journal Articles

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