Simple method to reduce the effect of patient positioning variation on three-dimensional motion analysis during treadmill gait

Recently, three-dimensional (3D) closed curve trajectories of markers placed at strategic body locations, called <em>cyclograms</em> or <em>Lissajous-like graphs</em>, are used for treadmill gait analysis. A simple method is presented to reduce the effect of patient positioning variation. After breaking down movement into three components (anterior-posterior, medial-lateral and superior-inferior), the time-series data and time-inverted data are serially concatenated. A fast Fourier transform (FFT) is done, and a high-pass filter (except 0 Hz) is applied to the anterior-posterior and medial-lateral components. Next an inverse FFT is executed, and the posterior half of the outcome, corresponding to time-inverted data, is deleted. The 3D closed curve is then reconstructed. Results showed that the proposed method was able to reduce the effect of patient positioning variation. Since the adjusted curve is simply a symbolized gait pattern, the method might be useful as an adjunct tool in observational gait analysis.

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Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽

10.3390/s21030789 ◽

2021 ◽

Vol 21 (3) ◽

pp. 789

Author(s):

David Kreuzer ◽

Michael Munz

Keyword(s):

Gait Analysis ◽

Time Series Data ◽

Low Cost ◽

Analysis Data ◽

Gait Disorders ◽

Human Gait ◽

Series Data ◽

Ageing Society ◽

Time Motion ◽

Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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Container port throughput analysis and active management using control theory

Proceedings of the Institution of Mechanical Engineers Part M Journal of Engineering for the Maritime Environment ◽

10.1177/14750902211020875 ◽

2021 ◽

pp. 147509022110208

Author(s):

Cuong Truong Ngoc ◽

Xiao Xu ◽

Hwan-Seong Kim ◽

Duy Anh Nguyen ◽

Sam-Sang You

Keyword(s):

Control Theory ◽

Time Series Data ◽

Sliding Mode ◽

Container Terminal ◽

Three Dimensional ◽

Maritime Transportation ◽

Container Terminals ◽

Active Management ◽

Dynamical Analysis ◽

Series Data

This paper deals with three-dimensional (3D) model of competitive Lotka-Volterra equation to investigate nonlinear dynamics and control strategy of container terminal throughput and capacity. Dynamical behaviors are intensely explored by using eigenvalue evaluation, bifurcation analysis, and time-series data. The dynamical analysis is to show the stability with bifurcation of the competition and collaboration of multiple container terminals in the maritime transportation. Based on the chaotic analysis, the sliding mode control theory has been utilized for optimization of port operations under disruptions. Extensive numerical simulations have been conducted to validate the efficacy and reliability of the presented control algorithms. Particularly, the closed-loop system has been assessed through chaotic suppression and synchronization strategies for port management. Finally, the presented fundamental techniques can be utilized to provide managerial insights and solutions on efficient seaport operations that allow more timely and cost-effective decision making for port authorities in such a highly competitive environment.

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A New Method to Detect Outliers in High-frequency Time Series

International Journal of Statistics and Probability ◽

10.5539/ijsp.v8n1p16 ◽

2018 ◽

Vol 8 (1) ◽

pp. 16

Author(s):

Ilaria Lucrezia Amerise ◽

Agostino Tarsitano

Keyword(s):

Time Series ◽

High Frequency ◽

Time Series Data ◽

New Method ◽

Series Data ◽

Absolute Difference ◽

Data Preparation ◽

Simple Method ◽

Pass Through ◽

Nonparametric Procedure

The objective of this research is to develop a fast, simple method for detecting and replacing extreme spikes in high-frequency time series data. The method primarily consists  of a nonparametric procedure that pursues a balance between fidelity to observed data and smoothness. Furthermore, through examination of the absolute difference between original and smoothed values, the technique is also able to detect and, where necessary, replace outliers with less extreme data. Unlike other filtering procedures found in the literature, our method does not require a model to be specified for the data. Additionally, the filter makes only a single pass through the time series. Experiments  show that the new method can be validly used as a data preparation tool to ensure that time series modeling is supported by clean data, particularly in a complex context such as one with high-frequency data.

