Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences

Understanding individuals' distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that "gait signatures" describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.e., 2D) joint angles. Here we characterized changes in gait signatures across a wide range of speeds, from very slow (0.3 m/s) to exceptionally fast (above the walk-to-run transition speed) in 17 able-bodied young adults. We further assessed whether 3D kinematic and/or kinetic (ground reaction forces, joint moments, and powers) data would improve the discrimination of gait signatures. Our study showed that gait signatures remained individual-specific across walking speeds: Notably, 3D kinematic signatures achieved exceptional accuracy (99.8%, confidence interval (CI): 99.1-100%) in classifying individuals, surpassing both 2D kinematics and 3D kinetics. Moreover, participants exhibited consistent, predictable linear changes in their gait signatures across the entire speed range. These changes were associated with participants' preferred walking speeds, balance ability, cadence, and step length. These findings support gait signatures as a tool to characterize individual differences in gait and predict speed-induced changes in gait dynamics.

Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques

The biomechanical-model-based approach with a contact model offers advantages in estimating ground reaction forces (GRFs) and ground reaction moments (GRMs), as it does not rely on the need for training data and gait assumptions. However, this approach faces the challenge of long computational times due to the inclusion of optimization processes. To address this challenge, the present study developed a new optical motion capture (OMC)-based method to estimate GRFs, GRMs, and joint torques without prolonged computational times. The proposed approach performs the estimation process by distributing external forces, as determined by a multibody model, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on optimization techniques. In this study, prediction accuracies during level walking were confirmed by comparing a general analysis using a force plate with the estimation results. The comparison revealed excellent or strong correlations between the prediction and the measurements for all GRFs, GRMs, and lower-limb-joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, contributing to its increased applicability in clinical settings.

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), a constituting component of jumps, through a biomechanical model-based approach allows for analysis, even in environments where force plates or machine learning training data would be impossible. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs), and GRFs and body loads were estimated using a 3D forward dynamics model. Our forward dynamics and optimization calculation-based estimation method generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of dynamic acceleration in the optimization calculation, we estimated the 3D orientation using sensor fusion, comprising acceleration and angular velocity data from IMUs and an extended Kalman filter. As a result, by generating cost function-based movements, we could calculate biomechanically valid GRFs while following the measured movements, even if not all joints were covered by IMUs. The estimation approach we developed in this study allows for measurement condition- or training data-independent 3D motion analysis.

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review

After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.

The virtual pivot point concept improves predictions of ground reaction forces

Ground reaction forces (GRFs) are essential for the analysis of human movement. To measure GRFs, 3D force plates that are fixed to the floor are used with large measuring ranges, excellent accuracy and high sample frequency. For less dynamic movements, like walking or squatting, portable 3D force plates are used, while if just the vertical component of the GRFs is of interest, pressure plates or in-shoe pressure measurements are often preferred. In many cases, however, it is impossible to measure 3D GRFs, e.g., during athletic competitions, at work or everyday life. It is still challenging to predict the horizontal components of the GRFs from kinematics using biomechanical models. The virtual pivot point (VPP) concept states that measured GRFs during walking intercept in a point located above the center of mass, while during running, the GRFs cross each other at a point below the center of mass. In the present study, this concept is used to compare predicted GRFs from measured kinematics with measured 3D-GRFs, not only during walking but also during more static movements like squatting and inline lunge. To predict the GRFs a full-body biomechanical model was used while gradually changing the positions of the VPP. It is shown that an optimal VPP improves the prediction of GRFs not only for walking but also for inline lunge and squats.

Markerless motion capture provides accurate predictions of ground reaction forces across a range of movement tasks

Measuring or estimating the forces acting on the human body during movement is critical for determining the biomechanical aspects relating to injury, disease and healthy ageing. In this study we examined whether quantifying whole-body motion (segmental accelerations) using a commercial markerless motion capture system could accurately predict three-dimensional ground reaction force during a diverse range of human movements: walking, running, jumping and cutting. We synchronously recorded 3D ground reaction forces (force instrumented treadmill or in-ground plates) with high-resolution video from eight cameras that were spatially calibrated relative to a common coordinate system. We used a commercially available software to reconstruct whole body motion, along with a geometric skeletal model to calculate the acceleration of each segment and hence the whole-body centre of mass and ground reaction force across each movement task. The average root mean square difference (RMSD) across all three dimensions and all tasks was 0.75 N/kg, with the maximum average RMSD being 1.85 N/kg for running vertical force (7.89 % of maximum). There was very strong agreement between peak forces across tasks, with R2 values indicating that the markerless prediction algorithm was able to predict approximately 95-99 % of the variance in peak force across all axes and movements. The results were comparable to previous reports using whole-body marker-based approaches and hence this provides strong proof-of-principle evidence that markerless motion capture can be used to predict ground reaction forces and therefore potentially assess movement kinetics with limited requirements for participant preparation.

A 0.05 m Change in Inertial Measurement Unit Placement Alters Time and Frequency Domain Metrics during Running

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

Comparison of Two Contact Detection Methods for Ground Reaction Forces and Moment Estimation During Sidestep Cuts, Runs, and Walks

Force platforms often limit the analysis of human movement to the laboratory. Promising methods for estimating ground reaction forces and moments (GRF&M) can overcome this limitation. The most effective family of methods consists of minimizing a cost, constrained by the subject's dynamic equilibrium, for distributing the force over the contact surface on the ground. The detection of contact surfaces over time is dependent on numerous parameters. This study proposes to evaluate two contact detection methods: the first based on foot kinematics and the second based on pressure sole data. Optimal parameters for these two methods were identified for walking, running, and sidestep cut tasks. The results show that a single threshold in position or velocity is sufficient to guarantee a good estimate. Using pressure sole data to detect contact improves the estimation of the position of the center of pressure (CoP). Both methods demonstrated a similar level of accuracy in estimating ground reaction forces.

