Pitto Lorenzo, Simon Frédéric R, Ertel Geoffrey N, Gauchard Gérome C, Mornieux Guillaume
Development Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, France.
CARE Grand Est, Université de Lorraine, 54000 Nancy, France.
Sensors (Basel). 2025 Jan 6;25(1):279. doi: 10.3390/s25010279.
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes. The current state-of-the-art methodologies for rowing performance analysis involve the installation of dedicated instrumented equipment, with the most commonly employed systems being PowerLine and BioRow. This procedure can be both expensive and time-consuming, thus limiting trainers' ability to monitor athletes. In this study, we developed an easier-to-install and cheaper method for estimating rowers' forces and powers based only on cable position sensors for ergometer rowing and inertial measurement units (IMUs) and GPS for scull rowing. We used data from 12 and 11 rowers on ergometer and on boat, respectively, to train a long short-term memory (LSTM) network. The LSTM was able to reconstruct the forces and power at the gate with an overall mean absolute error of less than 5%. The reconstructed forces and power were able to reveal inter-subject differences in technique, with an accuracy of 93%. Performing leave-one-out validation showed a significant increase in error, confirming that more subjects are needed in order to develop a tool that could be generalizable to external athletes.
分析赛艇运动中的表现，例如分析在测功仪或赛艇上产生的力和功率输出曲线，是教练和运动员的首要任务。当前用于赛艇表现分析的最先进方法涉及安装专用的仪器设备，最常用的系统是PowerLine和BioRow。这个过程既昂贵又耗时，从而限制了教练监控运动员的能力。在本研究中，我们开发了一种更易于安装且成本更低的方法，仅基于测功仪赛艇的电缆位置传感器以及单人双桨赛艇的惯性测量单元（IMU）和全球定位系统（GPS）来估算赛艇运动员的力和功率。我们分别使用了来自12名和11名在测功仪和赛艇上训练的赛艇运动员的数据来训练长短期记忆（LSTM）网络。该LSTM能够在门控时重建力和功率，总体平均绝对误差小于5%。重建的力和功率能够揭示受试者之间的技术差异，准确率为93%。进行留一法验证显示误差显著增加，这证实了为开发一种可推广到其他运动员的工具需要更多受试者。
