Assessment of Hip and Lumbar Spine Range of Motion After Total Hip Arthroplasty Using a Single Camera Markerless System.

BACKGROUND: Total hip arthroplasty (THA) is one of the most cost-effective and successful procedures in orthopedics. However, assessing the post-operative range of motion (ROM) remains a challenge due to the limitations of traditional measurement methods. This study aimed to evaluate hip and spine ROM post-operatively and single-leg balance, using a single-camera markerless motion capture system, and compare outcomes with pre-operative ROM and with an age-matched healthy control group. METHODS: An interventional study was conducted from January 2018 to December 2021. Twenty patients with hip osteoarthritis underwent THA and were assessed using a single-camera markerless system (Kinetisense software). Measurements were taken one month pre-operatively and one year post-operatively. RESULTS: Significant improvements were observed in hip and lumbar spine ROM variables after THA. The most notable enhancements were in hip and spinal flexion. Compared to the control group, the THA group showed minor deficits in hip ROM, particularly in external rotation. Single-leg balance demonstrated improved stability post-operatively. CONCLUSIONS: The single-camera markerless motion capture system offers a promising alternative for assessing hip and lumbar spine ROM, presenting potential advantages over manual goniometry and traditional 3D motion capture systems. Using this system for the evaluation of patients after THA, it seems that THA significantly enhances hip and lumbar spine ROM. Future research should focus on validating the accuracy of markerless systems.

Comparison of Six Sensor Fusion Algorithms with Electrogoniometer Estimation of Wrist Angle in Simulated Work Tasks.

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75-0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data.

In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.

Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors.

In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences.

The WGD-A Dataset of Assembly Line Working Gestures for Ergonomic Analysis and Work-Related Injuries Prevention.

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD-Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker's kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.

Wearable Motion Capture System Evaluation for Biomechanical Studies for Hip Joints.

Human motion capture (MOCAP) systems are vital while determining the loads occurring at the joints. Most of the clinical MOCAP systems are very costly, requiring investment and infrastructure. Therefore, alternative technologies are in demand. In this study, a novel markerless wearable MOCAP system was assessed for its compatibility with a biomechanical modeling software. To collect evidence, experiments were designed in two stages for quantifying the range of motion (ROM) of the hip joint, in vitro and in vivo. Three constrained single-plane motions-abduction/adduction, flexion/extension, and internal/external rotation movements of the active leg-were analyzed. The data were collected from 14 healthy volunteers, using the wearable system and a medical grade optoelectronic MOCAP system simultaneously and compared against. For the in vitro study, the root-mean-square error (RMSE) for the abduction/adduction motion of the hip joint was calculated as 0.11 deg/0.30 deg and 0.11 deg/0.09 deg, respectively, for the wearable and the opto-electronic system. The in vivo Bland-Altman plots showed that the two system data are comparable. The simulation software is found compatible to run the simulations in offline mode. The wearable system could be utilized in the field of biomechanics software for running the kinetic simulations. The results demonstrated that the wearable system could be an alternative in the field of biomechanics based on the evidence collected.

Human Motion Tracking with Less Constraint of Initial Posture from a Single RGB-D Sensor.

High-quality and complete human motion 4D reconstruction is of great significance for immersive VR and even human operation. However, it has inevitable self-scanning constraints, and tracking under monocular settings also has strict restrictions. In this paper, we propose a human motion capture system combined with human priors and performance capture that only uses a single RGB-D sensor. To break the self-scanning constraint, we generated a complete mesh only using the front view input to initialize the geometric capture. In order to construct a correct warping field, most previous methods initialize their systems in a strict way. To maintain high fidelity while increasing the easiness of the system, we updated the model while capturing motion. Additionally, we blended in human priors in order to improve the reliability of model warping. Extensive experiments demonstrated that our method can be used more comfortably while maintaining credible geometric warping and remaining free of self-scanning constraints.

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture.

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users' hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

A technical support tool for joint range of motion determination in functional diagnostics - an inter-rater study.

