Abstract
Technological advances in the areas of three-dimensional (3D) body scanning,
in-vivo imaging and novel forms of motion capture and data analytics (e.g. deep
learning neural networks) are rapidly bridging the lab versus field-based nexus
that has historically plagued the applied sport biomechanist. Similarly,
exponential advances in hardware and computer processing power has
witnessed the emergence of the personalised 'digital athlete', an overarching
vision that facilitates, via the integration of multiple technologies, real-time
biomechanical data collection, modelling and reporting for immediate
biofeedback.
in-vivo imaging and novel forms of motion capture and data analytics (e.g. deep
learning neural networks) are rapidly bridging the lab versus field-based nexus
that has historically plagued the applied sport biomechanist. Similarly,
exponential advances in hardware and computer processing power has
witnessed the emergence of the personalised 'digital athlete', an overarching
vision that facilitates, via the integration of multiple technologies, real-time
biomechanical data collection, modelling and reporting for immediate
biofeedback.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 34th International Conference of Biomechanics in Sport |
| Editors | Michiyoshi Ae, Yasushi Enomoto, Norihisa Fujii, Hideki Takagi |
| Place of Publication | Germany |
| Publisher | International Society of Biomechanics in Sports |
| Pages | 23-26 |
| Publication status | Published - 2016 |
| Event | 34th International Conference on Biomechanics in Sports - Tsukuba, Japan, Tsukuba, Japan Duration: 18 Jul 2016 → 22 Jul 2016 |
Publication series
| Name | ISBS Conference Proceedings Archive |
|---|---|
| ISSN (Print) | 1999-4168 |
Conference
| Conference | 34th International Conference on Biomechanics in Sports |
|---|---|
| Country/Territory | Japan |
| City | Tsukuba |
| Period | 18/07/16 → 22/07/16 |