From Raw to Big Data in Endurance Running
Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports enviro...
Shranjeno v:
| Glavni avtor: | |
|---|---|
| Format: | Online |
| Jezik: | angleščina |
| Izdano: |
FAU University Press
2025
|
| Teme: | |
| Online dostop: | ONIX_20251215T160703_9783961475391_6 |
| Oznake: |
Brez oznak, prvi označite!
|
| _version_ | 1869524735877971968 |
|---|---|
| author | Zrenner, Markus |
| author_browse | Zrenner, Markus |
| author_facet | Zrenner, Markus |
| author_sort | Zrenner, Markus |
| collection | Directory of Open Access Books |
| description | Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports environments outside of laboratories. One of the first sports disciplines, where many athletes have been using wearable devices, is endurance running. With the rising popularity of smartphones, smartwatches and inertial measurement units (IMUs), many runners started to track their performance and keep a digital training diary. Due to the high number of runners worldwide, which transferred their data of wearables to online fitness platforms, large databases were created, which enable Big Data analysis of running data. This kind of analysis offers the potential to conduct longitudinal sports science studies on a larger number of participants than ever before. In this dissertation, both studies showing how to extract endurance running-related parameters from raw data of foot-mounted IMUs as well as a Big Data study with running data from a fitness platform are presented. |
| format | Online |
| id | doab-20.500.12854ir-170239 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1702392025-12-16T05:33:01Z From Raw to Big Data in Endurance Running Zrenner, Markus Machine Learning Maschinelles Lernen Big Data Wearable Computer Data Science Marathonlauf Endurance Running thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJS Sensors thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports environments outside of laboratories. One of the first sports disciplines, where many athletes have been using wearable devices, is endurance running. With the rising popularity of smartphones, smartwatches and inertial measurement units (IMUs), many runners started to track their performance and keep a digital training diary. Due to the high number of runners worldwide, which transferred their data of wearables to online fitness platforms, large databases were created, which enable Big Data analysis of running data. This kind of analysis offers the potential to conduct longitudinal sports science studies on a larger number of participants than ever before. In this dissertation, both studies showing how to extract endurance running-related parameters from raw data of foot-mounted IMUs as well as a Big Data study with running data from a fitness platform are presented. 2025-12-16T05:32:58Z 2025-12-16T05:32:58Z 2025-12-15T15:08:50Z 2022 book ONIX_20251215T160703_9783961475391_6 https://library.oapen.org/handle/20.500.12657/109175 9783961475391 9783961475384 https://directory.doabooks.org/handle/20.500.12854/170239 eng FAU Studien aus der Informatik open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/109175/1/9783961475391.pdf FAU University Press 10.25593/978-3-96147-539-1 10.25593/978-3-96147-539-1 2c600dea-eece-4066-87be-da335e323fdb 9783961475391 9783961475384 179 Erlangen open access |
| spellingShingle | Machine Learning Maschinelles Lernen Big Data Wearable Computer Data Science Marathonlauf Endurance Running thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJS Sensors thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general Zrenner, Markus From Raw to Big Data in Endurance Running |
| title | From Raw to Big Data in Endurance Running |
| title_full | From Raw to Big Data in Endurance Running |
| title_fullStr | From Raw to Big Data in Endurance Running |
| title_full_unstemmed | From Raw to Big Data in Endurance Running |
| title_short | From Raw to Big Data in Endurance Running |
| title_sort | from raw to big data in endurance running |
| topic | Machine Learning Maschinelles Lernen Big Data Wearable Computer Data Science Marathonlauf Endurance Running thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJS Sensors thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general |
| topic_facet | Machine Learning Maschinelles Lernen Big Data Wearable Computer Data Science Marathonlauf Endurance Running thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJS Sensors thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPH Data science and analysis: general |
| url | ONIX_20251215T160703_9783961475391_6 |
| work_keys_str_mv | AT zrennermarkus fromrawtobigdatainendurancerunning |