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Traditionally, inertial measurement units- (IMU) based human joint angle estimation requires a priori knowledge about sensor alignment or specific calibration motions. Furthermore, magnetometer measurements can become unreliable indoors. Without magnetometers, however, IMUs lack a heading reference, which leads to unobservability issues. This paper proposes a magnetometer-free estimation method, which provides desirable observability qualities under joint kinematics that sufficiently excite the lower body degrees of freedom. The proposed lower body model expands on the current self-calibrating human-IMU estimation literature and demonstrates a novel knee hinge model, the inclusion of segment length anthropometry, segment cross-leg length discrepancy, and the relationship between the knee axis and femur/tibia segment. The maximum a posteriori problem is formulated as a factor graph and inference is performed via post-hoc, on-manifold global optimization. The method is evaluated (N = 12) for a prescribed human motion profile task. Accuracy of derived knee flexion/extension angle (4.34∘ root mean square error (RMSE)) without magnetometers is similar to current state-of-the-art with magnetometer use. The developed framework can be expanded for modeling additional joints and constraints.

The potential use of gait analysis for quantitative preoperative planning in total hip arthroplasty (THA) has previously been demonstrated. However, the joint kinematic data measured through this process tend to be unreliable for surgical planning due to distortions caused by soft tissue artifacts (STAs). In this study, we developed a novel motion capture framework by combining computed tomography (CT)-based postural calibration and subject-specific multibody dynamics modeling to prevent the effect of STAs in measuring hip kinematics. Three subjects with femoroacetabular impingement syndrome were recruited, and CT data for each patient were collected by attaching marker clusters near the hip. A subject-specific multibody hip joint model was developed based on reconstructed CT data. Spring–dashpot network calculations were performed to minimize the distance between the anatomical landmark and its corresponding infrared reflective marker. The STAs of the thigh was described as six degrees of freedom viscoelastic bushing elements, and their parameter values were identified via smooth orthogonal decomposition. Least squares optimization was used to modify the pelvic rotations to compensate for the rigid components of STAs. The results showed that CT-assisted motion tracking enabled the successful identification of STA influences in gait and squat positions. Furthermore, STA effects were found to alter maximal pelvis tilt and hip rotations during a squat. Compared to other techniques, such as dual fluoroscopic imaging, the adopted framework does not require additional medical imaging for patients undergoing robot-assisted THA surgery and is thus a practical way of evaluating hip joint kinematics for preoperative surgical planning.

Traditionally, inertial measurement unit (IMU)-based human joint angle estimation techniques are evaluated for general human motion where human joints explore all of their degrees of freedom. Pure human walking, in contrast, limits the motion of human joints and may lead to unobservability conditions that confound magnetometer-free IMU-based methods. This work explores the unobservability conditions emergent during human walking and expands upon a previous IMU-based method for the human knee to also estimate human hip angles relative to an assumed vertical datum. The proposed method is evaluated (N=12) in a human subject study and compared against an optical motion capture system. Accuracy of human knee flexion/extension angle (7.87∘ absolute root mean square error (RMSE)), hip flexion/extension angle (3.70∘ relative RMSE), and hip abduction/adduction angle (4.56∘ relative RMSE) during walking are similar to current state-of-the-art self-calibrating IMU methods that use magnetometers. Larger errors of hip internal/external rotation angle (6.27∘ relative RMSE) are driven by IMU heading drift characteristic of magnetometer-free approaches and non-hinge kinematics of the hip during gait, amongst other error sources. One of these sources of error, soft tissue perturbations during gait, is explored further in the context of knee angle estimation and it was observed that the IMU method may overestimate the angle during stance and underestimate the angle during swing. The presented method and results provide a novel combination of observability considerations, heuristic correction methods, and validation techniques to magnetic-blind, kinematic-only IMU-based skeletal pose estimation during human tasks with degenerate kinematics (e.g., straight line walking).

