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Sensors and Musculoskeletal Dynamics to Evaluate Human Movement

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 33443

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Special Issue Editors

Dr. Ajay Seth

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Guest Editor

Biomechanical Engineering, Delft University of Technology
Interests: Computational Biomechanics; Musculoskeletal Modeling; Motion Reconstruction; Multibody Dynamics; OpenSim

Prof. Dr. Peter Shull

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Guest Editor

Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: wearable systems using embedded electronics; real-time models; sensor fusion algorithms; novel feedback devices
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Special Issue Information

Dear Colleagues,

From inertial measurement units for sensing motion to in vivo ultrasonic measurement of wave speed to sense tendon force, sensors are the primary instruments of the movement scientist. By combining information from multiple sensors, researchers are able to form a more complete and detailed picture of human performance and further our understanding of human performance, both exceptional and pathological.

A major challenge for movement scientists, sensor researchers, and application developers, however, is to fuse multiple and potentially disparate signals from multiple sensors into a coherent, accurate, and reliable picture of performance. Implicit and explicit models have been employed to provide the context in which more accurate and reliable metrics can be estimated. For example, a model of skeletal kinematics, with its constraints on relative segment motion, is widely employed to reduce errors from motion-capture experiments and yield gait kinematics in terms of joint angles, which are not directly measured by wearable sensors.

This Special Issue on “Sensors and Musculoskeletal Dynamics to Evaluate Human Movement” aims to highlight the necessity of sensing and modeling to extract metrics of human performance in research, clinical, and sports applications. We welcome contributions that combine sensors and models to quantify and explain human performance.

Contributions that address but are not restricted to the following topics are welcome:

Wearable sensors to measure human performance in terms of:

Kinematics;
Kinetics;
Musculotendon mechanics;
Energetics and/or metabolic cost;

Neuromuscular and musculoskeletal models to estimate performance metrics from wearable sensors;
Reliability and accuracy of direct sensor measurements versus model-based estimates;
Experiments and methods to identify and quantify sensor borne errors due to noise, bias, and drift;
Improvements in sensor to model registration and calibration;
Algorithms to combine and integrate multiple sensors to extract novel measures of performance or to improve upon the accuracy and reliability of existing metrics;
Models and methods to standardize measurements and comparison of human performance across individuals (e.g., patients and/or athletes).

Submitted papers should present novel contributions. Relevant systematic and/or topical reviews are also welcome.

Dr. Ajay Seth
Dr. Peter Shull
Guest Editors

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Keywords

Biomechanics
Biomechanical sensors
Computational models
Musculoskeletal simulation
Inertial sensing
Human kinematics and kinetics
Sensor fusion
Movement disorders
Athletic performance

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Published Papers (7 papers)

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Research

21 pages, 16055 KiB

Open AccessArticle

Towards Single Camera Human 3D-Kinematics

by Marian Bittner, Wei-Tse Yang, Xucong Zhang, Ajay Seth, Jan van Gemert and Frans C. T. van der Helm

Sensors 2023, 23(1), 341; https://doi.org/10.3390/s23010341 - 28 Dec 2022

Cited by 14 | Viewed by 5012

Abstract

Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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16 pages, 75326 KiB

Open AccessArticle

Permanent Magnet Tracking Method Resistant to Background Magnetic Field for Assessing Jaw Movement in Wearable Devices

by Mantas Jucevičius, Rimantas Ožiūnas, Gintautas Narvydas and Darius Jegelevičius

