Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises.

This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7 ∘ and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.

Comparison of the Mallampati Classification in Sitting and Supine Position to Predict Difficult Tracheal Intubation: A Prospective Observational Cohort Study.

BACKGROUND: The Mallampati classification (MLPT) is normally evaluated in the sitting position. However, many patients cannot be evaluated in the sitting position for medical reasons. Thus, we compared the MLPT in sitting and supine positions in predicting difficult tracheal intubation (DTI). We hypothesized that the diagnostic accuracy of the MLPT performed in sitting and supine positions would differ. METHODS: We performed a single-center prospective observational study in adult patients who received general anesthesia and orotracheal intubation for noncardiac surgery. During the preanesthesia consultation, the MLPT in the sitting position was recorded. The day of surgery, the MLPT in the supine position and the difficulty of intubation (DTI) were recorded by an independent observer. The diagnostic performance of the MLPT for the prediction of DTI was evaluated in the sitting and supine positions through the area under the receiver operating characteristic (ROC) curve. The performance of the Naguib score in predicting DTI was calculated with the MLPT in sitting and supine positions. RESULTS: Among the 3036 patients, 157 (5.1%) had DTI. The area under the ROC curve for the MLPT in supine position (0.82 [0.78-0.84]) was greater than that for the MLPT in the sitting position (0.70 [0.66-0.75]; P < .001). The relationship between the sitting and supine MLPTs was moderate (Spearman rank correlation coefficient: 0.50; P < .001). The area under ROC curve for predicting DTI by the Naguib score calculated with the supine MLPT (0.78 [95% confidence interval, 0.74-0.82]) was greater than that for the Naguib score calculated with MLPT in the sitting position (0.69 [95% confidence interval, 0.63-0.74)]; P < .001). CONCLUSIONS: The MLPT performed in the supine position is possibly superior to that performed in the sitting position for predicting difficult intubation in adults.

Whole body center of mass estimation with portable sensors: using the statically equivalent serial chain and a Kinect.

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient's home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.

Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data.

BACKGROUND: The present study aimed at devising a data processing procedure that provides an optimal estimation of the 3-D instantaneous orientation of an inertial measurement unit (IMU). This result is usually obtained by fusing the data measured with accelerometers, gyroscopes, and magnetometers. Nevertheless, issues related to compensation of integration errors and high sensitivity of these devices to magnetic disturbances call for different solutions. In this study, a method based on the use of gyroscope data only is presented, which uses a Weighted Fourier Linear Combiner adaptive filter to perform a drift-free estimate of the 3D orientation of an IMU located on the lower trunk during walking. METHODS: A tuning of the algorithm parameters and a sensitivity analysis to its initial conditions was performed using treadmill walking data from 3 healthy subjects. The accuracy of the method was then assessed using data collected from 15 young healthy subjects during treadmill walking at variable speeds and comparing the pitch, roll, and yaw angles estimated from the gyroscopes data to those obtained with a stereophotogrammetric system. Root mean square (RMS) difference and correlation coefficients (r) were used for this purpose. RESULTS: An optimal set of values of the algorithm parameters was established. At all the observed speeds, and also in all the various sub-phases, the investigated angles were all estimated to within an average RMS difference of less than 1.2 deg and an average r greater than 0.90. CONCLUSIONS: This study proved the effectiveness of the Weighted Fourier Linear Combiner method in accurately reconstructing the 3D orientation of an IMU located on the lower trunk of a subject during treadmill walking. This method is expected to also perform satisfactorily for overground walking data and to be applicable also to other "quasi-periodic" tasks, such as squatting, rowing, running, or swimming.

Integration of human walking gyroscopic data using empirical mode decomposition.

The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns.

Dynamic estimation of soft tissue stiffness for use in modeling socket, orthosis or exoskeleton interfaces with lower limb segments.

