scholarly journals Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Mickaël Begon ◽  
Michael Skipper Andersen ◽  
Raphaël Dumas
Keyword(s):  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Model Development ◽  
Simulated Data ◽  
Ground Truth ◽  
Joint Kinematics ◽  
Sensitivity Analyses ◽  
Geometrical Parameters ◽  
Joint Models ◽  
Human Joint

Multibody kinematics optimization (MKO) aims to reduce soft tissue artefact (STA) and is a key step in musculoskeletal modeling. The objective of this review was to identify the numerical methods, their validation and performance for the estimation of the human joint kinematics using MKO. Seventy-four papers were extracted from a systematized search in five databases and cross-referencing. Model-derived kinematics were obtained using either constrained optimization or Kalman filtering to minimize the difference between measured (i.e., by skin markers, electromagnetic or inertial sensors) and model-derived positions and/or orientations. While hinge, universal, and spherical joints prevail, advanced models (e.g., parallel and four-bar mechanisms, elastic joint) have been introduced, mainly for the knee and shoulder joints. Models and methods were evaluated using: (i) simulated data based, however, on oversimplified STA and joint models; (ii) reconstruction residual errors, ranging from 4 mm to 40 mm; (iii) sensitivity analyses which highlighted the effect (up to 36 deg and 12 mm) of model geometrical parameters, joint models, and computational methods; (iv) comparison with other approaches (i.e., single body kinematics optimization and nonoptimized kinematics); (v) repeatability studies that showed low intra- and inter-observer variability; and (vi) validation against ground-truth bone kinematics (with errors between 1 deg and 22 deg for tibiofemoral rotations and between 3 deg and 10 deg for glenohumeral rotations). Moreover, MKO was applied to various movements (e.g., walking, running, arm elevation). Additional validations, especially for the upper limb, should be undertaken and we recommend a more systematic approach for the evaluation of MKO. In addition, further model development, scaling, and personalization methods are required to better estimate the secondary degrees-of-freedom (DoF).

The Use of Robotics Technology to Study Human Joint Kinematics: A New Methodology

1993 ◽  
Vol 115 (3) ◽  
pp. 211-217 ◽  
Author(s):  
Hiromichi Fujie ◽  
Kiyoshi Mabuchi ◽  
Savio L.-Y. Woo ◽  
Glen A. Livesay ◽  
Shinji Arai ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Complex Loading ◽  
Joint Kinematics ◽  
Loading Conditions ◽  
Joint Force ◽  
Physiological Loading ◽  
Control Forces ◽  
6 Degrees Of Freedom ◽  
Human Joint ◽  
Anterior Posterior

Robotics technologies have been modified to control and measure both the force and position of synovial joints for the study of joint kinematics. One such system was developed to perform kinematic testing of a human joint. A 6-axis articulated robotic manipulator with 6 degrees of freedom (DOF) of motion was designed and constructed; a mathematical description for joint force and position was devised; and hardware and software to control forces applied to the joint, as well as position of the joint, were developed. The new methodology was utilized to simulate physiological loading conditions and to perform an anterior-posterior (A-P) translation test on a human cadaveric knee. Testing showed that this new system can simulate complex loading conditions and also measure the resulting joint kinematics.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592095492
Author(s):  
Marco Del Giudice ◽  
Steven W. Gangestad
Keyword(s):  
Degrees Of Freedom ◽  
A Priori ◽  
Simulated Data ◽  
Style Analysis ◽  
Data Set ◽  
Scant Attention ◽  
Equivalence Type ◽  
The Impact ◽  
Biased Estimates