Kinematics of the knee during gait has mostly been studied using optical motion capture systems (MCS). The presence of soft tissue artifacts (STA) between the skin markers and the underlying bone presents a major impediment to obtaining a reliable joint kinematics assessment. In this study, we determined the effects of STA on the calculation of knee joint kinematics during walking and running, through the combination of high-speed dual fluoroscopic imaging system (DFIS) and magnetic resonance imaging technique. Ten adults walked and ran while data was collected simultaneously from MCS and high-speed DFIS. The study showed that measured STA underestimated knee flexion angle, but overestimated knee external and varus rotation. The absolute error values of the skin markers derived from knee flexion–extension angle, internal-external rotation, and varus-valgus rotation during walking were −3.2 ± 4.3 deg, 4.6 ± 3.1 deg, and 4.5 ± 3.2 deg respectively, and during running were −5.8 ± 5.4 deg, 6.6 ± 3.7 deg, and 4.8 ± 2.5 deg respectively. Average errors relative to the DFIS for flexion–extension angle, internal-external rotation, and varus-valgus rotation were 78 %, 271 %, 265 % during walking respectively, and were 43 %, 106 %, 200 % during running respectively. This study offers reference for the kinematic differences between MCS and high-speed DFIS, and will contribute to optimizing methods for analyzing knee kinematics during walking and running.

Obesity has a critical impact on musculoskeletal systems, and excessive weight directly affects the ability of subjects to realize movements. It is important to monitor the activities of obese subjects, their functional limitations, and the overall risks related to specific motor tasks. From this perspective, this systematic review identified and summarized the main technologies specifically used to acquire and quantify movements in scientific studies involving obese subjects. The search for articles was carried out on electronic databases, i.e., PubMed, Scopus, and Web of Science. We included observational studies performed on adult obese subjects whenever reporting quantitative information concerning their movement. The articles must have been written in English, published after 2010, and concerned subjects who were primarily diagnosed with obesity, thus excluding confounding diseases. Marker-based optoelectronic stereophotogrammetric systems resulted to be the most adopted solution for movement analysis focused on obesity; indeed, wearable technologies based on magneto-inertial measurement units (MIMUs) were recently adopted for analyzing obese subjects. Further, these systems are usually integrated with force platforms, so as to have information about the ground reaction forces. However, few studies specifically reported the reliability and limitations of these approaches due to soft tissue artifacts and crosstalk, which turned out to be the most relevant problems to deal with in this context. In this perspective, in spite of their inherent limitations, medical imaging techniques—such as Magnetic Resonance Imaging (MRI) and biplane radiography—should be used to improve the accuracy of biomechanical evaluations in obese people, and to systematically validate less-invasive approaches.

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.

When recording human movement with stereophotogrammetry, skin deformation and displacement (soft tissue artefact; STA) inhibits surface markers’ ability to validly represent the movement of the underlying bone. To resolve this issue, the components of marker motions which contribute to STA must be understood. The purpose of this study is to describe and quantify which components of this marker motion (cluster translation, rotation, scaling and deformation) contribute to STA during the stance phase of walking, a cutting manoeuvre, and one-legged hops. In vivo bone pin-based tibio-femoral kinematics of six healthy subjects were used to study skin marker-based STA. To quantify how total cluster translation, rotation, scaling and deformation contribute to STA, a resizable and deformable cluster model was constructed. STA was found to be greater in the thigh than the shank during all three movements. We found that the non-rigid (i.e. scaling and deformation) movements contribute very little to the overall amount of error, rendering surface marker optimisation methods aimed at minimising this component superfluous. The results of the current study indicate that procedures designed to account for cluster translation and rotation during human movement are required to correctly represent the motion of body segments, however reducing marker cluster scaling and deformation will have little effect on STA.

