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A muscle’s structure, or architecture, is indicative of its function and is plastic; changes in input to or use of the muscle alter its architecture. Stroke-induced neural deficits substantially alter both input to and usage of individual muscles. We combined in vivo imaging methods (second-harmonic generation microendoscopy, extended field-of-view ultrasound, and fat-suppression MRI) to quantify functionally meaningful architecture parameters in the biceps brachii of both limbs of individuals with chronic hemiparetic stroke and in age-matched, unimpaired controls. Specifically, serial sarcomere number (SSN) and physiological cross-sectional area (PCSA) were calculated from data collected at three anatomical scales: sarcomere length, fascicle length, and muscle volume. The interlimb differences in SSN and PCSA were significantly larger for stroke participants than for participants without stroke (P= 0.0126 andP= 0.0042, respectively), suggesting we observed muscle adaptations associated with stroke rather than natural interlimb variability. The paretic biceps brachii had ∼8,200 fewer serial sarcomeres and ∼2 cm²smaller PCSA on average than the contralateral limb (bothP< 0.0001). This was manifested by substantially smaller muscle volumes (112 versus 163 cm³), significantly shorter fascicles (11.0 versus 14.0 cm;P< 0.0001), and comparable sarcomere lengths (3.55 versus 3.59 μm;P= 0.6151) between limbs. Most notably, this study provides direct evidence of the loss of serial sarcomeres in human muscle observed in a population with neural impairments that lead to disuse and chronically place the affected muscle at a shortened position. This adaptation is consistent with functional consequences (increased passive resistance to elbow extension) that would amplify already problematic, neurally driven motor impairments.

 

Individuals post stroke experience motor impair- ments, such as loss of independent joint control, weakness, and delayed movement initiation, leading to an overall reduction in arm function. Their motion becomes slower and more discoordinated, making it difficult to complete timing- sensitive tasks, such as balancing a glass of water or carrying a bowl with a ball inside it. Understanding how the stroke- induced motor impairments interact with each other can help design assisted training regimens for improved recovery. In this study, we investigate the effects of abnormal joint coupling patterns induced by flexion synergy on timing-sensitive motor coordination in the paretic upper limb. We design a virtual ball-in-bowl task that requires fast movements for optimal performance and implement it on a robotic system, capable of providing varying levels of abduction loading at the shoulder. We recruit 12 participants (6 individuals with chronic stroke and 6 unimpaired controls) and assess their skill at the task at 3 levels of loading, defined by the vertical force applied at the robot end-effector. Our results show that, for individuals with stroke, loading has a significant effect on their ability to generate quick coordinated motion. With increases in loading, their overall task performance decreases and they are less able to compensate for ball dynamics—frequency analysis of their motion indicates that abduction loading weakens their ability to generate movements at the resonant frequency of the dynamic task. This effect is likely due to an increased reliance on lower resolution indirect motor pathways in individuals post stroke. Given the inter-dependency of loading and dynamic task performance, we can create targeted robot-aided training protocols focused on improving timing-sensitive motor control, similar to existing progressive loading therapies, which have shown efficacy for expanding reachable workspace post stroke. 

Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical human–robot interaction has been significantly more effective than unassisted practice or human-mediated training. This article describes a hybrid shared control robot, which enhances task learning through kinesthetic feedback. The assistance assesses user actions using a task-specific evaluation criterion and selectively accepts or rejects them at each time instant. Through two human subject studies (total [Formula: see text]), we show that this hybrid approach of switching between full transparency and full rejection of user inputs leads to increased skill acquisition and short-term retention compared with unassisted practice. Moreover, we show that the shared control paradigm exhibits features previously shown to promote successful training. It avoids user passivity by only rejecting user actions and allowing failure at the task. It improves performance during assistance, providing meaningful task-specific feedback. It is sensitive to initial skill of the user and behaves as an “assist-as-needed” control scheme, adapting its engagement in real time based on the performance and needs of the user. Unlike other successful algorithms, it does not require explicit modulation of the level of impedance or error amplification during training and it is permissive to a range of strategies because of its evaluation criterion. We demonstrate that the proposed hybrid shared control paradigm with a task-based minimal intervention criterion significantly enhances task-specific training. 

This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person’s existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis.