A Kalman Filter Approach for Estimating Tendon Wave Speed from Skin-Mounted Accelerometers

Achilles Tendon Loading during Running Estimated Via Shear Wave Tensiometry: A Step Toward Wearable Kinetic Analysis

PURPOSE

Understanding muscle-tendon forces (e.g., triceps surae and Achilles tendon) during locomotion may aid in the assessment of human performance, injury risk, and rehabilitation progress. Shear wave tensiometry is a noninvasive technique for assessing in vivo tendon forces that has been recently adapted to a wearable technology. However, previous laboratory-based and outdoor tensiometry studies have not evaluated running. This study was undertaken to assess the capacity for shear wave tensiometry to produce valid measures of Achilles tendon loading during running at a range of speeds.

METHODS

Participants walked (1.34 m·s -1 ) and ran (2.68, 3.35, and 4.47 m·s -1 ) on an instrumented treadmill while shear wave tensiometers recorded Achilles tendon wave speeds simultaneously with whole-body kinematic and ground reaction force data. A simple isometric task allowed for the participant-specific conversion of Achilles tendon wave speeds to forces. Achilles tendon forces were compared with ankle torque measures obtained independently via inverse dynamics analyses. Differences in Achilles tendon wave speed, Achilles tendon force, and ankle torque across walking and running speeds were analyzed with linear mixed-effects models.

RESULTS

Achilles tendon wave speed, Achilles tendon force, and ankle torque exhibited similar temporal patterns across the stance phase of walking and running. Significant monotonic increases in peak Achilles tendon wave speed (56.0-83.8 m·s -1 ), Achilles tendon force (44.0-98.7 N·kg -1 ), and ankle torque (1.72-3.68 N·m·(kg -1 )) were observed with increasing locomotion speed (1.34-4.47 m·s -1 ). Tensiometry estimates of peak Achilles tendon force during running (8.2-10.1 body weights) were within the range of those estimated previously via indirect methods.

CONCLUSIONS

These results set the stage for using tensiometry to evaluate Achilles tendon loading during unobstructed athletic movements, such as running, performed in the field.

Regional shear wave speeds track regional axial stress in nonuniformly loaded fibrous soft tissues

Ligaments and tendons undergo nonuniform deformation during movement. While deformations can be imaged, it remains challenging to use such information to infer regional tissue loading. Shear wave tensiometry is a promising noninvasive technique to gauge axial stress and is premised on a tensioned beam model. However, it is unknown whether tensiometry can predict regional stress in a nonuniformly loaded structure. The objectives of this study were to (1) determine whether regional shear wave speed tracks regional axial stress in nonuniformly loaded fibrous soft tissues, and (2) determine the sensitivity of regional axial stress and shear wave speed to nonuniform load distribution and fiber alignment. We created a representative set of 12,000 dynamic finite element models of a fibrous soft tissue with probabilistic variations in fiber alignment, stiffness, and aspect ratio. In each model, we applied a randomly selected nonuniform load distribution, and then excited a shear wave and tracked its regional propagation. We found that regional shear wave speed was an excellent predictor of the regional axial stress (RMSE = 0.57 MPa) and that the nature of the regional shear wave speed-stress relationship was consistent with a tensioned beam model (R2 = 0.99). Variations in nonuniform load distribution and fiber alignment did not substantially alter the wave speed-stress relationship, particularly at higher loads. Thus, these findings suggests that shear wave tensiometry could provide a quantitative estimate of regional tissue stress in ligaments and tendons.

A Single-Sensor Approach for Noninvasively Tracking Phase Velocity in Tendons during Dynamic Movement

Shear wave tensiometry is a noninvasive method for directly measuring wave speed as a proxy for force in tendons during dynamic activities. Traditionally, tensiometry has used broadband excitation pulses and measured the wave travel time between two sensors. In this work, we demonstrate a new method for tracking phase velocity using shaped excitations and measurements from a single sensor. We observed modulation of phase velocity in the Achilles tendon that was generally consistent with wave speed measures obtained via broadband excitation. We also noted a frequency dependence of phase velocity, which is expected for dispersive soft tissues. The implementation of this method could enhance the use of noninvasive wave speed measures to characterize tendon forces. Further, the approach allows for the design of smaller shear wave tensiometers usable for a broader range of tendons and applications.

Wearable sensing for understanding and influencing human movement in ecological contexts

Wearable sensors offer a unique opportunity to study movement in ecological contexts - that is, outside the laboratory where movement happens in ordinary life. This article discusses the purpose, means, and impact of using wearable sensors to assess movement context, kinematics, and kinetics during locomotion, and how this information can be used to better understand and influence movement. We outline the types of information wearable sensors can gather and highlight recent developments in sensor technology, data analysis, and applications. We close with a vision for important future research and key questions the field will need to address to bring the potential benefits of wearable sensing to fruition.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

A trained neural network model accurately predicts Achilles tendon stress during walking and running based on shear wave propagation

Shear wave tensiometry is a noninvasive technique for measuring tendon loading during activity based on the speed of a shear wave traveling along the tendon. Shear wave speed has been shown to modulate with axial stress, but calibration is required to obtain absolute measures of tendon loading. However, the current technique only makes use of wave speed, whereas other characteristics of the wave (e.g., amplitude, frequency content) may also vary with tendon loading. It is possible that these data could be used in addition to wave speed to circumvent the need for calibration. Given the potential complex relationships to tendon loading, and the lack of an analytical model to guide the use of these data, it is sensible to use a machine learning approach. Here, we used an ensemble neural network approach to predict inverse dynamics estimates of Achilles tendon stress from shear wave tensiometry data collected in a prior study. Neural network-predicted stresses were highly correlated with stance phase inverse dynamics estimates for walking (R2 = 0.89 ± 0.06) and running (R2 = 0.87 ± 0.11) data reserved for neural network model testing and not included in model training. Additionally, error between neural network-predicted and inverse dynamics-estimated stress was reasonable (walking: RMSD = 11 ± 2% of peak load; running: 25 ± 14%). Results of this pilot analysis suggest that a machine learning approach could reduce the reliance of shear wave tensiometry on calibration and expand its usability in many settings.