Recognizing and quantifying human movement patterns through haptic-based applications

2006 ◽  
Author(s):  
M. Orozco ◽  
A. El Saddik
Keyword(s):  
Movement Patterns ◽  
Human Movement

scholarly journals Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko
Keyword(s):  
Spatial Scales ◽  
Predictive Ability ◽  
Movement Patterns ◽  
Human Movement ◽  
Continental Scale ◽  
Movement Characteristics ◽  
Human Movements ◽  
Movement Models ◽  
Epidemiology Data

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

What is the Biomechanical Principle of Forehand Smash in Badminton? - Article Review

2020 ◽  
Vol 7 (2) ◽  
pp. 221-229
Author(s):  
Gilang Estina Putra ◽  
Ria Lumintuarso
Keyword(s):  
Movement Patterns ◽  
Movement Pattern ◽  
Human Movement ◽  
Biomechanical Analysis ◽  
Future Research ◽  
Good Movement

SummaryThe pattern of human movement is strongly influenced by biomotor conditions, age development will affect the level of biomotor maturity. Forehand smash is one of the techniques that every badminton athlete must master well. To do this stroke well, a good movement pattern is needed, biomotor maturity which in this study is related to the age of the athlete. So that the development of age will cause differences in movement patterns which in this study analyzes the forehand smash movement patterns in badminton based on age. It is known that the most efficient badminton technique for gaining points in badminton is the smash. The smash-hit is more likely to produce a fast shuttle lock rate performed using the forehand smash technique. So that in this study the writer will analyze a technique in badminton, namely the forehand smash technique. These findings contribute to future research on the biomechanical analysis of forehand smash motion in badminton.

scholarly journals The Use of Census Migration Data to Approximate Human Movement Patterns across Temporal Scales

PLoS ONE ◽  
2013 ◽  
Vol 8 (1) ◽  
pp. e52971 ◽  
Author(s):  
Amy Wesolowski ◽  
Caroline O. Buckee ◽  
Deepa K. Pindolia ◽  
Nathan Eagle ◽  
David L. Smith ◽  
...  
Keyword(s):  
Movement Patterns ◽  
Human Movement ◽  
Temporal Scales ◽  
Migration Data

scholarly journals Fine-scale GPS tracking to quantify human movement patterns and exposure to leptospires in the urban slum environment

2018 ◽  
Vol 12 (8) ◽  
pp. e0006752 ◽  
Author(s):  
Katharine A. Owers ◽  
Juliana Odetunde ◽  
Rosan Barbosa de Matos ◽  
Gielson Sacramento ◽  
Mayara Carvalho ◽  
...  
Keyword(s):  
Movement Patterns ◽  
Human Movement ◽  
Urban Slum ◽  
Gps Tracking ◽  
Fine Scale

Evaluation of Control Interfaces for Desktop Virtual Environments

2015 ◽  
Vol 24 (4) ◽  
pp. 322-334 ◽  
Author(s):  
Tyler Thrash ◽  
Mubbasir Kapadia ◽  
Mehdi Moussaid ◽  
Christophe Wilhelm ◽  
Dirk Helbing ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Real World ◽  
Precise Measurement ◽  
Movement Patterns ◽  
Human Movement ◽  
Transportation Systems ◽  
Spatial Behavior ◽  
Limited Resources ◽  
Movement Trajectories ◽  
Crowd Management

Tracking and analyzing the movement trajectories of individuals and groups is an important problem with applications in crowd management and the development of transportation systems. However, real-world tracking is limited due to the size of the trackable area and the precision with which a person can be tracked. Experiments in virtual environments have many advantages, including practically unlimited sizes and the precise measurement of spatial behavior. However, the generalizability of research using virtual environments to real-world scenarios is often limited by the translation of participants’ movements to those of their avatars. We compared human movement patterns in virtual environments with different control interfaces: a handheld joystick, a mouse-and-keyboard setup, and a keyboard-only setup. With each of these controls, participants completed several movement-related tasks of varying difficulty in a limited amount of time. Questionnaires indicated that participants preferred the mouse-and-keyboard setup over the other two setups. Standard performance measures suggested that the joystick underperformed in a variety of tasks. Movement trajectories in the final task indicated that each of the control setups produced somewhat realistic behavior, despite some apparent differences from real-world trajectories. Overall, the results indicated that, given limited resources, mouse-and-keyboard setups consistently outperform joysticks and produce realistic movement patterns.

UNDERSTANDING THE CORRELATION OF THE PROPERTIES OF HUMAN MOVEMENT PATTERNS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450019
Author(s):  
RAJIB RANJAN MAITI ◽  
ARUN MALLYA ◽  
ANIMESH MUKHERJEE ◽  
NILOY GANGULY
Keyword(s):  
Movement Patterns ◽  
Human Movement ◽  
Experimental Setup ◽  
Mobility Pattern ◽  
Mobility Patterns ◽  
Input Parameters ◽  
Layered Approach

Mobility of humans plays an important role in shaping the performance of various services in mobile and location dependent networks. In this paper, we amalgamate and summarize the parameters reported in various trace analyses, and explore the inter-dependencies among the parameters using a layered approach. We then identify using an experimental setup (a) several roadblocks in generating an intended synthesis model, (b) the changes in the mobility patterns with variation in inputs (e.g., the underlying map), and (c) the changes in efficiency of a given service (here we have considered simple broadcasting) with change in the input parameters. Interestingly, we note that the efficiency of the service does not necessarily depend on the mobility pattern and one needs to be extremely careful before drawing a direct correlation between the two.

Sign in / Sign up

Export Citation Format

Share Document