Fusion of Wearable Kinetic and Kinematic Sensors to Estimate Triceps Surae Work during Outdoor Locomotion on Slopes

Changes in Dynamic Mean Ankle Moment Arm in Unimpaired Walking Across Speeds, Ramps, and Stairs

Understanding the natural biomechanics of walking at different speeds and activities is crucial to develop effective assistive devices for persons with lower-limb impairments. While continuous measures such as joint angle and moment are well-suited for biomimetic control of robotic systems, whole-stride summary metrics are useful for describing changes across behaviors and for designing and controlling passive and semi-active devices. Dynamic mean ankle moment arm (DMAMA) is a whole-stride measure representing the moment arm of the ground reaction impulse about the ankle joint-effectively, how "forefoot-dominated" or "hindfoot-dominated" a movement is. DMAMA was developed as a target and performance metric for semi-active devices that adjust once per stride. However, for implementation in this application, DMAMA must be characterized across various activities in unimpaired individuals. In our study, unimpaired participants walked at "slow," "normal," and "fast" self-selected speeds on level ground and at a normal self-selected speed while ascending and descending stairs and a 5-degree incline ramp. DMAMA measured from these activities displayed a borderline-significant negative sensitivity to walking speed, a significant positive sensitivity to ground incline, and a significant decrease when ascending stairs compared to descending. The data suggested a nonlinear relationship between DMAMA and walking speed; half of the participants had the highest average DMAMA at their "normal" speed. Our findings suggest that DMAMA varies substantially across activities, and thus, matching DMAMA could be a valuable metric to consider when designing biomimetic assistive lower-limb devices.

Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video

Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) ≤ 0.21 , R 2 ≥ 0.81 . Walking task resulted the most accurate with RMSE = 189±62 N; nRMSE = 0.11±0.03 , R 2 = 0.92±0.04 . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.

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.

Impact-Aware Foot Motion Reconstruction and Ramp/Stair Detection Using One Foot-Mounted Inertial Measurement Unit

Motion reconstruction using wearable sensors enables broad opportunities for gait analysis outside laboratory environments. Inertial Measurement Unit (IMU)-based foot trajectory reconstruction is an essential component of estimating the foot motion and user position required for any related biomechanics metrics. However, limitations remain in the reconstruction quality due to well-known sensor noise and drift issues, and in some cases, limited sensor bandwidth and range. In this work, to reduce drift in the height direction and handle the impulsive velocity error at heel strike, we enhanced the integration reconstruction with a novel kinematic model that partitions integration velocity errors into estimates of acceleration bias and heel strike vertical velocity error. Using this model, we achieve reduced height drift in reconstruction and simultaneously accomplish reliable terrain determination among level ground, ramps, and stairs. The reconstruction performance of the proposed method is compared against the widely used Error State Kalman Filter-based Pedestrian Dead Reckoning and integration-based foot-IMU motion reconstruction method with 15 trials from six subjects, including one prosthesis user. The mean height errors per stride are 0.03±0.08 cm on level ground, 0.95±0.37 cm on ramps, and 1.27±1.22 cm on stairs. The proposed method can determine the terrain types accurately by thresholding on the model output and demonstrates great reconstruction improvement in level-ground walking and moderate improvement on ramps and stairs.

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.

Identification of runner fatigue stages based on inertial sensors and deep learning

Introduction: Running is one of the most popular sports in the world, but it also increases the risk of injury. The purpose of this study was to establish a modeling approach for IMU-based subdivided action pattern evaluation and to investigate the classification performance of different deep models for predicting running fatigue. Methods: Nineteen healthy male runners were recruited for this study, and the raw time series data were recorded during the pre-fatigue, mid-fatigue, and post-fatigue states during running to construct a running fatigue dataset based on multiple IMUs. In addition to the IMU time series data, each participant's training level was monitored as an indicator of their level of physical fatigue. Results: The dataset was examined using single-layer LSTM (S_LSTM), CNN, dual-layer LSTM (D_LSTM), single-layer LSTM plus attention model (LSTM + Attention), CNN, and LSTM hybrid model (LSTM + CNN) to classify running fatigue and fatigue levels. Discussion: Based on this dataset, this study proposes a deep learning model with constant length interception of the raw IMU data as input. The use of deep learning models can achieve good classification results for runner fatigue recognition. Both CNN and LSTM can effectively complete the classification of fatigue IMU data, the attention mechanism can effectively improve the processing efficiency of LSTM on the raw IMU data, and the hybrid model of CNN and LSTM is superior to the independent model, which can better extract the features of raw IMU data for fatigue classification. This study will provide some reference for many future action pattern studies based on deep learning.

