La reconstruction du mouvement du squelette : l'enjeu de l'artefact des tissus mous

In 3D human movement analysis performed using stereophotogrammetry and skin markers, bone pose can be only indirectly estimated. During a task, soft tissue deformations make the markers move with respect to the underlying bone generating soft tissue artefacts (STA), causing devastating effects on pose estimation and its compensation remains an open issue. The thesis’ aim was to contribute to the solution of this crucial issue. Modelling STA using measurable trial-specific variables is a prerequisite for its removal from marker trajectories. Two STA model architectures are proposed. A thigh marker-level model is first presented. STA was modeled as a linear combination of joint angles involved in the task. The model was calibrated with direct STA measures. The considerable number of model parameters led to defining STA approximations. Three definitions were proposed to represent STA as series of modes : individual marker displacements, marker-cluster geometrical transformations (MCGT), and skin envelope shape variations. Modes were selected using two criteria : modal energy and selecting them a priori. The MCGT allows to select either rigid or non-rigid components. It was also demonstrated that only the rigid component affects joint kinematics. A model of thigh and shank rigid component at cluster-level was then defined. An acceptable trade-off between STA compensation and number of parameters was obtained. These results lead to two main potential applications : generate realistic STAs for simulationLors de l'analyse 3D du mouvement humain basée sur des marqueurs cutanés, la position des os ne peut être qu'indirectement estimée. Au cours d'une tâche, les déformations des tissus mous génèrent des déplacements des marqueurs par rapport à l'os : les artefacts de tissus mous (STA), entraînant des effets dévastateurs sur l'estimation de la position. La compensation des STA demeure une question ouverte. L'objectif de cette thèse est de contribuer à la solution de cette question cruciale. La modélisation des STA en utilisant des variables spécifiques mesurables est une condition préalable à son élimination. Un modèle corrigeant les trajectoires individuelles de marqueurs de la cuisse, calibré par des mesures directes des STA, est d'abord présenté. Les STA sont modélisés comme une combinaison linéaire des angles articulaires impliqués. Trois représentations des STA par une série de modes sont proposées : déplacements de marqueurs individuels, transformations géométriques de clusters de marqueurs (MCGT), et variations de forme de l'enveloppe de peau. Le MCGT permet de dissocier les composantes rigides et non rigides. Il a été démontré que seule la composante rigide affecte la cinématique articulaire. Un modèle de cette composante est alors défini pour les clusters cuisse et jambe. Un compromis acceptable entre la correction des STA et le nombre de paramètres a ainsi été obtenu. Les principales applications sont de générer une simulation réaliste des STA ; et surtout, en se concentrant sur la composante rigide, le modèle permet une reconstruction satisfaisante des STA avec moins de paramètres, ce qui facilite son incorporation dans un algorithme d'estimation de la position osseus

Soft tissue displacement over pelvic anatomical landmarks during 3-D hip movements

The position, in a pelvis-embedded anatomical coordinate system, of skin points located over the following anatomical landmarks (AL) was determined while the hip assumed different spatial postures: right and left anterior superior and posterior superior iliac spines, and the sacrum. Postures were selected as occurring during walking and during a flexion-extension and circumduction movement, as used to determine the hip joint centre position (star-arc movement). Five volunteers, characterised by a wide range of body mass indices (22-37), were investigated. Subject-specific MRI pelvis digital bone models were obtained. For each posture, the pose of the pelvis-embedded anatomical coordinate system was determined by registering this bone model with points digitised over bony prominences of the pelvis, using a wand carrying a marker-cluster and stereophotogrammetry. The knowledge of how the position of the skin points varies as a function of the hip posture provided information regarding the soft tissue artefact (STA) that would affect skin markers located over those points during stereophotogrammetric movement analysis. The STA was described in terms of amplitude (relative to the position of the AL during an orthostatic posture), diameter (distance between the positions of the AL which were farthest away from each other), and pelvis orientation. The STA amplitude, exhibited, over all postures, a median [inter-quartile] value of 9[6] and 16[11]. mm, for normal and overweight volunteers, respectively. STA diameters were larger for the star-arc than for the walking postures, and the direction was predominantly upwards. Consequent errors in pelvic orientation were in the range 1-9 and 4-11 degrees, for the two groups respectively