Decisions made by researchers while analyzing data (e.g., how to measure variables, how to handle outliers) are sometimes arbitrary, without an objective justification for choosing one alternative over another. Multiverse-style methods (e.g., specification curve, vibration of effects) estimate an effect across an entire set of possible specifications to expose the impact of hidden degrees of freedom and/or obtain robust, less biased estimates of the effect of interest. However, if specifications are not truly arbitrary, multiverse-style analyses can produce misleading results, potentially hiding meaningful effects within a mass of poorly justified alternatives. So far, a key question has received scant attention: How does one decide whether alternatives are arbitrary? We offer a framework and conceptual tools for doing so. We discuss three kinds of a priori nonequivalence among alternatives—measurement nonequivalence, effect nonequivalence, and power/precision nonequivalence. The criteria we review lead to three decision scenarios: Type E decisions (principled equivalence), Type N decisions (principled nonequivalence), and Type U decisions (uncertainty). In uncertain scenarios, multiverse-style analysis should be conducted in a deliberately exploratory fashion. The framework is discussed with reference to published examples and illustrated with the help of a simulated data set. Our framework will help researchers reap the benefits of multiverse-style methods while avoiding their pitfalls.

Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

2019 ◽  
Vol 13 (1) ◽  
pp. 47-61
Author(s):  
Guenther Retscher ◽  
Jonathan Kleine ◽  
Lisa Whitemore
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Ground Truth ◽  
Mobile User ◽  
Location Based Services ◽  
Raspberry Pi ◽  
Indoor Environments ◽  
Point Location ◽  
Point Determination ◽  
The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

Abnormalities Detection of IMU Based on PCA in Motion Monitoring

2012 ◽  
Vol 224 ◽  
pp. 533-538 ◽  
Author(s):  
Jing Zhou ◽  
Steven Su ◽  
Ai Huang Guo ◽  
Wei Dong Chen
Keyword(s):  
Measurement Method ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Principal Component ◽  
Body Sensor Networks ◽  
Remote Measurement ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Multiple Degrees Of Freedom ◽  
Measurement Units

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people’s daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling’s T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

scholarly journals Association Between PM2.5 and Daily Hospital Admissions for Heart Failure: A Time-Series Analysis in Beijing

2018 ◽  
Vol 15 (10) ◽  
pp. 2217 ◽  
Author(s):  
Man Li ◽  
Yao Wu ◽  
Yao-Hua Tian ◽  
Ya-Ying Cao ◽  
Jing Song ◽  
...  
Keyword(s):  
Heart Failure ◽  
Time Series ◽  
Degrees Of Freedom ◽  
Hospital Admissions ◽  
Scientific Evidence ◽  
Poisson Model ◽  
Sensitivity Analyses ◽  
Calendar Time ◽  
Medical Claim ◽  
Increased Risk

There is little evidence that acute exposure to fine particulate matter (PM2.5) impacts the rate of hospitalization for congestive heart failure (CHF) in developing countries. The primary purpose of the present retrospective study was to evaluate the short-term association between ambient PM2.5 and hospitalization for CHF in Beijing, China. A total of 15,256 hospital admissions for CHF from January 2010 to June 2012 were identified from Beijing Medical Claim Data for Employees and a time-series design with generalized additive Poisson model was used to assess the obtained data. We found a clear significant exposure response association between PM2.5 and the number of hospitalizations for CHF. Increasing PM2.5 daily concentrations by 10 μg/m3 caused a 0.35% (95% CI, 0.06–0.64%) increase in the number of CHF admissions on the same day. We also found that female and older patients were more susceptible to PM2.5. These associations remained significant in sensitivity analyses involving changing the degrees of freedom of calendar time, temperature, and relative humidity. PM2.5 was associated with significantly increased risk of hospitalization for CHF in this citywide study. These findings may contribute to the limited scientific evidence about the acute impacts of PM2.5 on CHF in China.

scholarly journals Evaluating the reproducibility of single-cell gene regulatory network inference algorithms

2020 ◽  
Author(s):  
Yoonjee Kang ◽  
Denis Thieffry ◽  
Laura Cantini
Keyword(s):  
Single Cell ◽  
Network Inference ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Gene Regulatory Network Inference ◽  
Sequencing Platform ◽  
Cell Network ◽  
Inference Algorithms ◽  
Inference Methods

