Title |
Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions
|
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Published in |
Journal of Medical and Biological Engineering, July 2017
|
DOI | 10.1007/s40846-017-0297-2 |
Pubmed ID | |
Authors |
Angkoon Phinyomark, Giovanni Petri, Esther Ibáñez-Marcelo, Sean T. Osis, Reed Ferber |
Abstract |
The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called "topological data analysis" and directions for future research are outlined and discussed. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 6 | 15% |
Italy | 3 | 8% |
Canada | 3 | 8% |
France | 2 | 5% |
Brazil | 1 | 3% |
Japan | 1 | 3% |
United Kingdom | 1 | 3% |
Spain | 1 | 3% |
Finland | 1 | 3% |
Other | 4 | 10% |
Unknown | 16 | 41% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 22 | 56% |
Scientists | 14 | 36% |
Science communicators (journalists, bloggers, editors) | 2 | 5% |
Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 393 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 66 | 17% |
Student > Master | 59 | 15% |
Researcher | 58 | 15% |
Student > Bachelor | 36 | 9% |
Other | 18 | 5% |
Other | 58 | 15% |
Unknown | 98 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 97 | 25% |
Sports and Recreations | 48 | 12% |
Medicine and Dentistry | 32 | 8% |
Computer Science | 29 | 7% |
Nursing and Health Professions | 15 | 4% |
Other | 41 | 10% |
Unknown | 131 | 33% |