Ground Contact Force and Moment Estimation for Human-Exoskeleton Systems Using Dynamic Decoupled Coordinate System and Minimum Energy Hypothesis

Estimating the contact forces and moments (CFMs) between exoskeletons' feet and the ground is a prerequisite for calculating exoskeletons' joint moments. However, comfortable, portable, and high-precision force sensors for CFM detection are difficult to design and manufacture. In addition, there are many unknown CFM components (six force components and six moment components in the double-support phase). These reasons make it challenging to estimate CFMs precisely. In this paper, we propose a novel method for estimating these CFMs based on a proposed dynamic decoupled coordinate system (DDCS) and the minimum energy hypothesis. By decomposing these CFMs into a DDCS, the number of unknowns can be significantly reduced from twelve to two. Meanwhile, the minimum energy hypothesis provides a relatively reliable target for optimizing the remaining two unknown variables. We verify the accuracy of this method using a public data set about human walking. The validation shows that the proposed method is capable of estimating CFMs. This study provides a practical way to estimate the CFMs under the soles, which contributes to reducing the research and development costs of exoskeletons by avoiding the need for expensive plantar sensors. The sensor-free approach also reduces the dependence on high-precision, portable, and comfortable CFM detection sensors, which are usually difficult to design.

Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson's r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.

Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

Acceleration-Based Estimation of Vertical Ground Reaction Forces during Running: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns

Twenty-seven methods of estimating vertical ground reaction force first peak, loading rate, second peak, average, and/or time series from a single wearable accelerometer worn on the shank or approximate center of mass during running were compared. Force estimation errors were quantified for 74 participants across different running surfaces, speeds, and foot strike angles and biases, repeatability coefficients, and limits of agreement were modeled with linear mixed effects to quantify the accuracy, reliability, and precision. Several methods accurately and reliably estimated the first peak and loading rate, however, none could do so precisely (the limits of agreement exceeded ±65% of target values). Thus, we do not recommend first peak or loading rate estimation from accelerometers with the methods currently available. In contrast, the second peak, average, and time series could all be estimated accurately, reliably, and precisely with several different methods. Of these, we recommend the 'Pogson' methods due to their accuracy, reliability, and precision as well as their stability across surfaces, speeds, and foot strike angles.

OpenCap: Human movement dynamics from smartphone videos

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future

Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.

Estimating 3D ground reaction forces in running using three inertial measurement units

To understand the mechanisms causing running injuries, it is crucial to get insights into biomechanical loading in the runners' environment. Ground reaction forces (GRFs) describe the external forces on the body during running, however, measuring these forces is usually only possible in a gait laboratory. Previous studies show that it is possible to use inertial measurement units (IMUs) to estimate vertical forces, however, forces in anterior-posterior direction play an important role in the push-off. Furthermore, to perform an inverse dynamics approach, for modelling tissue specific loads, 3D GRFs are needed as input. Therefore, the goal of this work was to estimate 3D GRFs using three inertial measurement units. Twelve rear foot strike runners did nine trials at three different velocities (10, 12 and 14 km/h) and three stride frequencies (preferred and preferred ± 10%) on an instrumented treadmill. Then, data from IMUs placed on the pelvis and lower legs were used as input for artificial neural networks (ANNs) to estimate 3D GRFs. Additionally, estimated vertical GRF from a physical model was used as input to create a hybrid machine learning model. Using different splits in validation and training data, different ANNs were fitted and assembled into an ensemble model. Leave-one-subject-out cross-validation was used to validate the models. Performance of the machine learning, hybrid machine learning and a physical model were compared. The estimated vs. measured GRF for the hybrid model had a RMSE normalized over the full range of values of 10.8, 7.8 and 6.8% and a Pearson correlation coefficient of 0.58, 0.91, 0.97 for the mediolateral direction, posterior-anterior and vertical direction respectively. Performance for the three compared models was similar. The ensemble models showed higher model accuracy compared to the ensemble-members. This study is the first to estimate 3D GRF during continuous running from IMUs and shows that it is possible to estimate GRF in posterior-anterior and vertical direction, making it possible to estimate these forces in the outdoor setting. This step towards quantification of biomechanical load in the runners' environment is helpful to gain a better understanding of the development of running injuries.

Fusion of video and inertial sensing data via dynamic optimization of a biomechanical model

Inertial sensing and computer vision are promising alternatives to traditional optical motion tracking, but until now these data sources have been explored either in isolation or fused via unconstrained optimization, which may not take full advantage of their complementary strengths. By adding physiological plausibility and dynamical robustness to a proposed solution, biomechanical modeling may enable better fusion than unconstrained optimization. To test this hypothesis, we fused video and inertial sensing data via dynamic optimization with a nine degree-of-freedom model and investigated when this approach outperforms video-only, inertial-sensing-only, and unconstrained-fusion methods. We used both experimental and synthetic data that mimicked different ranges of video and inertial measurement unit (IMU) data noise. Fusion with a dynamically constrained model significantly improved estimation of lower-extremity kinematics over the video-only approach and estimation of joint centers over the IMU-only approach. It consistently outperformed single-modality approaches across different noise profiles. When the quality of video data was high and that of inertial data was low, dynamically constrained fusion improved estimation of joint kinematics and joint centers over unconstrained fusion, while unconstrained fusion was advantageous in the opposite scenario. These findings indicate that complementary modalities and techniques can improve motion tracking by clinically meaningful margins and that data quality and computational complexity must be considered when selecting the most appropriate method for a particular application.

Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis

Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.

Can static optimization detect changes in peak medial knee contact forces induced by gait modifications?

