This study examined the concurrent age-related differences in muscle and tendon structure and properties. Achilles tendon morphology and mechanical properties and triceps surae muscle architecture were measured from 100 subjects [33 young (24 ± 2 yr) and 67 old (75 ± 3 yr)]. Motion analysis-assisted ultrasonography was used to determine tendon stiffness, Young's modulus, and hysteresis during isometric ramp contractions. Ultrasonography was used to measure muscle architectural features and size and tendon cross-sectional area. Older participants had 17% lower ( P < 0.01) Achilles tendon stiffness and 32% lower ( P < 0.001) Young's modulus than young participants. Tendon cross-sectional area was also 16% larger ( P < 0.001) in older participants. Triceps surae muscle size was smaller ( P < 0.05) and gastrocnemius medialis muscle fascicle length shorter ( P < 0.05) in old compared with young. Maximal plantarflexion force was associated with tendon stiffness and Young's modulus ( r = 0.580, P < 0.001 and r = 0.561, P < 0.001, respectively). Comparison between old and young subjects with similar strengths did not reveal a difference in tendon stiffness. The results suggest that regardless of age, Achilles tendon mechanical properties adapt to match the level of muscle performance. Old people may compensate for lower tendon material properties by increasing tendon cross-sectional area. Lower tendon stiffness in older subjects might be beneficial for movement economy in low-intensity locomotion and thus optimized for their daily activities.

During human locomotion lower extremity muscle-tendon units undergo cyclic length changes that were previously assumed to be representative of muscle fascicle length changes. Measurements in cats and humans have since revealed that muscle fascicle length changes can be uncoupled from those of the muscle-tendon unit. Ultrasonography is frequently used to estimate fascicle length changes during human locomotion. Fascicle length analysis requires time consuming manual methods that are prone to human error and experimenter bias. To bypass these limitations, we have developed an automatic fascicle tracking method based on the Lucas-Kanade optical flow algorithm with an affine optic flow extension. The aims of this study were to compare gastrocnemius fascicle length changes during locomotion using the automated and manual approaches and to determine the repeatability of the automated approach. Ultrasound was used to examine gastrocnemius fascicle lengths in eight participants walking at 4, 5, 6, and 7 km/h and jogging at 7 km/h on a treadmill. Ground reaction forces and three dimensional kinematics were recorded simultaneously. The level of agreement between methods and the repeatability of the automated method were quantified using the coefficient of multiple correlation (CMC). Regardless of speed, the level of agreement between methods was high, with overall CMC values of 0.90 ± 0.09 (95% CI: 0.86–0.95). Repeatability of the algorithm was also high, with an overall CMC of 0.88 ± 0.08 (95% CI: 0.79–0.96). The automated fascicle tracking method presented here is a robust, reliable, and time-efficient alternative to the manual analysis of muscle fascicle length during gait.

In the past 20 years, the use of ultrasound-based methods has become a standard approach to measure tendon mechanical properties in vivo. Yet the multitude of methodological approaches adopted by various research groups probably contribute to the large variability of reported values. The technique of obtaining and relating tendon deformation to tensile force in vivo has been applied differently, depending on practical constraints or scientific points of view. Divergence can be seen in 1) methodological considerations, such as the choice of anatomical features to scan and to track, force measurements, or signal synchronization; and 2) in physiological considerations related to the viscoelastic behavior or length measurements of tendons. Hence, the purpose of the present review is to assess and discuss the physiological and technical aspects connected to in vivo testing of tendon mechanical properties. In doing so, our aim is to provide the reader with a qualitative analysis of ultrasound-based techniques. Finally, a list of recommendations is proposed for a number of selected issues.

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called "pose estimation" algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires "training" images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab.

Background: a fall occurs when an individual experiences a loss of balance from which they are unable to recover. Assessment of balance recovery ability in older adults may therefore help to identify individuals at risk of falls. The purpose of this 12-month prospective study was to assess whether the ability to recover from a forward loss of balance with a single step across a range of lean magnitudes was predictive of falls. Methods: two hundred and one community-dwelling older adults, aged 65–90 years, underwent baseline testing of sensori-motor function and balance recovery ability followed by 12-month prospective falls evaluation. Balance recovery ability was defined by whether participants required either single or multiple steps to recover from forward loss of balance from three lean magnitudes, as well as the maximum lean magnitude participants could recover from with a single step. Results: forty-four (22%) participants experienced one or more falls during the follow-up period. Maximal recoverable lean magnitude and use of multiple steps to recover at the 15% body weight (BW) and 25%BW lean magnitudes significantly predicted a future fall (odds ratios 1.08–1.26). The Physiological Profile Assessment, an established tool that assesses variety of sensori-motor aspects of falls risk, was also predictive of falls (Odds ratios 1.22 and 1.27, respectively), whereas age, sex, postural sway and timed up and go were not predictive. Conclusion: reactive stepping behaviour in response to forward loss of balance and physiological profile assessment are independent predictors of a future fall in community-dwelling older adults. Exercise interventions designed to improve reactive stepping behaviour may protect against future falls.