Literature DB >> 32329647

Effects of digital filtering on peak acceleration and force measurements for artistic gymnastics skills.

Rhiannon A Campbell1,2, Elizabeth J Bradshaw3,4, Nick Ball1,5, Adam Hunter6, Wayne Spratford1,5.   

Abstract

Low-pass filters are ideal when filtering human movements, however the effectiveness of such filters relies on the correct selection of the cut-off frequency. The aim of this study was to determine the most appropriate filter cut-off for acceleration- and force-time data when measuring peak resultant acceleration (PRA) and ground reaction force (PRGRF) during gymnastics landings. Sixteen gymnasts executed backward handsprings and backward somersault landings onto a matted force plate while wearing four inertial measurement units (IMUs). Acceleration- and force-time data were filtered using a fourth-order Butterworth filter at different cut-off frequencies ranging from raw through to 250 Hz. Residual analysis plots were produced, and the PRGRF and PRA for all IMUs were calculated for each participant and skill at all cut-off frequencies. Descriptive statistics, model II linear regressions and Bland-Altman plots were conducted. Results indicated that a minimum 85 Hz cut-off is optimal. High cut-off frequencies (>80 Hz) showed good linear relationships and had minimal mean bias compared with raw values, indicating that either filtered (above ~85 Hz) or raw signals can be used. It is suggested that for applied sports settings no filtering is needed, however a minimum cut-off of 85 Hz should be implemented for research purposes.

Entities:  

Keywords:  Training; accelerometer; biomechanics; landing; load

Mesh:

Year:  2020        PMID: 32329647     DOI: 10.1080/02640414.2020.1757374

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  2 in total

1.  The Application of Artificial Intelligence Technology in Art Teaching Taking Architectural Painting as an Example.

Authors:  Jing Li; Bingyu Zhang
Journal:  Comput Intell Neurosci       Date:  2022-05-17

2.  The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching.

Authors:  Lijun Xiao; Yan Luo
Journal:  Comput Intell Neurosci       Date:  2022-06-13
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.