Medial knee contact force (MCF) is related to the pathomechanics of medial knee osteoarthritis. However, MCF cannot be directly measured in the native knee, making it difficult for therapeutic gait modifications to target this metric. Static optimization, a musculoskeletal simulation technique, can estimate MCF, but there has been little work validating its ability to detect changes in MCF induced by gait modifications. In this study, we quantified the error in MCF estimates from static optimization compared to measurements from instrumented knee replacements during normal walking and seven different gait modifications. We then identified minimum magnitudes of simulated MCF changes for which static optimization correctly identified the direction of change (i.e., whether MCF increased or decreased) at least 70% of the time. A full-body musculoskeletal model with a multi-compartment knee and static optimization was used to estimate MCF. Simulations were evaluated using experimental data from three subjects with instrumented knee replacements who walked with various gait modifications for a total of 115 steps. Static optimization underpredicted the first peak (mean absolute error = 0.16 bodyweights) and overpredicted the second peak (mean absolute error = 0.31 bodyweights) of MCF. Average root mean square error in MCF over stance phase was 0.32 bodyweights. Static optimization detected the direction of change with at least 70% accuracy for early-stance reductions, late-stance reductions, and early-stance increases in peak MCF of at least 0.10 bodyweights. These results suggest that a static optimization approach accurately detects the direction of change in early-stance medial knee loading, potentially making it a valuable tool for evaluating the biomechanical efficacy of gait modifications for knee osteoarthritis.

A Wearable Real-time Kinematic and Kinetic Measurement Sensor Setup for Human Locomotion

Current laboratory-based setups (optical marker cameras + force plates) for human motion measurement require participants to stay in a constrained capture region which forbids rich movement types. This study established a fully wearable system, based on commercially available sensors (inertial measurement units + pressure insoles) that can measure both kinematic and kinetic motion data simultaneously and support wireless frame-by-frame streaming. In addition, its capability and accuracy were tested against a conventional laboratory-based setup. An experiment was conducted, with 9 participants wearing the wearable measurement system and performing 13 daily motion activities, from slow walking to fast running, together with vertical jump, squat, lunge and single-leg landing, inside the capture space of the laboratory-based motion capture system. The recorded sensor data were post-processed to obtain joint angles, ground reaction forces (GRFs), and joint torques (via multi-body inverse dynamics). Compared to the laboratory-based system, the established wearable measurement system can measure accurate information of all lower limb joint angles (Pearson's r = 0.929), vertical GRFs (Pearson's r = 0.954), and ankle joint torques (Pearson's r = 0.917). Center of pressure (CoP) in the anterior-posterior direction and knee joint torques were fairly matched (Pearson's r = 0.683 and 0.612, respectively). Calculated hip joint torques and measured medial-lateral CoP did not match with the laboratory-based system (Pearson's r = 0.21 and 0.47, respectively). Furthermore, both raw and processed datasets are openly accessible (https://doi.org/10.5281/zenodo.6457662). Documentation, data processing codes, and guidelines to establish the real-time wearable kinetic measurement system are also shared (https://github.com/HuaweiWang/WearableMeasurementSystem).

The Relationship Between the Effects of Lateral Wedge Insoles and Kinematic Chain Dynamics Between the Hindfoot and Lower Leg in Patients With Osteoarthritis of the Knee

Purpose We aim to determine whether kinematic chain dynamics of the hindfoot and lower leg are involved in the effect of a lateral wedge insole (LWI) on reducing lateral thrust among patients with medial compartment knee osteoarthritis (KOA). Participants and methods Eight patients with knee osteoarthritis were included in the study. Evaluation of the kinematic chain and gait analysis was performed using an inertial measurement unit (IMU). The dynamics of the kinematic chain were calculated as linear regression coefficients of the external rotation angle of the lower leg relative to the inversion angle of the hindfoot during repeated inversion and eversion of the foot in the standing position (kinematic chain ratio (KCR)). Walk tests were performed under four conditions: barefoot (BF), neutral insole (NI) with an incline of 0 degrees, and LWI with an incline of approximately 5 and 10 degrees (5LWI and 10LWI, respectively). Results The mean (± standard deviation (SD)) KCR was 1.4 ± 0.5. The KCR was significantly correlated with the change in 5LWI lateral thrust acceleration relative to BF (r = 0.74). A significant correlation was also observed between changes in the hindfoot evolution angle and lower leg internal rotation angle with a 10LWI with respect to BF and NI, and changes in lateral thrust acceleration. Conclusion The results of this study suggest that the kinematic chain is involved in the effects of an LWI in patients with knee osteoarthritis.

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

Neuromusculoskeletal models often require three-dimensional (3D) body motions, ground reaction forces (GRF), and electromyography (EMG) as input data. Acquiring these data in real-world settings is challenging, with barriers such as the cost of instruments, setup time, and operator skills to correctly acquire and interpret data. This study investigated the consequences of limiting EMG and GRF data on modelled anterior cruciate ligament (ACL) forces during a drop–land–jump task in late-/post-pubertal females. We compared ACL forces generated by a reference model (i.e., EMG-informed neural mode combined with 3D GRF) to those generated by an EMG-informed with only vertical GRF, static optimisation with 3D GRF, and static optimisation with only vertical GRF. Results indicated ACL force magnitude during landing (when ACL injury typically occurs) was significantly overestimated if only vertical GRF were used for either EMG-informed or static optimisation neural modes. If 3D GRF were used in combination with static optimisation, ACL force was marginally overestimated compared to the reference model. None of the alternative models maintained rank order of ACL loading magnitudes generated by the reference model. Finally, we observed substantial variability across the study sample in response to limiting EMG and GRF data, indicating need for methods incorporating subject-specific measures of muscle activation patterns and external loading when modelling ACL loading during dynamic motor tasks.

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Introduction

Gait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.

Methods

The method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.

