Elbow Joint Stiffness Functional Scales Based on Hill's Muscle Model and Genetic Optimization.

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified "Reach and retrieve" task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill's muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.

Age and sex impact on visuospatial working memory (VSWM), mental rotation, and cognitive strategies during navigation.

This study assessed the impact of sex and typical aging on visuospatial working memory (VSWM), mental rotations, and navigational strategies using behavioral information. Fifty healthy participants regrouped in older (OA) and young adults (YA) performed the Walking Corsi test (WalCT) and the Redrawn Mental Rotation Test (MRT) to explore mental rotation abilities. We recorded kinematic data such as locomotion trajectories, and spatial orientations during navigation. We created a new method of data analysis for the WalCT performances and compared it with the classical approach. This original method allowed us to identify cognitive strategies based on errors analysis. Our data suggested that VSWM and mental rotation capacities in locomotion were modulated by age (YA scored higher than OA), and sex (Young Adult Males (YA-M) having higher performance than Young Adult Females (YA-F). We observed a preferential use of cognitive strategies related to sex; YA-F relied more on egocentric strategies whereas YA-M relied more on allocentric strategies. The preferential use of cognitive strategies in the YA group was not observed in the OA group producing more random errors per sequence. The results suggest the effects that age and sex have on VSWM, cognitive strategies, and mental rotation during navigation and highlight the importance of navigational strategies training.