Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.
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This content will become publicly available on January 25, 2025
Assessing the Impact of Active Back Support Exoskeletons on Muscular Activity during Construction Tasks: Insights from Physiological Sensing
Active exoskeletons are emerging as ergonomic solutions in the construction sector to reduce work-related musculoskeletal injuries. While the benefits of active exoskeletons are promising, they can also cause increased muscle activity, leading to local muscular fatigue. This study aimed to examine the impact of the active exoskeleton system on the muscular activity of construction workers during common construction activities. Ten subjects completed material handling tasks under two weight conditions (10 and 30 lbs) in a lab-controlled environment, with and without using an active exoskeleton. Portable electromyography (EMG) sensors were used to measure lumbar erector spinae (LES) muscle activity in each condition. Four descriptive statistics features in the time and frequency domains were extracted from the collected signals. Results of the t-test showed a significant difference in the physiological metrics extracted from the subjects’ EMG signals of the LES muscle. Findings demonstrated that using active exoskeletons reduces the internal muscle force in the lower back regions of construction workers.
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- Award ID(s):
- 2410255
- PAR ID:
- 10518056
- Publisher / Repository:
- American Society of Civil Engineers
- Date Published:
- Journal Name:
- Computing in Civil Engineering 2023
- ISBN:
- 9780784485248
- Page Range / eLocation ID:
- 340 to 347
- Format(s):
- Medium: X
- Location:
- Corvallis, Oregon
- Sponsoring Org:
- National Science Foundation
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