The reconstruction of skeletal movement: the soft tissue artefact issue

In 3D human movement analysis performed using stereophotogrammetric systems and skin markers, bone pose can only be estimated in an indirect fashion. During a movement, soft tissue deformations make the markers move with respect to the underlying bone generating soft tissue artefact (STA). STA has devastating effects on bone pose estimation and its compensation remains an open question. The aim of this PhD thesis was to contribute to the solution of this crucial issue. Modelling STA using measurable trial-specific variables is a fundamental prerequisite for its removal from marker trajectories. Two STA model architectures are proposed. Initially, a thigh marker-level artefact model is presented. STA was modelled as a linear combination of joint angles involved in the movement. This model was calibrated using ex-vivo and in-vivo STA invasive measures. The considerable number of model parameters led to defining STA approximations. Three definitions were proposed to represent STA as a series of modes: individual marker displacements, marker-cluster geometrical transformations (MCGT), and skin envelope shape variations. Modes were selected using two criteria: one based on modal energy and another on the selection of modes chosen a priori. The MCGT allows to select either rigid or non-rigid STA components. It was also empirically demonstrated that only the rigid component affects joint kinematics, regardless of the non-rigid amplitude. Therefore, a model of thigh and shank STA rigid component at cluster-level was then defined. An acceptable trade-off between STA compensation effectiveness and number of parameters can be obtained, improving joint kinematics accuracy. The obtained results lead to two main potential applications: the proposed models can generate realistic STAs for simulation purposes to compare different skeletal kinematics estimators; and, more importantly, focusing only on the STA rigid component, the model attains a satisfactory STA reconstruction with less parameters, facilitating its incorporation in an pose estimator

Characterisation of the transient mechanical response and the electromyographical activation of lower leg muscles in whole body vibration training

The aim of this study is to characterise the transient mechanical response and the neuromuscular activation of lower limb muscles in subjects undergoing Whole Body Vibration (WBV) at different frequencies while holding two static postures, with focus on muscles involved in shaping postural responses. Twenty-five participants underwent WBV at 15, 20, 25 and 30 Hz while in hack squat or on fore feet. Surface electromyography and soft tissue accelerations were collected from Gastrocnemius Lateralis (GL), Soleus (SOL) and Tibialis Anterior (TA) muscles. Estimated displacement at muscle bellies revealed a pattern never highlighted before that differed across frequencies and postures (p < 0.001). After stimulation starts, muscle oscillation peaks, drops and further stabilises, suggesting the occurrence of a neuromuscular activation to reduce the vibration-induced oscillation. The oscillation attenuation at the SOL muscle correlated with its increased activation (rho = 0.29, p < 0.001). Furthermore, only specific WBV settings led to a significant increase in muscle contraction: WBV-induced activation of SOL and GL was maximal in fore-feet (p < 0.05) and in response to higher frequencies (30 Hz vs 15 Hz, p < 0.001). The analysis of the mechanical dynamics of lower leg muscles highlights a resonant response to WBVs, that for the SOL correlates to the increased muscle activation. Despite differing across frequencies and postures, this resonant behaviour seems to discourage the use of dynamic exercises on vibrating platforms. As for the most efficient WBV combination, calf muscle response to WBVs is maximised if those muscles are already pre-contracted and the stimulation frequencies are in the 25-30 Hz range

A quality control check to ensure comparability of stereophotogrammetric data between session and systems

Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies

Standardization proposal of soft tissue artefact description for data sharing in human motion measurements