Sensors 2022, 22(3), 971; https://doi.org/10.3390/s22030971 - 26 Jan 2022

Cited by 6 | Viewed by 3623

Abstract

There is a large gap between primitive bruxism detectors and sophisticated clinical machines for jaw kinematics evaluation. Large, expensive clinical appliances can precisely record jaw motion, but completely restrain the patient for the duration of the test. Wearable bruxism detectors allow continuously counting and recording bites, but provide no information about jaw movement trajectories. Previously, we developed a permanent magnet and three-axis magnetometer-based method for wearable, intra-oral continuous jaw position registration. In this work, we present an effective solution of the two main drawbacks of the method. Firstly, a two-adjacent-magnetometer approach is able to compensate for background magnetic fields with no reference sensor outside of the system’s magnetic field. Secondly, jaw rotational angles were included in the position calculations, by applying trigonometric equations that link the translation of the jaw to its rotation. This way, we were able to use a three-degree-of-freedom (3-DOF) magnetic position determination method to track the positions of the 5-DOF human masticatory system. To validate the method, finite element modeling and a 6-DOF robotic arm (0.01 mm, 0.01°) were used, which showed a 37% decrease in error in the average RMSE = 0.17 mm. The method’s potentially can be utilized in small-scale, low-power, wearable intra-oral devices for continuous jaw motion recording. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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18 pages, 1759 KiB

Open AccessArticle

Validity and Sensitivity of an Inertial Measurement Unit-Driven Biomechanical Model of Motor Variability for Gait

by Christopher A. Bailey, Thomas K. Uchida, Julie Nantel and Ryan B. Graham

Sensors 2021, 21(22), 7690; https://doi.org/10.3390/s21227690 - 19 Nov 2021

Cited by 23 | Viewed by 4765

Abstract

Motor variability in gait is frequently linked to fall risk, yet field-based biomechanical joint evaluations are scarce. We evaluated the validity and sensitivity of an inertial measurement unit (IMU)-driven biomechanical model of joint angle variability for gait. Fourteen healthy young adults completed seven-minute trials of treadmill gait at several speeds and arm swing amplitudes. Trunk, pelvis, and lower-limb joint kinematics were estimated by IMU- and optoelectronic-based models using OpenSim. We calculated range of motion (ROM), magnitude of variability (meanSD), local dynamic stability (λ_max), persistence of ROM fluctuations (DFAα), and regularity (SaEn) of each angle over 200 continuous strides, and evaluated model accuracy (RMSD: root mean square difference), consistency (ICC_2,1: intraclass correlation), biases, limits of agreement, and sensitivity to within-participant gait responses (effects of speed and swing). RMSDs of joint angles were 1.7–9.2° (pooled mean of 4.8°), excluding ankle inversion. ICCs were mostly good to excellent in the primary plane of motion for ROM and in all planes for meanSD and λ_max, but were poor to moderate for DFAα and SaEn. Modelled speed and swing responses for ROM, meanSD, and λ_max were similar. Results suggest that the IMU-driven model is valid and sensitive for field-based assessments of joint angle time series, ROM in the primary plane of motion, magnitude of variability, and local dynamic stability. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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10 pages, 1416 KiB

Open AccessArticle

Use of Pressure-Measuring Insoles to Characterize Gait Parameters in Simulated Reduced-Gravity Conditions

by Christian Ison, Connor Neilsen, Jessica DeBerardinis, Mohamed B. Trabia and Janet S. Dufek

Sensors 2021, 21(18), 6244; https://doi.org/10.3390/s21186244 - 17 Sep 2021

Viewed by 2742

Abstract

Prior researchers have observed the effect of simulated reduced-gravity exercise. However, the extent to which lower-body positive-pressure treadmill (LBPPT) walking alters kinematic gait characteristics is not well understood. The purpose of the study was to investigate the effect of LBPPT walking on selected gait parameters in simulated reduced-gravity conditions. Twenty-nine college-aged volunteers participated in this cross-sectional study. Participants wore pressure-measuring insoles (Medilogic GmBH, Schönefeld, Germany) and completed three 3.5-min walking trials on the LBPPT (AlterG, Inc., Fremont, CA, USA) at 100% (normal gravity) as well as reduced-gravity conditions of 40% and 20% body weight (BW). The resulting insole data were analyzed to calculate center of pressure (COP) variables: COP path length and width and stance time. The results showed that 100% BW condition was significantly different from both the 40% and 20% BW conditions, p < 0.05. There were no significant differences observed between the 40% and 20% BW conditions for COP path length and width. Conversely, stance time significantly differed between the 40% and 20% BW conditions. The findings of this study may prove beneficial for clinicians as they develop rehabilitation strategies to effectively unload the individual’s body weight to perform safe exercises. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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25 pages, 5445 KiB

