Paper Info
Title
Intention-detection strategies for upper limb exosuits: model-based myoelectric vs dynamic-based control
Abstract
The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics.
Year
DOI
Venue
2020
10.1109/BioRob49111.2020.9224284
2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
Keywords
DocType
ISSN
Human-machine interfaces,Wearable technologies,Soft robotics
Conference
2155-1782
ISBN
Citations 
PageRank 
978-1-7281-5907-2
1
0.38
References 
Authors
0
7
Name
Order
Citations
PageRank
Nicola Lotti151.89
Michele Xiloyannis2295.94
Francesco Missiroli351.89
Domenico Chiaradia4104.11
Antonio Frisoli547665.01
Vittorio Sanguineti66812.86
Lorenzo Masia784.43