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Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics

Grimmer, Martin ; Zeiss, Julian ; Weigand, Florian ; Zhao, Guoping (2022):
Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics. (Publisher's Version)
In: Frontiers in Neurorobotics, 16, Frontiers Media S.A., e-ISSN 1662-5218,
DOI: 10.26083/tuprints-00022549,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics
Language: English
Abstract:

Human-in-the-loop (HITL) optimization with metabolic cost feedback has been proposed to reduce walking effort with wearable robotics. This study investigates if lower limb surface electromyography (EMG) could be an alternative feedback variable to overcome time-intensive metabolic cost based exploration. For application, it should be possible to distinguish conditions with different walking efforts based on the EMG. To obtain such EMG data, a laboratory experiment was designed to elicit changes in the effort by loading and unloading pairs of weights (in total 2, 4, and 8 kg) in three randomized weight sessions for 13 subjects during treadmill walking. EMG of seven lower limb muscles was recorded for both limbs. Mean absolute values of each stride prior to and following weight loading and unloading were used to determine the detection rate (100% if every loading and unloading is detected accordingly) for changing between loaded and unloaded conditions. We assessed the use of multiple consecutive strides and the combination of muscles to improve the detection rate and estimated the related acquisition times of diminishing returns. To conclude on possible limitations of EMG for HITL optimization, EMG drift was evaluated during the Warmup and the experiment. Detection rates highly increased for the combination of multiple consecutive strides and the combination of multiple muscles. EMG drift was largest during Warmup and at the beginning of each weight session. The results suggest using EMG feedback of multiple involved muscles and from at least 10 consecutive strides (5.5 s) to benefit from the increases in detection rate in HITL optimization. In combination with up to 20 excluded acclimatization strides, after changing the assistance condition, we advise exploring about 16.5 s of walking to obtain reliable EMG-based feedback. To minimize the negative impact of EMG drift on the detection rate, at least 6 min of Warmup should be performed and breaks during the optimization should be avoided. Future studies should investigate additional feedback variables based on EMG, methods to reduce their variability and drift, and should apply the outcomes in HITL optimization with lower limb wearable robots.

Journal or Publication Title: Frontiers in Neurorobotics
Volume of the journal: 16
Place of Publication: Darmstadt
Publisher: Frontiers Media S.A.
Collation: 20 Seiten
Uncontrolled Keywords: EMG, human-in-the-loop, optimization, control, exoskeleton, wearable robotics, feedback, electromyography
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
700 Künste und Unterhaltung > 796 Sport
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik
03 Department of Human Sciences > Institut für Sportwissenschaft > Sportbiomechanik
Date Deposited: 31 Oct 2022 14:08
Last Modified: 31 Oct 2022 14:08
DOI: 10.26083/tuprints-00022549
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-225492
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22549
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