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Modeling Interactive Behaviors While Learning With Digitized Objects in Virtual Reality Environments

Cognitive and Affective Perspectives on Immersive Technology in Education - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-7998-3250-8.ch011 ◽

2020 ◽

pp. 215-234

Author(s):

Eric Poitras ◽

Kirsten R. Butcher ◽

Matthew P. Orr

Keyword(s):

Learning Environments ◽

Time Series Data ◽

Three Dimensional ◽

Synthetic Data ◽

Virtual Learning ◽

Virtual Learning Environments ◽

Series Data ◽

Student Behaviors ◽

Virtual Models ◽

Interaction Behaviors

This chapter outlines a framework for automated detection of student behaviors in the context of virtual learning environments. The components of the framework establish several parameters for data acquisition, preprocessing, and processing as a means to classify different types of behaviors. The authors illustrate these steps in training and evaluating a detector that differentiates between students' observations and functional behaviors while students interact with three-dimensional (3D) virtual models of dinosaur fossils. Synthetic data were generated in controlled conditions to obtain time series data from different channels (i.e., orientation from the virtual model and remote controllers) and modalities (i.e., orientation in the form of Euler angles and quaternions). Results suggest that accurate detection of interaction behaviors with 3D virtual models requires smaller moving windows to segment the log trace data as well as features that characterize orientation of virtual models in the form of quaternions. They discuss the implications for personalized instruction in virtual learning environments.

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A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter

Remote Sensing of Environment ◽

10.1016/j.rse.2004.03.014 ◽

2004 ◽

Vol 91 (3-4) ◽

pp. 332-344 ◽

Cited By ~ 947

Author(s):

Jin Chen ◽

Per. Jönsson ◽

Masayuki Tamura ◽

Zhihui Gu ◽

Bunkei Matsushita ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

High Quality ◽

Data Set ◽

Simple Method ◽

Ndvi Time Series

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A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky?Golay filter

Remote Sensing of Environment ◽

10.1016/s0034-4257(04)00080-x ◽

2004 ◽

Cited By ~ 2

Author(s):

J CHEN

Keyword(s):

Time Series ◽

Time Series Data ◽

Series Data ◽

High Quality ◽

Data Set ◽

Simple Method ◽

Ndvi Time Series

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Using Three-Dimensional Gait Data for Foot/Ankle Orthopaedic Surgery

The Open Orthopaedics Journal ◽

10.2174/1874325000903010089 ◽

2009 ◽

Vol 3 (1) ◽

pp. 89-95 ◽

Cited By ~ 7

Author(s):

Gwyneth de Vries ◽

Kevin Roy ◽

Victoria Chester

Keyword(s):

Gait Analysis ◽

Motion Capture ◽

Tendon Transfer ◽

Treatment Effectiveness ◽

Treatment Plan ◽

Three Dimensional ◽

Gait Pattern ◽

Posterior Tibial Tendon ◽

Post Surgery ◽

Stiff Knee

We present the case of a forty year old male who sustained a torn carotid during strenuous physical activity. This was followed by a right hemispheric stroke due to a clot associated with the carotid. Upon recovery, the patient’s gait was characterized as hemiparetic with a stiff-knee pattern, a fixed flexion deformity of the toe flexors, and a hindfoot varus. Based on clinical exams and radiographs, the surgical treatment plan was established and consisted of correction of the forefoot deformities, possible hamstrings lengthening, and tendon transfer of the posterior tibial tendon to the dorsolateral foot. To aid in surgical planning, a three-dimensional gait analysis was conducted using a state-of-the-art motion capture system. Data from this analysis provided insight into the pathomechanics of the patient’s gait pattern. A forefoot driven hindfoot varus was evident from the presurgical data and the tendon transfer procedure was deemed unnecessary. A computer was used in the OR to provide surgeons with animations of the patient’s gait and graphical results as needed. A second gait analysis was conducted 6 weeks post surgery, shortly after cast removal. Post-surgical gait data showed improved foot segment orientation and position. Motion capture data provides clinicians with detailed information on the multisegment kinematics of foot motion during gait, before and during surgery. Further, treatment effectiveness can be evaluated by repeating gait analyses after recovery.

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Control of Chaos in the Rössler System

Australian Journal of Physics ◽

10.1071/p96015 ◽

1997 ◽

Vol 50 (2) ◽

pp. 263 ◽

Cited By ~ 2

Author(s):

Stuart Corney

Keyword(s):

Periodic Orbits ◽

Time Series Data ◽

Control Method ◽

Three Dimensional ◽

Dynamical Variable ◽

Series Data ◽

External Parameter ◽

Unstable Periodic Orbits ◽

System A ◽

Linear Differential

The control method of Ott, Grebogi and Yorke (1990) as applied to the Rössler system, a set of three-dimensional non-linear differential equations, is examined. Using numerical time series data for a single dynamical variable the method was successfully employed to control several of the unstable periodic orbits in a three-dimensional embedding of the data. The method also failed for a number of unstable periodic orbits due to difficulties in linearising about the orbit or the tangential coincidence of the stable manifold and the motion of the orbit with external parameter.

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