Modeling the interface between the lower limb segments and a socket, orthosis or exoskeleton is crucial to the design, control, and assessment of such devices. The present study aimed to estimate translational and rotational soft tissue stiffness at the thigh and shank during daily living activities performed by six subjects. Smooth orthogonal decomposition (SOD) was used on skin marker trajectories and fluoroscopy-based knee joint kinematics to compute stiffness coefficients during squatting, sitting and rising from a chair, level walking, and stair descending. On average, for all subjects and for all activities, in the anatomical directions observed, the translational and rotational stiffness coefficients for the shank were, respectively, 1.4 ± 1.99kN/m (median and interquartile range) and 41.5 ± 34.3Nm/deg. The results for the thigh segment were 1.79 ± 2.73kN/m and 30.5 ± 50.4Nm/deg. As previously reported in the literature dealing with the soft tissue artifact - considered as soft tissue deformation in this study - the computed stiffness coefficients were dependent on tasks, subjects, segments, and anatomical directions. The main advantage of SOD over previous methods lies in enabling estimation of a task-dependent 6 × 6 stiffness matrix of the interface between segments and external devices, useful in their modeling and assessment.

Sparse Visual-Inertial Measurement Units Placement for Gait Kinematics Assessment.

This study investigates the possibility of estimating lower-limb joint kinematics and meaningful performance indexes for physiotherapists, during gait on a treadmill based on data collected from a sparse placement of new Visual Inertial Measurement Units (VIMU) and the use of an Extended Kalman Filter (EKF). The proposed EKF takes advantage of the biomechanics of the human body and of the investigated task to reduce sensor inaccuracies. Two state-vector formulations, one based on the use of constant acceleration model and one based on Fourier series, and the tuning of their corresponding parameters were analyzed. The constant acceleration model, due to its inherent inconsistency for human motion, required a cumbersome optimisation process and needed the a-priori knowledge of reference joint trajectories for EKF parameters tuning. On the other hand, the Fourier series formulation could be used without a specific parameters tuning process. In both cases, the average root mean square difference and correlation coefficient between the estimated joint angles and those reconstructed with a reference stereophotogrammetric system was 3.5deg and 0.70, respectively. Moreover, the stride lengths were estimated with a normalized root mean square difference inferior to 2% when using the forward kinematics model receiving as input the estimated joint angles. The popular gait deviation index was also estimated and showed similar results very close to 100, using both the proposed method and the reference stereophotogrammetric system. Such consistency was obtained using only three wireless and affordable VIMU located at the pelvis and both heels and tracked using two affordable RGB cameras. Being further easy-to-use and suitable for applications taking place outside of the laboratory, the proposed method thus represents a good compromise between accurate reference stereophotogrammetric systems and markerless ones for which accuracy is still under debate.

Accuracy of image data stream of a markerless motion capture system in determining the local dynamic stability and joint kinematics of human gait.

Assessment of gait parameters is commonly performed through the high-end motion tracking systems, which limits the measurement to sophisticated laboratory settings due to its excessive cost. Recently, Microsoft Kinect (v2) sensor has become popular in clinical gait analysis due to its low-cost. But, determining the accuracy of its RGB-D image data stream in measuring the joint kinematics and local dynamic stability remains an unsolved problem. This study examined the suitability of Kinect(v2) RGB-D image data stream in assessing those gait parameters. Fifteen healthy participants walked on a treadmill during which lower body kinematics were measured by a Kinect(v2) sensor and a optophotogrametric tracking system, simultaneously. Extended Kalman filter was used to extract the lower extremity joint angles from Kinect, while inverse kinematics was used for the gold standard system. For both systems, local dynamic stability was assessed using maximal Lyapunov exponent. Sprague's validation metrics, root mean square error (RMSE) and normalized RMSE were computed to confirm the difference between the joint angles time series of the two systems while relative agreement between them was investigated through Pearson's correlation coefficient (pr). Fisher's Exact Test was performed on maximal Lyapunov exponent to investigate the data independence while reliability was assessed using intraclass correlation coefficients. This study concludes that the RGB-D data stream of Kinect sensor is efficient in estimating joint kinematics, but not suitable for measuring the local dynamic stability.

Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.

This paper proposes a method to enable the use of non-intrusive, small, wearable, and wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic-EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features, such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37%, respectively, estimates joint angles with 2.4° root mean squared error, and segments the motion into repetitions with 96% accuracy.

Neonatal and maternal outcomes of pregnancy with maternal cardiac disease (the NORMANDY study) : Years 2000-2014.