Results

The results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71–0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.

Conclusion

To conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

Inertial Sensor Location for Ground Reaction Force and Gait Event Detection Using Reservoir Computing in Gait

Inertial measurement units (IMUs) have shown promising outcomes for estimating gait event detection (GED) and ground reaction force (GRF). This study aims to determine the best sensor location for GED and GRF prediction in gait using data from IMUs for healthy and medial knee osteoarthritis (MKOA) individuals. In this study, 27 healthy and 18 MKOA individuals participated. Participants walked at different speeds on an instrumented treadmill. Five synchronized IMUs (Physilog®, 200 Hz) were placed on the lower limb (top of the shoe, heel, above medial malleolus, middle and front of tibia, and on medial of shank close to knee joint). To predict GRF and GED, an artificial neural network known as reservoir computing was trained using combinations of acceleration signals retrieved from each IMU. For GRF prediction, the best sensor location was top of the shoe for 72.2% and 41.7% of individuals in the healthy and MKOA populations, respectively, based on the minimum value of the mean absolute error (MAE). For GED, the minimum MAE value for both groups was for middle and front of tibia, then top of the shoe. This study demonstrates that top of the shoe is the best sensor location for GED and GRF prediction.

Real-Time Human Motion Tracking by Tello EDU Drone

Human movement tracking is useful in a variety of areas, such as search-and-rescue activities. CCTV and IP cameras are popular as front-end sensors for tracking human motion; however, they are stationary and have limited applicability in hard-to-reach places, such as those where disasters have occurred. Using a drone to discover a person is challenging and requires an innovative approach. In this paper, we aim to present the design and implementation of a human motion tracking method using a Tello EDU drone. The design methodology is carried out in four steps: (1) control panel design; (2) human motion tracking algorithm; (3) notification systems; and (4) communication and distance extension. Intensive experimental results show that the drone implemented by the proposed algorithm performs well in tracking a human at a distance of 2-10 m moving at a speed of 2 m/s. In an experimental field of the size 95×35m2, the drone tracked human motion throughout a whole day, with the best tracking results observed in the morning. The drone was controlled from a laptop using a Wi-Fi router with a maximum horizontal tracking distance of 84.30 m and maximum vertical distance of 13.40 m. The experiment showed an accuracy rate for human movement detection between 96.67 and 100%.

Estimation of Lower Extremity Muscle Activity in Gait Using the Wearable Inertial Measurement Units and Neural Network

The inertial measurement unit (IMU) has become more prevalent in gait analysis. However, it can only measure the kinematics of the body segment it is attached to. Muscle behaviour is an important part of gait analysis and provides a more comprehensive overview of gait quality. Muscle behaviour can be estimated using musculoskeletal modelling or measured using an electromyogram (EMG). However, both methods can be tasking and resource intensive. A combination of IMU and neural networks (NN) has the potential to overcome this limitation. Therefore, this study proposes using NN and IMU data to estimate nine lower extremity muscle activities. Two NN were developed and investigated, namely feedforward neural network (FNN) and long short-term memory neural network (LSTM). The results show that, although both networks were able to predict muscle activities well, LSTM outperformed the conventional FNN. This study confirms the feasibility of estimating muscle activity using IMU data and NN. It also indicates the possibility of this method enabling the gait analysis to be performed outside the laboratory environment with a limited number of devices.

Open-source software library for real-time inertial measurement unit data-based inverse kinematics using OpenSim

Background

Inertial measurements (IMUs) facilitate the measurement of human motion outside the motion laboratory. A commonly used open-source software for musculoskeletal simulation and analysis of human motion, OpenSim, includes a tool to enable kinematics analysis of IMU data. However, it only enables offline analysis, i.e., analysis after the data has been collected. Extending OpenSim's functionality to allow real-time kinematics analysis would allow real-time feedback for the subject during the measurement session and has uses in e.g., rehabilitation, robotics, and ergonomics.

Methods

We developed an open-source software library for real-time inverse kinematics (IK) analysis of IMU data using OpenSim. The software library reads data from IMUs and uses multithreading for concurrent calculation of IK. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. We published the code under an open-source license on GitHub.

Results

A standard desktop computer calculated full-body inverse kinematics from treadmill walking at 1.5 m/s with data from 12 IMUs in real-time with a mean delay below 55 ms and reached a throughput of more than 90 samples per second. A laptop computer had similar delays and reached a throughput above 60 samples per second with treadmill walking. Minimal walking kinematics, motion of lower extremities and torso, were calculated from treadmill walking data in real-time with a throughput of 130 samples per second on the laptop and 180 samples per second on the desktop computer, with approximately half the delay of full-body kinematics.

Conclusions

The software library enabled real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models. The performance results show that subject-specific full-body motion analysis is feasible in real-time, while a laptop computer and IMUs allowed the use of the method outside the motion laboratory.

Estimation of the knee joint load using plantar pressure data measured by smart socks: A feasibility study

BACKGROUND

Unsupervised sports activities could cause traumas, about 70% of them are those of the low extremities. To avoid traumas, the athlete should be aware of dangerous forces acting within low extremity joints. Research in gait analysis indicated that plantar pressure alteration rate correlates with the gait pace. Thus, the changes in plantar pressure should correlate with the accelerations of extremities, and with the forces, acting in the joints. Smart socks provide a budget solution for the measurement of plantar pressure.

OBJECTIVE

To estimate the correlation between the plantar pressure, measured using smart socks, and forces, acting in the joints of the lower extremities.

METHODS

The research is case study based. The volunteer performed a set of squats. The arbitrary plantar pressure-related data were obtained using originally developed smart socks with embedded knitted pressure sensors. Simultaneously, the lower extremity motion data were recorded using two inertial measurement units, attached to the tight and the ankle, from which the forces acted in the knee joint were estimated. The simplest possible model of knee joint mechanics was used to estimate force.

