Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.
This content will become publicly available on December 5, 2024
Design and Validation of a Versatile High Torque Quasidirect Drive Hip Exoskeleton
The field of wearable robotics has made significant progress toward augmenting human functions from multimodal ambulation to manual lifting tasks. However, most of these systems are designed to be task-specific and only focus on a single type of movement (e.g., ambulation). In this work, we design, fabricate, and characterize a versatile hip exoskeleton testbed for lifting and ambulation tasks. The exoskeleton testbed is actuated with custom-built quasidirect drive actuators. We produce an orthotic interface to transmit high torques and assemble a custom mechatronic control system for the exoskeleton testbed. We also detail controllers for level ground walking, incline walking, and symmetric knee to waist lifting. We quantify the actuator torque tracking performance quantified through benchtop and human experiments. During knee-to-waist cyclic lifting, the powered condition exhibited a 16.7% reduction in net metabolic cost compared to the no exoskeleton condition (three subjects). For additional tasks (inclined walking, level-walking), the device provided metabolic reductions when compared with the unpowered case (single subject). These testbed results illustrate the potential for versatile hip assistance and can be used to design future optimized devices.
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- Award ID(s):
- 1830498
- NSF-PAR ID:
- 10478138
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE/ASME Transactions on Mechatronics
- ISSN:
- 1083-4435
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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