Pregnancies complicated by congenital or acquired heart diseases are at high risk of maternal, obstetrical and neonatal poor outcomes. During the period 2000-2014, 197 pregnancies occurring in 147 women with heart disease were managed in our institution. A maternal cardiac event complicated 13 pregnancies. Obstetrical and neonatal complications occurred respectively in 35.0% (95% CI [28.3-41.7]) and 37.0% (95% CI [30.3-43.7]) of pregnancies. All complications were more frequent amongst cardiomyopathies or obstructive and conotruncal lesions, whereas left-to-right shunts were less prone to present with complications. Complications occurred between the end of the second trimester and the middle of the third trimester or during the post-partum period. Caesarean section was the mode of delivery in 37% (95% CI [30.3-43.7]) of cases, and general anaesthesia was performed in 8.6% of cases (95% CI [4.7-12.5]). Although reporting relatively mild heart diseases, this retrospective study shows an evolution in the management of pregnancies complicated by cardiopathies. Vaginal delivery under locoregional anaesthesia can be achieved in many pregnancies, whereas others require strict multi-disciplinary follow-up in a specialized centre. The creation of a large, multi-centric registry might help improve and personalize the management of these high-risk pregnancies.

A sensitivity analysis method for the body segment inertial parameters based on ground reaction and joint moment regressor matrices.

This paper presents a method allowing a simple and efficient sensitivity analysis of dynamics parameters of complex whole-body human model. The proposed method is based on the ground reaction and joint moment regressor matrices, developed initially in robotics system identification theory, and involved in the equations of motion of the human body. The regressor matrices are linear relatively to the segment inertial parameters allowing us to use simple sensitivity analysis methods. The sensitivity analysis method was applied over gait dynamics and kinematics data of nine subjects and with a 15 segments 3D model of the locomotor apparatus. According to the proposed sensitivity indices, 76 segments inertial parameters out the 150 of the mechanical model were considered as not influent for gait. The main findings were that the segment masses were influent and that, at the exception of the trunk, moment of inertia were not influent for the computation of the ground reaction forces and moments and the joint moments. The same method also shows numerically that at least 90% of the lower-limb joint moments during the stance phase can be estimated only from a force-plate and kinematics data without knowing any of the segment inertial parameters.

Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a soft tissue artefact model.

To reduce the impact of the soft tissue artefact (STA) on the estimate of skeletal movement using stereophotogrammetric and skin-marker data, multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) have been proposed. This paper assessed the feasibility and efficiency of these methods when they embed a mathematical model of the STA and simultaneously estimate the ankle, knee and hip joint kinematics and the model parameters. A STA model was used that provides an estimate of the STA affecting the marker-cluster located on a body segment as a function of the kinematics of the adjacent joints. The MKO and the EKF were implemented with and without the STA model. To assess these methods, intra-cortical pin and skin markers located on the thigh, shank, and foot of three subjects and tracked during the stance phase of running were used. Embedding the STA model in MKO and EKF reduced the average RMS of marker tracking from 12.6 to 1.6mm and from 4.3 to 1.9mm, respectively, showing that a STA model trial-specific calibration is feasible. Nevertheless, with the STA model embedded in MKO, the RMS difference between the estimated and the reference joint kinematics determined from the pin markers slightly increased (from 2.0 to 2.1deg) On the contrary, when the STA model was embedded in the EKF, this RMS difference was slightly reduced (from 2.0 to 1.7deg) thus showing a better potentiality of this method to attenuate STA effects and improve the accuracy of joint kinematics estimate.

Optimal External Wrench Distribution During a Multi-Contact Sit-to-Stand Task.

This paper aims at developing and evaluating a new practical method for the real-time estimate of joint torques and external wrenches during multi-contact sit-to-stand (STS) task using kinematics data only. The proposed method allows also identifying subject specific body inertial segment parameters that are required to perform inverse dynamics. The identification phase is performed using simple and repeatable motions. Thanks to an accurately identified model the estimate of the total external wrench can be used as an input to solve an under-determined multi-contact problem. It is solved using a constrained quadratic optimization process minimizing a hybrid human-like energetic criterion. The weights of this hybrid cost function are adjusted and a sensitivity analysis is performed in order to reproduce robustly human external wrench distribution. The results showed that the proposed method could successfully estimate the external wrenches under buttocks, feet, and hands during STS tasks (RMS error lower than 20 N and 6 N.m). The simplicity and generalization abilities of the proposed method allow paving the way of future diagnosis solutions and rehabilitation applications, including in-home use.