Open AccessArticle

Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements

by Xuelong Fan, Carl Mikael Lind, Ida-Märta Rhen and Mikael Forsman

Sensors 2021, 21(16), 5527; https://doi.org/10.3390/s21165527 - 17 Aug 2021

Cited by 18 | Viewed by 4293

Abstract

Accelerometer-based inclinometers have dominated kinematic measurements in previous field studies, while the use of inertial measurement units that additionally include gyroscopes is rapidly increasing. Recent laboratory studies suggest that these two sensor types and the two commonly used angular velocity computational methods may produce substantially different results. The aim of this study was, therefore, to evaluate the effects of sensor types and angular velocity computational methods on the measures of work postures and movements in a real occupational setting. Half-workday recordings of arm and trunk postures, and movements from 38 warehouse workers were compared using two sensor types: accelerometers versus accelerometers with gyroscopes—and using two angular velocity computational methods, i.e., inclination velocity versus generalized velocity. The results showed an overall small difference (<2° and value independent) for posture percentiles between the two sensor types, but substantial differences in movement percentiles both between the sensor types and between the angular computational methods. For example, the group mean of the 50th percentiles were for accelerometers: 71°/s (generalized velocity) and 33°/s (inclination velocity)—and for accelerometers with gyroscopes: 31°/s (generalized velocity) and 16°/s (inclination velocity). The significant effects of sensor types and angular computational methods on angular velocity measures in field work are important in inter-study comparisons and in comparisons to recommended threshold limit values. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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17 pages, 1246 KiB

Open AccessArticle

An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability

by Cristian Kaori Valencia-Marin, Juan Diego Pulgarin-Giraldo, Luisa Fernanda Velasquez-Martinez, Andres Marino Alvarez-Meza and German Castellanos-Dominguez

Sensors 2021, 21(13), 4443; https://doi.org/10.3390/s21134443 - 29 Jun 2021

Cited by 8 | Viewed by 3234

Abstract

Motion capture (Mocap) data are widely used as time series to study human movement. Indeed, animation movies, video games, and biomechanical systems for rehabilitation are significant applications related to Mocap data. However, classifying multi-channel time series from Mocap requires coding the intrinsic dependencies (even nonlinear relationships) between human body joints. Furthermore, the same human action may have variations because the individual alters their movement and therefore the inter/intraclass variability. Here, we introduce an enhanced Hilbert embedding-based approach from a cross-covariance operator, termed EHECCO, to map the input Mocap time series to a tensor space built from both 3D skeletal joints and a principal component analysis-based projection. Obtained results demonstrate how EHECCO represents and discriminates joint probability distributions as kernel-based evaluation of input time series within a tensor reproducing kernel Hilbert space (RKHS). Our approach achieves competitive classification results for style/subject and action recognition tasks on well-known publicly available databases. Moreover, EHECCO favors the interpretation of relevant anthropometric variables correlated with players’ expertise and acted movement on a Tennis-Mocap database (also publicly available with this work). Thereby, our EHECCO-based framework provides a unified representation (through the tensor RKHS) of the Mocap time series to compute linear correlations between a coded metric from joint distributions and player properties, i.e., age, body measurements, and sport movement (action class). Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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20 pages, 5600 KiB

Open AccessArticle

Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

by Dimitar Stanev, Konstantinos Filip, Dimitrios Bitzas, Sokratis Zouras, Georgios Giarmatzis, Dimitrios Tsaopoulos and Konstantinos Moustakas

Sensors 2021, 21(5), 1804; https://doi.org/10.3390/s21051804 - 5 Mar 2021

Cited by 17 | Viewed by 8191

Abstract

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge. Full article

(This article belongs to the Special Issue Sensors and Musculoskeletal Dynamics to Evaluate Human Movement)

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