DDSS: denge decision support system to recommend the athlete-specific workouts on balance data


ABİDİN D., ERDEM M. G.

Neural Computing and Applications, cilt.34, sa.16, ss.13969-13986, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34 Sayı: 16
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00521-022-07208-2
  • Dergi Adı: Neural Computing and Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.13969-13986
  • Anahtar Kelimeler: Decision support systems, Machine learning, Deep learning, Classification, Balance boards
  • Manisa Celal Bayar Üniversitesi Adresli: Evet

Özet

Monitoring the balance conditions and physical abilities of athletes is important to track their current situations which enables us to apply appropriate training programs for recovery. For different branches of sports, there are three main balance board types to be used; not swaying board (i.e. Wii board), semi-spherical fulcrum (i.e. Wobble board), and springboard (i.e. Spring Balance Board). In this study, the Balance springboard, which is new to the literature, is used. The springboard equipped with sensors uses Bluetooth technology to transmit collected balance data. There are various previous studies developed for assessing the balance performance of athletes regarding the first two types of balance-boards. Most of them are based on statistical analysis and machine learning (ML) techniques. In this study, the usage of a shallow deep learning model trained with the balance data, which is a contribution to the literature, gathered from the springboard is introduced. This model (DDSS, Denge Decision Support System) is compared with the base ANN model -which leads the study to tend our DDSS model- and ML techniques. Our DDSS model outperforms when compared with the base ANN and ML techniques, Sequential Minimal Optimization and Random Forest, and offers appropriate training program suggestions with a success rate of 92.11%.