Name
Abstract | ||
|---|---|---|
The intention-detection strategy used to drive an exosuit is fundamental to evaluate the effectiveness and acceptability of the device. Yet, current literature on wearable soft robotics lacks evidence on the comparative performance of different control approaches for online intention-detection. In the present work, we compare two different and complementary controllers on a wearable robotic suit, previously formulated and tested by our group; a model-based myoelectric control (
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i>
), which estimates the joint torque from the activation of target muscles, and a force control that estimates human torques using an inverse dynamics model (
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i>
). We test them on a cohort of healthy participants performing tasks replicating functional activities of daily living involving a wide range of dynamic movements. Our results suggest that both controllers are robust and effective in detecting human–motor interaction, and show comparable performance for augmenting muscular activity. In particular, the biceps brachii activity was reduced by up to 74% under the assistance of the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i>
and up to 47% under the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i>
, compared to a no-suit condition. However, the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i>
outperformed the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i>
in promptness and assistance during movements that involve high dynamics. The exosuit work normalized with respect to the overall work was
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$68.84 \pm 3.81\%$</tex-math></inline-formula>
when it was ran by the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myoprocessor</i>
, compared to
<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$45.29 \pm 7.71\%$</tex-math></inline-formula>
during the
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic arm</i>
condition. The reliability and accuracy of motor intention detection strategies in wearable device is paramount for both the efficacy and acceptability of this technology. In this article, we offer a detailed analysis of the two most widely used control approaches, trying to highlight their intrinsic structural differences and to discuss their different and complementary performance. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TRO.2021.3137748 | IEEE Transactions on Robotics |
Keywords | DocType | Volume |
Control and learning for soft robots,control architectures and programming,human–machine interfaces (HRIs),modeling,wearable robots | Journal | 38 |
Issue | ISSN | Citations |
3 | 1552-3098 | 2 |
PageRank | References | Authors |
0.45 | 10 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Lotti | 1 | 5 | 1.89 |
| Michele Xiloyannis | 2 | 29 | 5.94 |
| Francesco Missiroli | 3 | 5 | 1.89 |
| Casimir Bokranz | 4 | 2 | 1.13 |
| Domenico Chiaradia | 5 | 10 | 4.11 |
| Antonio Frisoli | 6 | 476 | 65.01 |
| Robert Riener | 7 | 421 | 75.80 |
| Lorenzo Masia | 8 | 4 | 1.51 |