Assessment of macro- and micro-oxygenation parameters during fractional fluid infusion: A pilot study.

PURPOSE: The main goal of this study was to assess whether maximal fluid infusion improves both oxygen delivery (DO2) and micro-circulatory parameters during hemodilution. The secondary objective was to assess the ability of baseline micro-circulatory parameters to predict oxygen consumption (VO2) response following fluid infusion. MATERIALS AND METHODS: In a postoperative cardiac ICU, patients received repeated fluid infusion until stroke volume (SV) was maximized. Before and after each fluid expansion, macro- (DO2, VO2) and micro-circulatory oxygenation parameters were recorded [central venous oxygen saturation (ScVO2), blood lactate, difference in veno-arterial carbon dioxide tension (P(v-a)CO2), somatic and cerebral oxygen saturation (rSO2)]. Patients were classified as VO2-Responders or VO2-Non-Responders according to an increase in VO2 above or below 15%, respectively. RESULTS: After maximal fluid infusion, all patients showed improved macro- and micro-circulatory oxygenation parameters, but VO2-Responders had lower values (especially for ScVO2 and cerebral rSO2). Only baseline ScVO2 and cerebral rSO2 were useful to predict the VO2 response to maximal fluid infusion (ROCAUC 0.80 (95% CI: 0.54-0.95, P=0.012) and 0.83 (95% CI: 0.57-0.96, P=0.001). CONCLUSIONS: Maximal fluid infusion improves macro- and micro-circulatory oxygenation parameters. For VO2-Responders, only ScVO2 and cerebral rSO2 could serve as markers of tissue hypoxia.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

The role of age and comorbidities in postoperative outcome of mitral valve repair: A propensity-matched study.

The average age of patients undergoing mitral valve repair is increasing each year. This retrospective study aimed to compare postoperative complications of mitral valve repair (known to be especially high-risk) between 2 age groups: under and over the age of 80.Patients who underwent mitral valve repair were divided into 2 groups: group 1 (<80 years old) and group 2 (≥80 years old). Baseline characteristics, pre- and postoperative hemodynamic data, surgical characteristics, and postoperative follow-up data until hospital discharge were collected.A total of 308 patients were included: 264 in group 1 (age 63 ±â€Š13 years) and 44 in group 2 (age 83 ±â€Š2 years). Older patients had more comorbidities (atrial fibrillation, history of cardiac decompensation, systemic hypertension, pulmonary hypertension, and chronic kidney disease) and they presented more postoperative complications (50.0% vs 33.7%; P = 0.043), with a longer hospital stay (8.9 ±â€Š6.9 vs 6.6 ±â€Š4.6 days; P = 0.005). To assess the burden of age, a propensity score was awarded to postoperative complications. Active smoking, chronic pulmonary disease, chronic kidney disease, associated ischemic heart disease, obesity, and cardio pulmonary by-pass duration were described as independent risk factors. When matched on this propensity score, there was no difference in morbidity or mortality between group 1 and group 2.Older patients suffered more postoperative complications, which were related to their comorbidities and not only to their age.

Fast Determination of the Planar Body Segment Inertial Parameters Using Affordable Sensors.

This study aimed at developing and evaluating a new method for the fast and reliable identification of body segment inertial parameters with a planar model using affordable sensors. A Kinect sensor, with a new marker-based tracking system, and a Wii balance board were used as an affordable and portable motion capture system. A set of optimal exciting motions was used in a biofeedback interface to identify the body segment parameters. The method was validated with 12 subjects performing various standardized motions. The same dynamometric quantities estimated both with the proposed system and, as a reference, with a laboratory grade force-plate were compared. The results showed that the proposed method could successfully estimate the resultant moment and the vertical ground reaction force (rms errors less than 8 Nm and 12 N, respectively). Finally, when local segment values were artificially varied, the proposed method was able to detect and estimate the additional masses accurately and with an error of less than 0.5 Kg, contrary to values generated with commonly used anthropometric tables.