Authors:
Ivan Miguel Pires
1
;
Nuno M. Garcia
2
and
Maria Cristina Canavarro Teixeira
3
Affiliations:
1
University of Beira Interior and Altranportugal, Portugal
;
2
University of Beira Interior and Universidade Lusófona de Humanidades e Tecnologias, Portugal
;
3
Polytechnique Institute of Castelo Branco, Portugal
Keyword(s):
Mobile Application, Algorithm, Jump Flight Time, Smartphone, Accelerometer, Physical Training, Vertical Jump, Jumping, Mobile Devices, Pattern Recognition, Activity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Mining
;
Data Visualization
;
Databases and Information Systems Integration
;
Datamining
;
Devices
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Wearable Sensors and Systems
Abstract:
This paper describes the research and implementation and validation method of a smartphone application
that calculates a vertical jump flight time, using the data collected from the accelerometry sensors in a
smartphone. To validate the algorithm results, a statistical number of experiments were performed. While
recording the experimental data with a commodity smartphone, a bioPlux Research device equipped with a
pressure sensor and with a tri-axial accelerometer was also used to estimate the time the user was airborne
while jumping, as a golden standard. The pressure sensor was placed in a jump platform built in the
laboratory, and a tri-axial accelerometer was placed on the user’s waist. The data collected by this device
were compared with data obtained by smartphone in order to validate the algorithm and make the necessary
corrections. The research data and the developed application are available for download and further research
in a free and public repository.