Soft tissue artefact (STA) represents one of the main obstacles for obtaining accurate and reliable skeletal kinematics from motion capture. Many studies have addressed this issue, yet there is no consensus on the best available bone pose estimator and the expected errors associated with relevant results. Furthermore, results obtained by different authors are difficult to compare due to the high variability and specificity of the phenomenon and the different metrics used to represent these data. Therefore, the aim of this study was twofold: firstly, to propose standards for description of STA; and secondly, to provide illustrative STA data samples for body segments in the upper and lower extremities and for a range of motor tasks specifically, level walking, stair ascent, sit-to-stand, hip- and knee-joint functional movements, cutting motion, running, hopping, arm elevation and functional upper-limb movements. The STA dataset includes motion of the skin markers measured in vivo and ex vivo using stereophotogrammetry as well as motion of the underlying bones measured using invasive or bio-imaging techniques (i.e., X-ray fluoroscopy or MRI). The data are accompanied by a detailed description of the methods used for their acquisition, with information given about their quality as well as characterization of the STA using the proposed standards. The availability of open-access and standard-format STA data will be useful for the evaluation and development of bone pose estimators thus contributing to the advancement of three-dimensional human movement analysis and its translation into the clinical practice and other applications

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device's walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and - 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application.Trial registration: ISRCTN - 12246987

Laboratory and free-living gait performance in adults with COPD and healthy controls

BACKGROUND Gait characteristics are important risk factors for falls, hospitalisations and mortality in older adults, but the impact of COPD on gait performance remains unclear. We aimed to identify differences in gait characteristics between adults with COPD and healthy age-matched controls during 1) laboratory tests that included complex movements and obstacles, 2) simulated daily-life activities (supervised) and 3) free-living daily-life activities (unsupervised). METHODS This case-control study used a multi-sensor wearable system (INDIP) to obtain seven gait characteristics for each walking bout performed by adults with mild-to-severe COPD (n=17; forced expiratory volume in 1 s 57±19% predicted) and controls (n=20) during laboratory tests, and during simulated and free-living daily-life activities. Gait characteristics were compared between adults with COPD and healthy controls for all walking bouts combined, and for shorter (≤30 s) and longer (>30 s) walking bouts separately. RESULTS Slower walking speed (-11 cm·s$^{-1}$, 95% CI: -20 to -3) and lower cadence (-6.6 steps·min$^{-1}$, 95% CI: -12.3 to -0.9) were recorded in adults with COPD compared to healthy controls during longer (>30 s) free-living walking bouts, but not during shorter (≤30 s) walking bouts in either laboratory or free-living settings. Double support duration and gait variability measures were generally comparable between the two groups. CONCLUSION Gait impairment of adults with mild-to-severe COPD mainly manifests during relatively long walking bouts (>30 s) in free-living conditions. Future research should determine the underlying mechanism(s) of this impairment to facilitate the development of interventions that can improve free-living gait performance in adults with COPD

An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks

There is growing interest in the quantification of gait as part of complex motor tasks. This requires gait events (GEs) to be detected under conditions different from straight walking. This study aimed to propose and validate a new marker-based GE detection method, which is also suitable for curvilinear walking and step negotiation. The method was first tested against existing algorithms using data from healthy young adults (YA, n = 20) and then assessed in data from 10 individuals from the following five cohorts: older adults, chronic obstructive pulmonary disease, multiple sclerosis, Parkinson’s disease, and proximal femur fracture. The propagation of the errors associated with GE detection on the calculation of stride length, duration, speed, and stance/swing durations was investigated. All participants performed a variety of motor tasks including curvilinear walking and step negotiation, while reference GEs were identified using a validated methodology exploiting pressure insole signals. Sensitivity, positive predictive values (PPV), F1-score, bias, precision, and accuracy were calculated. Absolute agreement [intraclass correlation coefficient (ICC2,1)] between marker-based and pressure insole stride parameters was also tested. In the YA cohort, the proposed method outperformed the existing ones, with sensitivity, PPV, and F1 scores ≥ 99% for both GEs and conditions, with a virtually null bias (<10 ms). Overall, temporal inaccuracies minimally impacted stride duration, length, and speed (median absolute errors ≤1%). Similar algorithm performances were obtained for all the other five cohorts in GE detection and propagation to the stride parameters, where an excellent absolute agreement with the pressure insoles was also found (ICC2,1=0.817− 0.999). In conclusion, the proposed method accurately detects GE from marker data under different walking conditions and for a variety of gait impairments