Adjacent tissues modulate shear wave propagation in axially loaded tendons

Shear wave tensiometry is a noninvasive approach for gauging tendon loads based on shear wave speed. Transient shear waves are induced and tracked via sensors secured to the skin overlying a superficial tendon. Wave speeds measured in vivo via tensiometry modulate with tendon load but are lower than that predicted by a tensioned beam model of an isolated tendon, which may be due to the added inertia of adjacent tissues. The objective of this study was to investigate the effects of adjacent fat tissue on shear wave propagation measurements in axially loaded tendons. We created a layered, dynamic finite element model of an elliptical tendon surrounded by subcutaneous fat. Transient shear waves were generated via an impulsive excitation delivered across the tendon or through the subcutaneous fat. The layered models demonstrated dispersive behavior with phase velocity increasing with frequency. Group shear wave speed could be ascertained via dispersion analysis or time-to-peak measures at sequential spatial locations. Simulated wave speeds in the tendon and adjacent fat were similar and modulated with tendon loading. However, wave speed magnitudes were consistently lower in the layered models than in an isolated tendon. For all models, the wave speed-stress relationship was well described by a tensioned beam model after accounting for the added inertia of the adjacent tissues. These results support the premise that externally excited shear waves are measurable in subcutaneous fat and modulate with axial loading in the underlying tendon. The model suggests that adjacent tissues add inertia to the system, which in turn lowers shear wave speeds. This information must be considered when using tensiometry as a clinical or research tool to infer absolute tendon loading.

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.

Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop

Changes in old age that contribute to the complex issue of an increased metabolic cost of walking (mass-specific energy cost per unit distance traveled) in older adults appear to center at least in part on changes in gait biomechanics. However, age-related changes in energy metabolism, neuromuscular function and connective tissue properties also likely contribute to this problem, of which the consequences are poor mobility and increased risk of inactivity-related disease and disability. The U.S. National Institute on Aging convened a workshop in September 2021 with an interdisciplinary group of scientists to address the gaps in research related to the mechanisms and consequences of changes in mobility in old age. The goal of the workshop was to identify promising ways to move the field forward toward improving gait performance, decreasing energy cost, and enhancing mobility for older adults. This report summarizes the workshop and brings multidisciplinary insight into the known and potential causes and consequences of age-related changes in gait biomechanics. We highlight how gait mechanics and energy cost change with aging, the potential neuromuscular mechanisms and role of connective tissue in these changes, and cutting-edge interventions and technologies that may be used to measure and improve gait and mobility in older adults. Key gaps in the literature that warrant targeted research in the future are identified and discussed.

Modulation of Achilles tendon force with load carriage and exosuit assistance

Exosuits have the potential to assist locomotion in both healthy and pathological populations, but the effect of exosuit assistance on the underlying muscle-tendon tissue loading is not yet understood. In this study, we used shear wave tensiometers to characterize the modulation of Achilles tendon force with load carriage and exosuit assistance at the ankle. When walking (1.25 m/s) unassisted on a treadmill with load carriage weights of 15 and 30% of body weight, peak Achilles tendon force increased by 11 and 23%, respectively. Ankle exosuit assistance significantly reduced peak Achilles tendon force relative to unassisted, although the magnitude of change was variable across participants. Peak Achilles tendon force was significantly correlated with peak ankle torque for unassisted walking across load carriage conditions. However, when ankle plantarflexor assistance was applied, the relationship between peak tendon force and peak biological ankle torque was no longer significant. An outdoor pilot study was conducted in which a wearable shear wave tensiometer was used to measure Achilles tendon wave speed and compare across an array of assistance loading profiles. Reductions in tendon loading varied depending on the profile, highlighting the importance of in vivo measurements of muscle and tendon forces when studying and optimizing exoskeletons and exosuits.

Design of Multimodal Sensor Module for Outdoor Robot Surveillance System

Recent studies on surveillance systems have employed various sensors to recognize and understand outdoor environments. In a complex outdoor environment, useful sensor data obtained under all weather conditions, during the night and day, can be utilized for application to robots in a real environment. Autonomous surveillance systems require a sensor system that can acquire various types of sensor data and can be easily mounted on fixed and mobile agents. In this study, we propose a method for modularizing multiple vision and sound sensors into one system, extracting data synchronized with 3D LiDAR sensors, and matching them to obtain data from various outdoor environments. The proposed multimodal sensor module can acquire six types of images: RGB, thermal, night vision, depth, fast RGB, and IR. Using the proposed module with a 3D LiDAR sensor, multimodal sensor data were obtained from fixed and mobile agents and tested for more than four years. To further prove its usefulness, this module was used as a monitoring system for six months to monitor anomalies occurring at a given site. In the future, we expect that the data obtained from multimodal sensor systems can be used for various applications in outdoor environments.