Skin Marker (SM) based motion capture is the most widespread technique used for motion analysis. Yet, the accuracy is often hindered by Soft Tissue Artifact (STA). This is a major issue in clinical gait analysis where kinematic results are used for decision-making. It also has a considerable influence on the results of rigid body and Finite Element (FE) musculoskeletal models that rely on SM-based kinematics to estimate muscle, contact and ligament forces. Current techniques devised to compensate for STA, in particular multi-body optimization methods, often consider simplified joint models. Although joint personalization with anatomical constraints has improved kinematic estimation, these models yet don’t represent a fully reliable solution to the STA problem, thus allowing us to envisage an alternative approach. In this perspective, we propose to develop a conceptual FE-based model of the lower limb for STA compensation and evaluate it for 66 healthy subjects under level walking motor task. Both hip and knee joint kinematics were analyzed, considering both rotational and translational joint motion. Results showed that STA caused underestimation of the hip joint kinematics (up to 2.2°) for all rotational DoF, and overestimation of knee joint kinematics (up to 12°) except in flexion/extension. Joint kinematics, in particular the knee joint, appeared to be sensitive to soft tissue stiffness parameters (rotational and translational mean difference up to 1.5° and 3.4 mm). Analysis of the results using alternative joint representations highlighted the versatility of the proposed modeling approach. This work paves the way for using personalized models to compensate for STA in healthy subjects and different activities.

Skin-mounted marker based motion capture systems are widely used in measuring the movement of human joints. Kinematic measurements associated with skin-mounted markers are subject to soft tissue artifacts (STA), since the markers follow skin movement, thus generating errors when used to represent motions of underlying bone segments. We present a novel ultrasound tracking system that is capable of directly measuring tibial and femoral bone surfaces during dynamic motions, and subsequently measuring six-degree-of-freedom (6-DOF) tibiofemoral kinematics. The aim of this study is to quantitatively compare the accuracy of tibiofemoral kinematics estimated by the ultrasound tracking system and by a conventional skin-mounted marker based motion capture system in a cadaveric experimental scenario. Two typical tibiofemoral joint models (spherical and hinge models) were used to derive relevant kinematic outcomes. Intra-cortical bone pins equipped with optical markers were inserted in the tibial and femoral bones to serve as a reference to provide ground truth kinematics. The ultrasound tracking system resulted in lower kinematic errors than the skin-mounted markers (the ultrasound tracking system: maximum root-mean-square (RMS) error 3.44° for rotations and 4.88 mm for translations, skin-mounted markers with the spherical joint model: 6.32° and 6.26 mm, the hinge model: 6.38° and 6.52 mm). Our proposed ultrasound tracking system has the potential of measuring direct bone kinematics, thereby mitigating the influence and propagation of STA. Consequently, this technique could be considered as an alternative method for measuring 6-DOF tibiofemoral kinematics, which may be adopted in gait analysis and clinical practice.

Motion reconstruction provides essential inputs for analyzing human movement through musculoskeletal simulations. To reconstruct joint angles from motion capture data, several multibody kinematic optimization methods have been developed. However, a computationally efficient method yet simple to implement while ensuring consistent kinematics at all levels is lacking. Here, we propose a potential field method generated by virtual spring-dampers connecting measured-derived skin markers to segment-fixed model points to reconstruct motion in a forward dynamic manner by solving the equations of motion. The virtual spring-damper forces move the mechanical system to minimize the elastic potential and the distance between markers during the motion. Several evaluation strategies are performed which demonstrate that the potential field method is computationally fast (2.5ms per frame) with comparable accuracy to the well-established least squares method in terms of reconstructed marker trajectories and joint angles (RMSE < 0.37 mm, 1.87°) and with low marker residuals (< 18.7 ± 12.6 mm) in line with reported ranges. Furthermore, soft tissue artifacts are compensated well compared to the simulated true values (RMSE < 1.66 mm, 3.69°). Sternoclavicular, scapulothoracic and glenohumeral rotations were reconstructed well the major trends and magnitudes of experimental measurements. We anticipate our method will pave the way for complex applications that demand reliable and rapid large-scale biomechanical analysis of human movement.