RESULTS

The estimates of the plantar pressure and knee joint forces demonstrate a strong correlation (r= 0.75, P< 0.001). The established linear regression equation enables the calculation of the knee joint force with an uncertainty of 22% using the plantar pressure estimate. The accuracy of the classification of the joint force as excessive, i.e., being more than 90% of the maximal force, was 82%.

CONCLUSION

The results demonstrate the feasibility of the smart socks for the estimation of the forces in the knee joints. Smart socks therefore could be used to develop excessive joint force alert devices, that could replace less convenient inertial sensors.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies

Background

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait.

Research objective

This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms.

Methods

A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included.

Results and significance

Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Biomechanical estimation of tennis serve using inertial sensors: A case study

Inertial measurement units may provide a relevant on-court 3-Dimension measurement system for tennis serve biomechanical analysis. Therefore, this case study aimed to report the feasibility of inertial measurement unit's kinematic and kinetic data collection during tennis serve. Two injury-free highly-trained tennis players were equipped with the inertial measurement unit (Xsens MVN suit) and performed 2 trials of five flat “first” serves on a 1 m2 target zone bordering the service box of an indoor GreenSet® tennis court surface. With the exception of the center of gravity rotation at the loading stage, all joint (shoulder, elbow, knee) angles, center of mass displacements and rotations followed a similar development for both female and male participants from loading to finish stages. At ball contact stage, articular moments (mid-trunk, upper-trunk, shoulder, elbow, wrist) and segmental contribution (pelvis linear, pelvis rotation, trunk, shoulder, elbow, wrist) repartitions also showed a comparable movement. From loading to finish stages, total, lower and upper energy contribution were similar for both players, with coefficient of variations deemed acceptable between the two trials. This inertial measurement unit appears suitable for on-court tennis serve biomechanical data collection and subsequent analysis to provide tennis players and practitioners tailored feedbacks to facilitate motor learning process and develop serve efficiency.

Synthesising 2D Video from 3D Motion Data for Machine Learning Applications

To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11−3.49 mm). We further demonstrated the utility of the method in a downstream machine learning use-case in which we trained and then tested the validity of an artificial neural network (ANN) to estimate ground reaction forces (GRFs) using synthesised and real 2D videos. Training an ANN to estimate GRFs using eight OpenPose keypoints derived from synthesised 2D videos resulted in accurate waveform GRF estimations (r > 0.9; nRMSE < 14%). When compared with using the smaller number of real videos only, accuracy was improved by adding the synthetic views and enlarging the dataset. The results highlight the utility of the developed approach to enlarge small 2D video datasets, or to create 2D video images to accompany 3D motion capture datasets to make them accessible for machine learning applications.

Inertial Motion Capture-Based Estimation of L5/S1 Moments during Manual Materials Handling

Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies in the workplace. Because of the need to measure contact forces, most of these in situ studies are limited to a kinematic analysis, such as posture or working technique analysis. This paper aims to develop and evaluate an IMC-based approach to estimate back loading during manual material handling (MMH) tasks. During various representative workplace MMH tasks performed by nine participants, this approach was evaluated by comparing the results with the ones computed from optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found between both computations on indicators based on L5/S1 peak and cumulative flexion moments, while lower correlations were found on indicators based on asymmetric moments. Since no force measurement or load kinematics measurement is needed, this study shows the potential of using only the handler's kinematics measured by IMC to estimate kinetics variables. The assessment of workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics evaluation and will improve the ecological validity compared to laboratory studies, where the situations are often simplified and standardized.

Model-Based Acetabular Cup Orientation Optimization Based On Minimizing the Risk of Edge-Loading and Implant Impingement Following Total Hip Arthroplasty

In this paper, a computationally-efficient model-based method for determining patient-specific optimal acetabular cup alignment for total hip arthroplasty (THA) is presented. The proposed algorithm minimizes the risk of implant impingement and edge-loading, which are reported as the major causes of hip dislocation following THA. First, by using motion capture data recorded from the patient performing different daily activities, the hip contact force and the relative orientation of the femur and pelvis are calculated by a musculoskeletal model. Then, by defining two quantitative indices i.e., angular impingement distance and angular edge-loading distance, the risk of impingement and edge-loading are assessed for a wide range of cup alignments. And finally, three optimization criteria are introduced to estimate the optimal cup alignment with a tradeoff between the risk of impingement and edge-loading. The results show that patient-specific characteristics such as pelvic tilt could significantly change the optimal cup alignment, especially the value of cup anteversion. Therefore, in some cases, the well-known Lewinnek safe zone may not be optimal, or even safe. Unlike other dynamic model-based methods, in this work, the need for force plate measurements is eliminated by estimating the ground reaction forces and moments, which makes this method more practical and cost-efficient. Furthermore, the low computational complexity due to analytical formulas makes this method suitable for both preoperative and intra-operative planning.

Motion-Based Ground Reaction Forces and Moments Prediction Method for Interaction with a Moving And/Or Non-Horizontal Structure

Inverse dynamics methods are commonly used for the biomechanical analysis of human motion. External forces applied on the subject are required as an input data to solve the dynamic equilibrium of the subject. Force platforms measure ground reactions forces and moments (GRF&Ms) but they limit the ecological aspect of experimental conditions. Motion-based GRF&Ms prediction may circumvent this limitation. The current study aims at evaluating the accuracy of an optimization-based GRF&Ms prediction method modified to be applied to the interaction with a moving and/or non horizontal structure. The main improvement of the method deals with the contact detection in such a moving and/or non horizontal frame. To evaluate the accuracy of the method, 20 subjects performed squats and steps on an instrumented moving structure, measuring both motion and GRF&Ms. The comparison of the root mean square error between the predicted and measured GFR&Ms divided by the subjects mass showed a similar order of magnitude than those from the method without the studied modification (0.14 N/kg for antero-posterior forces, 0.29 N/kg for medio lateral forces, 0.61 N/kg for longitudinal forces, 0.06 Nm/kg for frontal moments, 0.13 Nm/kg for sagittal moments, and 0.03 Nm/kg for transverse moments). The results showed the suitability of the method to study human motions for tasks performed on a moving and/or non-horizontal structure.

Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review

With the emergence of wearable technology and machine learning approaches, gait monitoring in real-time is attracting interest from the sports biomechanics community. This study presents a systematic review of machine learning approaches in running biomechanics using wearable sensors. Electronic databases were retrieved in PubMed, Web of Science, SPORTDiscus, Scopus, IEEE Xplore, and ScienceDirect. A total of 4,068 articles were identified via electronic databases. Twenty-four articles that met the eligibility criteria after article screening were included in this systematic review. The range of quality scores of the included studies is from 0.78 to 1.00, with 40% of articles recruiting participant numbers between 20 and 50. The number of inertial measurement unit (IMU) placed on the lower limbs varied from 1 to 5, mainly in the pelvis, thigh, distal tibia, and foot. Deep learning algorithms occupied 57% of total machine learning approaches. Convolutional neural networks (CNN) were the most frequently used deep learning algorithm. However, the validation process for machine learning models was lacking in some studies and should be given more attention in future research. The deep learning model combining multiple CNN and recurrent neural networks (RNN) was observed to extract different running features from the wearable sensors and presents a growing trend in running biomechanics.

Inertial Sensor-to-Segment Calibration for Accurate 3D Joint Angle Calculation for Use in OpenSim

Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R² between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.

Ability of Wearable Accelerometers-Based Measures to Assess the Stability of Working Postures

With the rapid development and widespread application of wearable inertial sensors in the field of human motion capture, the low-cost and non-invasive accelerometer (ACC) based measures have been widely used for working postural stability assessment. This study systematically investigated the abilities of ACC-based measures to assess the stability of working postures in terms of the ability to detect the effects of work-related factors and the ability to classify stable and unstable working postures. Thirty young males participated in this study and performed twenty-four load-holding tasks (six working postures × two standing surfaces × two holding loads), and forty-three ACC-based measures were derived from the ACC data obtained by using a 17 inertial sensors-based motion capture system. ANOVAs, t-tests and machine learning (ML) methods were adopted to study the factors’ effects detection ability and the postural stability classification ability. The results show that almost all forty-three ACC-based measures could (p < 0.05) detect the main effects of Working Posture and Load Carriage, and their interaction effects. However, most of them failed in (p ≥ 0.05) detecting Standing Surface’s main or interaction effects. Five measures could detect both main and interaction effects of all the three factors, which are recommended for working postural stability assessment. The performance in postural stability classification based on ML was also good, and the feature set exerted a greater influence on the classification accuracy than sensor configuration (i.e., sensor placement locations). The results show that the pelvis and lower legs are recommended locations overall, in which the pelvis is the first choice. The findings of this study have proved that wearable ACC-based measures could assess the stability of working postures, including the work-related factors’ effects detection ability and stable-unstable working postures classification ability. However, researchers should pay more attention to the measure selection, sensors placement, feature selection and extraction in practical applications.

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

Muscle–tendon power output is commonly assessed in the laboratory through the work loop, a paired analysis of muscle force and length during a cyclic task. Work-loop analysis of muscle–tendon function in out-of-lab conditions has been elusive due to methodological limitations. In this work, we combined kinetic and kinematic measures from shear wave tensiometry and inertial measurement units, respectively, to establish a wearable system for estimating work and power output from the soleus and gastrocnemius muscles during outdoor locomotion. Across 11 healthy young adults, we amassed 4777 strides of walking on slopes from −10° to +10°. Results showed that soleus work scales with incline, while gastrocnemius work is relatively insensitive to incline. These findings agree with previous results from laboratory-based studies while expanding technological capabilities by enabling wearable analysis of muscle–tendon kinetics. Applying this system in additional settings and activities could improve biomechanical knowledge and evaluation of protocols in scenarios such as rehabilitation, device design, athletics, and military training.

Performance of machine learning models in estimation of ground reaction forces during balance exergaming

Background

Balance training exercise games (exergames) are a promising tool for reducing fall risk in elderly. Exergames can be used for in-home guided exercise, which greatly increases availability and facilitates independence. Providing biofeedback on weight-shifting during in-home balance exercise improves exercise efficiency, but suitable equipment for measuring weight-shifting is lacking. Exergames often use kinematic data as input for game control. Being able to useg such data to estimate weight-shifting would be a great advantage. Machine learning (ML) models have been shown to perform well in weight-shifting estimation in other settings. Therefore, the aim of this study was to investigate the performance of ML models in estimation of weight-shifting during exergaming using kinematic data.

Methods

Twelve healthy older adults (mean age 72 (± 4.2), 10 F) played a custom exergame that required repeated weight-shifts. Full-body 3D motion capture (3DMoCap) data and standard 2D digital video (2D-DV) was recorded. Weight shifting was directly measured by 3D ground reaction forces (GRF) from force plates, and estimated using a linear regression model, a long-short term memory (LSTM) model and a decision tree model (XGBoost). Performance was evaluated using coefficient of determination (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2) and root mean square error (RMSE).