BACKGROUND: The examination of joint range of motion (RoM) is part of musculo-skeletal functional diagnostics, used, for example, in occupational examinations. Various examination methodologies exist that have been optimized for occupational medical practice, which means they were reduced to the most necessary and feasible measures and examinations for efficiency and usability reasons. Because of time constraints in medical examinations in occupational settings, visual inspection is commonly used to quantify joint RoM. To support medical examiners, an inertial sensor-based measurement system (CUELA) was adapted for joint RoM examination in these settings. The objective of the present study was to evaluate the measurement tool in functional diagnostics under conditions close to clinical practice. METHODS: The joint RoM of twenty healthy subjects were examined by three physicians, who were simultaneously using the measurement tool. Physicians were blinded to the measurement results and the other physicians. Active RoM was examined on the cervical, thoracic and lumbar spine while passive RoM was examined on the shoulder, elbow, wrist, hip, and knee, resulting in a total of 40 joint examination angles. The means, standard deviations, intraclass correlation coefficients (I C C 3,k ), and Bland-Altman-Plots were calculated using MatLab for statistical analysis. RESULTS: Most measurement results were in accordance with expected joint RoMs. All examinations showed an acceptable repeatability. In active RoM examinations, the ICC of inter-rater reliability varied between 0.79 and 0.95. In passive RoM examination the ICC varied between 0.71 and 0.96, except examination angles at the elbow and knee extension (ICC: 0.0-0.77). CONCLUSION: The reliability and objectivity of active RoM examinations were improved by the measurement tool compared with examiners. In passive RoM examinations of upper and lower extremities, the increase of objectivity by the measurements was limited for some examination angles by external factors such as the individual examiner impact on motion execution or the given joint examination conditions. Especially the elbow joint examination requires further development to achieve acceptable reliability. A modification in the examination method to reduce the examiner impact on measurement and the implementation of a more complex calibration procedure could improve the objectivity and reliability of the measurement tool in passive joint RoM examination to be applicable on nearly the whole body.

A novel method for assessing the 3-D orientation accuracy of inertial/magnetic sensors.

A novel method for assessing the accuracy of inertial/magnetic sensors is presented. The method, referred to as the "residual matrix" method, is advantageous because it decouples the sensor's error with respect to Earth's gravity vector (attitude residual error: pitch and roll) from the sensor's error with respect to magnetic north (heading residual error), while remaining insensitive to singularity problems when the second Euler rotation is close to ±90°. As a demonstration, the accuracy of an inertial/magnetic sensor mounted to a participant's forearm was evaluated during a reaching task in a laboratory. Sensor orientation was measured internally (by the inertial/magnetic sensor) and externally using an optoelectronic measurement system with a marker cluster rigidly attached to the sensor's enclosure. Roll, pitch and heading residuals were calculated using the proposed novel method, as well as using a common orientation assessment method where the residuals are defined as the difference between the Euler angles measured by the inertial sensor and those measured by the optoelectronic system. Using the proposed residual matrix method, the roll and pitch residuals remained less than 1° and, as expected, no statistically significant difference between these two measures of attitude accuracy was found; the heading residuals were significantly larger than the attitude residuals but remained below 2°. Using the direct Euler angle comparison method, the residuals were in general larger due to singularity issues, and the expected significant difference between inertial/magnetic sensor attitude and heading accuracy was not present.

Evaluación de riesgos de manipulación repetitiva a alta frecuencia basada en análisis de esfuerzos dinámicos en las articulaciones sobre modelos humanos digitales / Risk assessment of high frequency repetitive handling based on dynamic stress analysis in joints of Digital human Models

El nuevo método desarrollado está dirigido a evaluar el riesgo derivado de la realización de tareas repetitivas a alta frecuencia. Utiliza un sistema de captura de movimiento basado en sensores inerciales, utilizable en los propios puestos de trabajo, que nos permite trasladar el movimiento a un modelo biomecánico de antropometría similar al sujeto observado. Incorpora un motor de cálculo de esfuerzos en las articulaciones, que tiene en cuenta las dimensiones antropométricas, las fuerzas externas, las reacciones en los puntos de apoyo, así como las fuerzas de inercia derivadas de las aceleraciones lineales y angulares alcanzadas durante la tarea. El resultado es un método predictivo de riesgo musculoesquelético que, a diferencia de otros métodos que requieren un trabajo muy minucioso (UNE 1005-3) a la hora de definir las acciones técnicas, reduce drásticamente el proceso de análisis ya que el proceso es automático, y no está influenciado por la subjetividad del evaluador.

The new method developed is aimed at assessing the risk from repetitive tasks at high frequency. It uses a motion capture system based on inertial sensors for use in their own jobs, which allows us to transfer the movement to a biomechanical model similar to the observed subject anthropometry. Incorporates a calculation engine of efforts in joints, considerating the anthropometric dimensions, external forces, the reactions in the support points, as well as the inertial forces arising from linear and angular accelerations encountered during the task. The result is a musculoskeletal risk predictive method which, unlike other methods that require a very thorough job (UNE 1005-3) for defining the technical actions, dramatically reduces the analysis process because the process is automated, and is not influenced by the subjectivity of the evaluator.