AbstractNetworks are powerful tools to represent and investigate biological systems. The development of algorithms inferring regulatory interactions from functional genomics data has been an active area of research. With the advent of single-cell RNA-seq data (scRNA-seq), numerous methods specifically designed to take advantage of single-cell datasets have been proposed. However, published benchmarks on single-cell network inference are mostly based on simulated data. Once applied to real data, these benchmarks take into account only a small set of genes and only compare the inferred networks with an imposed ground-truth.Here, we benchmark four single-cell network inference methods based on their reproducibility, i.e. their ability to infer similar networks when applied to two independent datasets for the same biological condition. We tested each of these methods on real data from three biological conditions: human retina, T-cells in colorectal cancer, and human hematopoiesis.GENIE3 results to be the most reproducible algorithm, independently from the single-cell sequencing platform, the cell type annotation system, the number of cells constituting the dataset, or the thresholding applied to the links of the inferred networks. In order to ensure the reproducibility and ease extensions of this benchmark study, we implemented all the analyses in scNET, a Jupyter notebook available at https://github.com/ComputationalSystemsBiology/scNET.

scholarly journals Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy

2019 ◽  
Author(s):  
Hesam Mazidi ◽  
Tianben Ding ◽  
Arye Nehorai ◽  
Matthew D. Lew
Keyword(s):  
Single Molecule ◽  
Computational Models ◽  
Amyloid Fibrils ◽  
Imaging System ◽  
Simulated Data ◽  
Super Resolution ◽  
Ground Truth ◽  
Localization Algorithm ◽  
Reconstruction Algorithms ◽  
Imaging Data

The resolution and accuracy of single-molecule localization micro-scopes (SMLMs) are routinely benchmarked using simulated data, calibration “rulers,” or comparisons to secondary imaging modalities. However, these methods cannot quantify the nanoscale accuracy of an arbitrary SMLM dataset. Here, we show that by computing localization stability under a well-chosen perturbation with accurate knowledge of the imaging system, we can robustly measure the confidence of individual localizations without ground-truth knowledge of the sample. We demonstrate that our method, termed Wasserstein-induced flux (WIF), measures the accuracy of various reconstruction algorithms directly on experimental 2D and 3D data of microtubules and amyloid fibrils. We further show that WIF confidences can be used to evaluate the mismatch between computational models and imaging data, enhance the accuracy and resolution of recon-structed structures, and discover hidden molecular heterogeneities. As a computational methodology, WIF is broadly applicable to any SMLM dataset, imaging system, and localization algorithm.

scholarly journals Entanglement-enhanced matter-wave interferometry in a high-finesse cavity

2021 ◽  
Author(s):  
James Thompson ◽  
Graham Greve ◽  
Chengyi Luo ◽  
Baochen Wu
Keyword(s):  
Cavity Qed ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Mean Field ◽  
New Physics ◽  
Matter Wave ◽  
Quantum Limit ◽  
Entangled State ◽  
Standard Quantum ◽  
Standard Quantum Limit

Abstract Entanglement is a fundamental resource that allows quantum sensors to surpass the standard quantum limit set by the quantum collapse of independent atoms. Collective cavity-QED systems have succeeded in generating large amounts of directly observed entanglement involving the internal degrees of freedom of laser-cooled atomic ensembles. Here we demonstrate cavity-QED entanglement of external degrees of freedom to realize a matter-wave interferometer of 700 atoms in which each individual atom falls freely under gravity and simultaneously traverses two paths through space while also entangled with the other atoms. We demonstrate both quantum non-demolition measurements and cavity-mediated spin interactions for generating squeezed momentum states with directly observed metrological gain 3.4^{+1.1}_{-0.9} dB and 2.5^{+0.6}_{-0.6} dB below the standard quantum limit respectively. An entangled state is for the first time successfully injected into a Mach-Zehnder light-pulse interferometer with 1.7^{+0.5}_{-0.5} dB of directly observed metrological enhancement. These results open a new path for combining particle delocalization and entanglement for inertial sensors, searches for new physics, particles, and fields, future advanced gravitational wave detectors, and accessing beyond mean-field quantum many-body physics.

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