Results

Results from estimation of GRF components using 3DMoCap data show a mean (± 1SD) RMSE (% total body weight, BW) of the vertical GRF component (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_z$$\end{document}Fz) of 4.3 (2.5), 11.1 (4.5), and 11.0 (4.7) for LSTM, XGBoost and LinReg, respectively. Using 2D-DV data, LSTM and XGBoost achieve mean RMSE (± 1SD) in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_z$$\end{document}Fz estimation of 10.7 (9.0) %BW and 19.8 (6.4) %BW, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>.97$$\end{document}>.97 for the LSTM in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_z$$\end{document}Fz component using 3DMoCap data, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>.77$$\end{document}>.77 using 2D-DV data. For XGBoost, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_z$$\end{document}Fz\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2$$\end{document}R2 was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>.86$$\end{document}>.86 using 3DMoCap data, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$>.56$$\end{document}>.56 using 2D-DV data.

Conclusion

This study demonstrates that an LSTM model can estimate 3-dimensional GRF components using 2D kinematic data extracted from standard 2D digital video cameras. The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_z$$\end{document}Fz component is estimated more accurately than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_y$$\end{document}Fy and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_x$$\end{document}Fx components, especially when using 2D-DV data. Weight-shifting performance during exergaming can thus be extracted using kinematic data only, which can enable effective independent in-home balance exergaming.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Added value of motion capture technology for occupational health and safety innovations

Ergonomic principles in production assembly and manufacturing operations have become an essential part of comprehensive health and safety innovations. We aim to provide new insights into occupational health and safety innovations and how they utilise biomechanical methods and cutting-edge motion capture technology by assessing movements at a workplace. The practical goal is to quantify a connection between work exposure and ergonomic risk measures to determine biomechanical risk factors of diseases or health-related disorders objectively. The target group consisted of 62 factory employees working in manufacturing (26 participants on 12 devices) or assembly areas (36 participants on 9 devices). Body posture, body parts position, movements, energy cost and workloads were assessed using an inertial motion capture (MC) system. MC technology accurately assesses the operator’s movements. The proposed methodology could complement ergonomic procedures in the design of workstations, which is the added value of the motion capture technology for occupational health and safety innovations.

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.

Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes' performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

Inertial Motion Capture-Based Whole-Body Inverse Dynamics

Inertial Motion Capture (IMC) systems enable in situ studies of human motion free of the severe constraints imposed by Optical Motion Capture systems. Inverse dynamics can use those motions to estimate forces and moments developing within muscles and joints. We developed an inverse dynamic whole-body model that eliminates the usage of force plates (FPs) and uses motion patterns captured by an IMC system to predict the net forces and moments in 14 major joints. We validated the model by comparing its estimates of Ground Reaction Forces (GRFs) to the ground truth obtained from FPs and comparing predictions of the static model’s net joint moments to those predicted by 3D Static Strength Prediction Program (3DSSPP). The relative root-mean-square error (rRMSE) in the predicted GRF was 6% and the intraclass correlation of the peak values was 0.95, where both values were averaged over the subject population. The rRMSE of the differences between our model’s and 3DSSPP predictions of net L5/S1 and right and left shoulder joints moments were 9.5%, 3.3%, and 5.2%, respectively. We also compared the static and dynamic versions of the model and found that failing to account for body motions can underestimate net joint moments by 90% to 560% of the static estimates.

Assessment of Balance Instability by Wearable Sensor Systems During Postural Transitions

Several studies have demonstrated beneficial effects of real-time biofeedback for improving postural control. However, the application for daily activities, which also include postural transitions, is still limited. One crucial aspect is the time point of providing feedback, and thus its reliability. This might depend on the sensor system used, but also on how the threshold is defined. This study investigates which wearable sensor system and what kind of threshold is more reliable in a situation of a postural transition.To this end, we compared three sensor systems regarding their accuracy in timing in a stable and unstable postural transition in 16 healthy young adults: a multiple Inertial Measurement Unit system (IMU), a pressure Insoles System (IS), and a combination of both systems (COMB). Further, we contrasted two threshold parameters for each system: a Quiet Standing-based threshold (QSth) and a Limits of Stability-based threshold (LoSth).Two-way repeated measures ANOVAs and Wilcoxon tests (α = 0.05) indicated highest accuracy in the COMB LoSth, though with small differences to the IS LoSth. The LoSth showed more accurate timing than the QSth, especially in medio-lateral direction for IS and COMB.Consequently, for providing a reliable timing for a potential biofeedback applied by a wearable device in everyday life situations applications should focus on pressure insoles and a functional stability threshold, such as the LoS-based threshold.

Effects of lumbar spinal disorders on the vertical ground reaction force and Spatio-temporal parameters in gait

BACKGROUND

Ground reaction forces are biomechanical data, providing information to investigate pathological gait. The vertical component of ground reaction force introduces the upward thrust force within gait progression. Although alterations in the vertical component in patients with spinal disorders were addressed in the literature, still the corresponding effect on spinal disorders is a major issue to scrutiny. In this study, the effects of two different anatomical spinal disorders on the vertical component pattern were investigated.

METHODS

Two groups of patients with lumbar spine stenosis and lumbar intervertebral disc degeneration with lesions at L4-L5 and/or L5-S1 levels, were recruited. The vertical component of ground reaction force and spatio-temporal parameters were obtained and analyzed using one-way analysis of variance.

FINDINGS

The results indicated that all spatio-temporal parameters differed significantly (P < 0.05) except step lengths and stride times (P > 0.05). In a similar test, the Fz2 in patients with lumbar stenosis was higher than that of those with disc degeneration (P < 0.05). Besides, the vertical ground reaction force pattern showed lower slopes in stenosis patients.

INTERPRETATION

This study showed that the vertical component of ground reaction force alterations and spatio-temporal parameters could be employed as indicators for certain spinal lesions. The results of this study could implement as an adjunct diagnostic method to help clinicians to differentiate between stenosis and disc degeneration patients and plan for their rehabilitation purposes.

