Exoskeletons and robots have been used as a common practice to assist and automate rehabilitation exercises. Exoskeleton fitting and alignments are important factors and challenges that need to be addressed for smooth and safe operations and better outcomes. Such challenges often dictate the exoskeleton design approaches. Some focus on simplifying and mimicking human joints (joint-based) while others have a focus on a specific task (task-based), which does not need to align with the corresponding limb joint/s to generate the desired anatomical motion. In this study, the two design approaches are assessed in an elbow flexion-extension task. The muscle responses have been collected and compared with and without the exoskeletons. Based on 6 with no disability participants, the normalized Electromyography (EMG) RMS values are plotted. The plot profiles and magnitudes are used as a base to assess the exoskeleton alignment. For this specific task, the task-based exoskeleton has shown a profile closer to the one without exoskeleton with a relatively identical support as the joint-based one; the latter is evidenced through most subjects’ muscle response magnitudes. This preliminary data has shown a good methodology and insight towards the assessment of exoskeletons, but more human subject data is needed with different task combinations to further strengthen the findings.
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Design of Bio-Exoskeleton for Elbow Rehabilitation
In this study, a methodology for designing a task-based exoskeleton which can recreate the end-effector trajectory of a given limb during a rehabilitation task/movement is presented. The exoskeleton provides an option to replace traditional joint-based exoskeleton joints, which often have alignment issues with the biological joint. The proper fit of the exoskeleton to the user and task are research topics to reduce pain or joint injuries as well as for the execution of the task. The proposed task-based synthesis method was successfully applied to generate the 3D motions of the elbow flexion and extensions using a one degree of freedom (DOF), spatial four-bar mechanism. The elbow joint is analyzed through motion capture system to develop the bio-exoskeleton. The resulted exoskeleton does not need to align with the corresponding limb joint to generate the desired anatomical motion.
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- Award ID(s):
- 1915872
- NSF-PAR ID:
- 10250299
- Date Published:
- Journal Name:
- 2021 Design of Medical Devices Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Human-exoskeleton misalignment could lead to permanent damages upon the targeted limb with long-term use in rehabilitation. Hence, achieving proper alignment is necessary to ensure patient safety and an effective rehabilitative journey. In this study, a joint-based and task-based exoskeleton for upper limb rehabilitation were modeled and assessed. The assessment examined and quantified the misalignment present at the elbow joint as well as its effects on the main flexor and extensor muscles’ tendon length during elbow flexion-extension. The effects of the misalignments found for both exoskeletons resulted to be minimal in most muscles observed, except the anconeus and brachialis. The anconeus muscle demonstrated a relatively higher variation in tendon length with the joint-based exoskeleton misalignment, indicating that the task-based exoskeleton is favored for tasks that involve this particular muscle. Moreover, the brachialis demonstrated a significantly higher variation with the task-based exoskeleton misalignment, indicating that the joint-based exoskeleton is favored for tasks that involve the muscle.
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null (Ed.)Perception of limb position and motion combines sensory information from spindles in muscles that span one joint (monoarticulars) and two joints (biarticulars). This anatomical organization should create interactions in estimating limb position. We developed two models, one with only monoarticulars and one with both monoarticulars and biarticulars, to explore how biarticulars influence estimates of arm position in hand ( x, y) and joint ( shoulder, elbow) coordinates. In hand coordinates, both models predicted larger medial-lateral than proximal-distal errors, although the model with both muscle groups predicted that biarticulars would reduce this bias. In contrast, the two models made significantly different predictions in joint coordinates. The model with only monoarticulars predicted that errors would be uniformly distributed because estimates of angles at each joint would be independent. In contrast, the model that included biarticulars predicted that errors would be coupled between the two joints, resulting in smaller errors for combinations of flexion or extension at both joints and larger errors for combinations of flexion at one joint and extension at the other joint. We also carried out two experiments to examine errors made by human subjects during an arm position matching task in which a robot passively moved one arm to different positions and the subjects moved their other arm to mirror-match each position. Errors in hand coordinates were similar to those predicted by both models. Critically, however, errors in joint coordinates were only similar to those predicted by the model with monoarticulars and biarticulars. These results highlight how biarticulars influence perceptual estimates of limb position by helping to minimize medial-lateral errors. NEW & NOTEWORTHY It is unclear how sensory information from muscle spindles located within muscles spanning multiple joints influences perception of body position and motion. We address this issue by comparing errors in estimating limb position made by human subjects with predicted errors made by two musculoskeletal models, one with only monoarticulars and one with both monoarticulars and biarticulars. We provide evidence that biarticulars produce coupling of errors between joints, which help to reduce errors.more » « less
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null (Ed.)For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system.more » « less
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Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DMD2021-1035
