Age-related changes in upper limb coordination in a complex reaching task

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement

For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is labor-intensive to design and adjust game levels for different individuals. We work on generating training content for a desktop end-effector rehabilitation robot and propose a method to automatically generate individualized training plans. By modeling the search of the training motions as finding optimal hand paths and trajectories, we introduce solving the design problem with a multi-objective optimization (MO) solver. We further improve the MO solver to enhance the diversity of the solutions. With the proposed approach, our system is capable of automatically generating various training plans considering the training intensity and dexterity of each joint in the upper limb. In addition, the enhanced diversity avoids repeated training plans, which helps motivate the user in the rehabilitation. We test our method with different requirements on the training plans and validate the solutions.

Intra-limb joint coordination measures of upper limb and hand movements: A systematic review

BACKGROUND

People with central nervous system disorders typically have difficulties in coordination of the upper limb and hand movements, which significantly impairs their activities of daily living. Laboratory-based measures can provide quantitative and objective information about intra-limb coordination to aid the rehabilitation process of this population. However, there is currently no comprehensive review of laboratory-based measures.

RESEARCH QUESTIONS

The aim of this review was to identify and summarize laboratory-based intra-limb coordination measures for different upper limb and hand movements.

METHODS

Searches were performed in the CINAHL, Embase, IEEE Xplore, MEDLINE, PubMed and Web of Science databases to identify studies published between 2013 and 2022. Two authors independently performed paper selection, data extraction and quality assessment.

RESULTS

21 papers were identified, and six types of coordination measures were classified. These included principal component analysis, continuous relative phase analysis, correlation analysis, regression analysis, uncontrolled manifold analysis, and uncorrelated surrogate data analysis, in descending order of occurrence. Regarding psychometric properties, all measures demonstrated good discriminative validity. However, only the principal component analysis approach and the continuous relative phase analysis approach were found to have good convergent validity and responsiveness, respectively. In terms of their practicality, these measures were primarily utilized for quantifying coordination in individuals with neurological disorders, with a greater emphasis on the coordination of upper limb movements rather than hand movements.

SIGNIFICANCE

This review summarized and critiqued the characteristics of six types of joint coordination measures. Researchers and clinicians should therefore select appropriate measures based on individual needs. Future research should continue on analysing coordination in individuals with pathological conditions and exploring the application of these measures in quantifying hand movement coordination, to advance current knowledge and inform rehabilitation practices.

Kinematic coordinations capture learning during human-exoskeleton interaction

Human-exoskeleton interactions have the potential to bring about changes in human behavior for physical rehabilitation or skill augmentation. Despite significant advances in the design and control of these robots, their application to human training remains limited. The key obstacles to the design of such training paradigms are the prediction of human-exoskeleton interaction effects and the selection of interaction control to affect human behavior. In this article, we present a method to elucidate behavioral changes in the human-exoskeleton system and identify expert behaviors correlated with a task goal. Specifically, we observe the joint coordinations of the robot, also referred to as kinematic coordination behaviors, that emerge from human-exoskeleton interaction during learning. We demonstrate the use of kinematic coordination behaviors with two task domains through a set of three human-subject studies. We find that participants (1) learn novel tasks within the exoskeleton environment, (2) demonstrate similarity of coordination during successful movements within participants, (3) learn to leverage these coordination behaviors to maximize success within participants, and (4) tend to converge to similar coordinations for a given task strategy across participants. At a high level, we identify task-specific joint coordinations that are used by different experts for a given task goal. These coordinations can be quantified by observing experts and the similarity to these coordinations can act as a measure of learning over the course of training for novices. The observed expert coordinations may further be used in the design of adaptive robot interactions aimed at teaching a participant the expert behaviors.

Age-related difference in muscle metabolism patterns during upper limb's encircling exercise: a near-infrared spectroscopy study

Aging is usually accompanied by decrease in limb motor function and change in muscle metabolism patterns. However, few studies have investigated the aging effect on muscle hemodynamics of the upper extremity. This study aims to explore the aging effect on muscle metabolism patterns during upper limb's exercise. Twelve middle-aged and elderly subjects and 12 young subjects were recruited, and muscle oxygenation signals from these subjects' biceps brachii muscles were collected during active and passive upper limb's encircling exercise with near-infrared spectroscopy (NIRS). The old group showed stronger muscle hemodynamic metabolism than the young group. The multiscale fuzzy approximate entropy and multiscale transfer entropy analyses indicated higher complexity and stronger interlimb coupling of the muscle oxygenation signals for the old group. Based on the selected muscle metabolism features, the constructed support vector machine model showed a high accuracy rate for classifying the two groups of subjects: 91.6% for the passive mode and 87.5% for the active mode. Our results proved the specific muscle metabolism patterns in the upper limb's exercise for old subjects, promoting the understanding of the aging effect on muscle hemodynamics.