Kinetic measurement system use in individuals following anterior cruciate ligament reconstruction: a scoping review of methodological approaches

Purpose

Our primary objectives were to (1) describe current approaches for kinetic measurements in individuals following anterior cruciate ligament reconstruction (ACLR) and (2) suggest considerations for methodological reporting. Secondarily, we explored the relationship between kinetic measurement system findings and patient-reported outcome measures (PROMs).

Methods

We followed the PRISMA extension for scoping reviews and Arksey and O’Malley’s 6-stage framework. Seven electronic databases were systematically searched from inception to June 2020. Original research papers reporting parameters measured by kinetic measurement systems in individuals at least 6-months post primary ACLR were included.

Results

In 158 included studies, 7 kinetic measurement systems (force plates, balance platforms, pressure mats, force-measuring treadmills, Wii balance boards, contact mats connected to jump systems, and single-sensor insoles) were identified 4 main movement categories (landing/jumping, standing balance, gait, and other functional tasks). Substantial heterogeneity was noted in the methods used and outcomes assessed; this review highlighted common methodological reporting gaps for essential items related to movement tasks, kinetic system features, justification and operationalization of selected outcome parameters, participant preparation, and testing protocol details. Accordingly, we suggest considerations for methodological reporting in future research. Only 6 studies included PROMs with inconsistency in the reported parameters and/or PROMs.

Conclusion

Clear and accurate reporting is vital to facilitate cross-study comparisons and improve the clinical application of kinetic measurement systems after ACLR. Based on the current evidence, we suggest methodological considerations to guide reporting in future research. Future studies are needed to examine potential correlations between kinetic parameters and PROMs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40634-021-00397-0.

Echo State Networks for Estimating Exteroceptive Conditions From Proprioceptive States in Quadruped Robots

We propose a methodology based on reservoir computing for mapping local proprioceptive information acquired at the level of the leg joints of a simulated quadruped robot into exteroceptive and global information, including both the ground reaction forces at the level of the different legs and information about the type of terrain traversed by the robot. Both dynamic estimation and terrain classification can be achieved concurrently with the same reservoir computing structure, which serves as a soft sensor device. Simulation results are presented together with preliminary experiments on a real quadruped robot. They demonstrate the suitability of the proposed approach for various terrains and sensory system fault conditions. The strategy, which belongs to the class of data-driven models, is independent of the robotic mechanical design and can easily be generalized to different robotic structures.

Implementation of Kinetic and Kinematic Variables in Ergonomic Risk Assessment Using Motion Capture Simulation: A Review

Work-related musculoskeletal disorders (WMSDs) are among the most common disorders in any work sector and industry. Ergonomic risk assessment can reduce the risk of WMSDs. Motion capture that can provide accurate and real-time quantitative data has been widely used as a tool for ergonomic risk assessment. However, most ergonomic risk assessments that use motion capture still depend on the traditional ergonomic risk assessment method, focusing on qualitative data. Therefore, this article aims to provide a view on the ergonomic risk assessment and apply current motion capture technology to understand classical mechanics of physics that include velocity, acceleration, force, and momentum in ergonomic risk assessment. This review suggests that using motion capture technologies with kinetic and kinematic variables, such as velocity, acceleration, and force, can help avoid inconsistency and develop more reliable results in ergonomic risk assessment. Most studies related to the physical measurement conducted with motion capture prefer to use non-optical motion capture because it is a low-cost system and simple experimental setup. However, the present review reveals that optical motion capture can provide more accurate data.

Forces: A Motion Capture-Based Ergonomic Method for the Today's World

Approximately three of every five workers are affected by musculoskeletal disorders, especially in production environments. In this regard, workstation ergonomic evaluations are especially beneficial for conducting preventive actions. Nevertheless, today's context demonstrates that traditional ergonomic methods should lead to smart ergonomic methods. This document introduces the Forces ergonomic method, designed considering the possibilities of inertial motion capture technology and its applicability to evaluating actual workstations. This method calculates the joint risks for each posture and provides the total risk for the assessed workstation. In this calculation, Forces uses postural measurement and a kinetic estimation of all forces and torques that the joints support during movement. This paper details the method's fundamentals to achieve structural validity, demonstrating that all parts that compose it are logical and well-founded. This method aims to aid prevention technicians in focusing on what matters: making decisions to improve workers' health. Likewise, it aims to answer the current industry needs and reduce musculoskeletal disorders caused by repetitive tasks and lower the social, economic, and productivity losses that such disorders entail.

Evaluation of selected indices of gait asymmetry for the assessment of running asymmetry

BACKGROUND

The methods of running asymmetry evaluation are not as highly developed as the methods of gait evaluation.

RESEARCH QUESTION

Which asymmetry indices used in the gait analysis best characterize the asymmetry of the running movement?

METHODS

The kinematics of the sprint in a straight run over a distance of 50 m was evaluated using the X-sens system. Three indices (Ia, IS, SA) were based on discrete values from the first point of contact of the foot with the ground (1% of the running cycle phase) and were called discrete coefficients. Furthermore, two indices (SI, RAI) were used to evaluate asymmetry over the entire running cycle and were termed continuous coefficients. The study examined 21 elite and non-professional middle-distance runners of both sexes. The evaluation of the usefulness of individual indices for the evaluation of gait asymmetry was performed by means of the analysis of ROC curves and evaluation of data scatter on Bland-Altman charts.

RESULTS

The values of discrete and continuous asymmetry coefficients were different to each other. In Bland-Altman plots there was a meaningful variety of discrete coefficients and a small variety of continuous coefficients. The analysis of ROC curves proves this assumption. Including the real curve course of angular placement in particular joints it is observed that continuous coefficients describe asymmetry of movement more precisely.

SIGNIFICANCE

It was found that the so-called continuous indices SI and RAI ensure the best identification of the phenomenon of movement asymmetry.