scholarly journals A New Method for Time Normalization Based on the Continuous Phase: Application to Neck Kinematics

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3138
Author(s):  
Carlos Llopis-Albert ◽  
William Ricardo Venegas Toro ◽  
Nidal Farhat ◽  
Pau Zamora-Ortiz ◽  
Álvaro Felipe Page Del Pozo
Keyword(s):  
Functional Data ◽  
Time Scale ◽  
Computational Cost ◽  
Human Movement ◽  
Continuous Phase ◽  
Normalization Method ◽  
Movement Data ◽  
Flexion Extension ◽  
Angular Velocities ◽  
Scale Normalization

There is growing interest in analyzing human movement data for clinical, sport, and ergonomic applications. Functional Data Analysis (FDA) has emerged as an advanced statistical method for overcoming the shortcomings of traditional analytic methods, because the information about continuous signals can be assessed over time. This paper takes the current literature a step further by presenting a new time scale normalization method, based on the Hilbert transform, for the analysis of functional data and the assessment of the effect on the variability of human movement waveforms. Furthermore, a quantitative comparison of well-known methods for normalizing datasets of temporal biomechanical waveforms using functional data is carried out, including the linear normalization method and nonlinear registration methods of functional data. This is done using an exhaustive database of human neck flexion-extension movements, which encompasses 423 complete cycles of 31 healthy subjects measured in two trials of the experiment on different days. The results show the advantages of the novel method compared to existing techniques in terms of computational cost and the effectiveness of time-scale normalization on the phase differences of curves and on the amplitude of means, which are assessed by Root Mean Square (RMS) values of functional means of angles, angular velocities, and angular accelerations. Additionally, the confidence intervals are obtained through a bootstrapping process.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Arm Movement Analysis in Human Daily Life for Improving Durability of Artificial Elbow Joint

2020 ◽  
Author(s):  
Chihiro Kamio ◽  
Tatsuhito Aihara ◽  
Gaku Minorikawa
Keyword(s):  
Daily Life ◽  
Human Life ◽  
Arm Movement ◽  
Movement Analysis ◽  
Human Movement ◽  
Healthcare Applications ◽  
Artificial Joints ◽  
Measuring Device ◽  
Movement Data ◽  
Set Up

Abstract Human movement data can contribute to the quality improvement of industrial and medical products affected by such movement. Such data can be used to improve the quality of industrial products as well as in healthcare applications, such as the development of artificial joints. To develop and design artificial joints with enhance durability, it is necessary to set up standards of durability using human movement data in daily life. The aim of this study is to obtain data that contributes to the improvement in durability of artificial elbow joints. We have developed a wearable device that can measure its self-acceleration, angular velocity, and quaternions to collect human movement data continuously for long-term. Additionally, we collected the arm movement data of 30 participants using the developed device. The participants of this study carried on with their normal lives with the measuring device worn on their wrist. This study calculated the posture of the wrist over time using quaternions and mainly analyzed posture changes. We clarified the characteristics and trends of the movement of bending the elbow in daily human life.

scholarly journals Multi-class classification of biomechanical data: A functional LDA approach based on multi-class penalized functional PLS

2019 ◽  
pp. 1471082X1787115
Author(s):  
M. Carmen Aguilera-Morillo ◽  
Ana M. Aguilera
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Functional Data ◽  
Repeated Measures ◽  
Linear Acceleration ◽  
Human Movement ◽  
Angular Rotation ◽  
Kinematic Data ◽  
Linear Discriminant ◽  
Multi Class Classification

A functional linear discriminant analysis approach to classify a set of kinematic data (human movement curves of individuals performing different physical activities) is performed. Kinematic data, usually collected in linear acceleration or angular rotation format, can be identified with functions in a continuous domain (time, percentage of gait cycle, etc.). Since kinematic curves are measured in the same sample of individuals performing different activities, they are a clear example of functional data with repeated measures. On the other hand, the sample curves are observed with noise. Then, a roughness penalty might be necessary in order to provide a smooth estimation of the discriminant functions, which would make them more interpretable. Moreover, because of the infinite dimension of functional data, a reduction dimension technique should be considered. To solve these problems, we propose a multi-class approach for penalized functional partial least squares (FPLS) regression. Then linear discriminant analysis (LDA) will be performed on the estimated FPLS components. This methodology is motivated by two case studies. The first study considers the linear acceleration recorded every two seconds in 30 subjects, related to three different activities (walking, climbing stairs and down stairs). The second study works with the triaxial angular rotation, for each joint, in 51 children when they completed a cycle walking under three conditions (walking, carrying a backpack and pulling a trolley). A simulation study is also developed for comparing the performance of the proposed functional LDA with respect to the corresponding multivariate and non-penalized approaches.

scholarly journals Differences in Trunk Strength Between Weightlifters and Wrestlers

2019 ◽  
Vol 67 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Amira Ben Moussa Zouita ◽  
Sghaier Zouita ◽  
Catherine Dziri ◽  
Matt Brughelli ◽  
David G. Behm ◽  
...  
Keyword(s):  
Average Power ◽  
Peak Torque ◽  
Control Group ◽  
Trunk Flexion ◽  
Extensor Muscles ◽  
Flexion Extension ◽  
Angular Velocities ◽  
High Level ◽  
Trunk Extension ◽  
Trunk Strength

AbstractInvestigations of trunk strength with high-level athletes are limited. The purpose of this study was to compare maximal concentric isokinetic trunk extension and flexion torque, power, and strength ratios between high-level weightlifters (n = 20), wrestlers (n = 20) and a control (n = 25) population. Isokinetic dynamometry was used to evaluate peak torque, power and strength ratios during seated trunk extension/flexion actions at 60°/s and 180°/s. There were no significant anthropometric differences between groups. Overall, trunk isokinetic force variables as a function of the increase in angular velocity, showed a decrease in peak torque, but an increase in power (athletes and controls). Compared to the control group, athletes demonstrated significantly higher trunk extension torque (+67.05 N·m, ES = 0.81) and power (+49.28 N·m, ES = 0.82) at 60°/s and 180°/s, respectively. Athletes produced significantly greater trunk flexion-extension ratios at 60°/s and 180°/s (ES = 0.80-0.47) than controls. Weightlifters and wrestlers exhibited significantly higher extensor than flexor torque at all angular velocities. Weightlifters demonstrated greater torque (ES = 0.79) than wrestlers at 60°/s. The wrestlers’ average power was significantly higher (ES = 0.43) than weightlifters at 180°/s. There were no significant ratio differences between wrestlers (66.23%) and weightlifters (72.06%). Weightlifters had stronger extensor muscles at 60°/s, whereas wrestlers had higher power at 180°/s for extensor muscles. It was postulated that the extensor muscles were stronger than the flexors to ensure trunk stabilisation, and for prevention of injuries. These differences seem to be associated to the movements that occur in each sport in terms of both muscle actions and contractile forces.

scholarly journals BIG BICYCLE DATA PROCESSING: FROM PERSONAL DATA TO URBAN APPLICATIONS

2016 ◽  
Vol III-2 ◽  
pp. 173-179
Author(s):  
C. J. Pettit ◽  
S. N. Lieske ◽  
S. Z. Leao
Keyword(s):  
Hot Spots ◽  
Smart Phone ◽  
Personal Data ◽  
Human Movement ◽  
Relevant Information ◽  
Policy Makers ◽  
Movement Data ◽  
The City ◽  
Smart Phone Application ◽  
Source Of Information

Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog. We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing the data and conducting higher volume and more extensive analysis.

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