The effect of age on ankle joint position sense differs between contralateral matching and ipsilateral reproduction tasks.

Ankle joint angle position sense (JPS) plays a crucial role in maintaining balance and coordinating movements, yet its changes across the lifespan remain unclear. This cross-sectional study aimed to investigate changes in ankle JPS across the lifespan using an ipsilateral reproduction task (IRT) and a contralateral concurrent matching task (CMT). One hundred and fifty eight individuals (6-92yrs) were allocated into 6 groups: Young-children (YC; n = 14) and Old-children (OC; n = 15), Young (Y: 19-39yrs; n = 33), Middle-aged (M: 40-59yrs; n = 41), Young-Old (YO: 60-5yrs; n = 24) and Old-Old (OO: >76yrs; n = 31) adults.

Role of motor unit activity in flexor digitorum brevis to maintain balance during forward leaning.

Our purpose was to compare the influence of motor unit activity in Flexor Digitorum Brevis (FDB) and Soleus (SOL) on force fluctuations during three forward-leaning tasks. Ground reaction forces and high-density EMG signals were collected from 19 males when leaning forward at 25%, 50%, and 75% of maximal forward leaning force. EMG amplitude increased with percent of leaning and was greater for SOL than FDB, but there were no differences in force fluctuations across tasks.

Distinguishing the activity of flexor digitorum brevis and soleus across standing postures with deep learning models.

Background: Electromyographic (EMG) recordings indicate that both the flexor digitorum brevis and soleus muscles contribute significantly to the control of standing balance, However, less is known about the adjustments in EMG activity of these two muscles across different postures.

Research Question: The purpose of our study was to use deep-learning models to distinguish between the EMG activity of the flexor digitorum brevis and soleus muscles across four standing postures.

Methods: Deep convolutional neural networks were employed to classify standing postures based on the temporal and spatial features embedded in high-density surface EMG signals.

Distinguishing among standing postures with machine learning-based classification algorithms.

The purpose of our study was to evaluate the accuracy with which classification algorithms could distinguish among standing postures based on center-of-pressure (CoP) trajectories. We performed a secondary analysis of published data from three studies: Study A) assessment of balance control on firm or foam surfaces with eyes-open or closed, Study B) quantification of postural sway in forward-backward and side-to-side directions during four standing-balance tasks that differed in difficulty, and Study C) an evaluation of the impact of two modes of transcutaneous electrical nerve stimulation on balance control in older adults. Three classification algorithms (decision tree, random forest, and k-nearest neighbor) were used to classify standing postures based on the extracted features from CoP trajectories in both the time and time-frequency domains.

Reliability of Semitendinosus and Biceps Femoris Aponeurosis Thickness Using B-Mode Ultrasound.

Objectives: The aim of the present study was to assess the reliability of aponeurosis and muscle thickness of the hamstrings using ultrasound (US).

Methods: US images were captured from the semitendinosus (ST) and biceps femoris long head (BFlh) of 30 individuals (15 females), undergoing two testing sessions with a 24-hour interval. Measurements were taken at six sites along the thigh at two knee angles (0° = full extension and 90°) in prone position.