The purpose of the current study was to compare the ability of five different methods to estimate eccentric–concentric and concentric-only bench-press 1RM from load–velocity profile data. Smith machine bench-press tests were performed in an eccentric–concentric (n = 192) and a concentric-only manner (n = 176) while mean concentric velocity was registered using a linear position transducer. Load–velocity profiles were derived from incremental submaximal load (40–80% 1RM) tests. Five different methods were used to calculate 1RM using the slope, intercept, and velocity at 1RM (minimum velocity threshold—MVT) from the load–velocity profiles: calculation with individual MVT, calculation with group average MVT, multilinear regression without MVT, regularized regression without MVT, and an artificial neural network without MVT. Mean average errors for all methods ranged from 2.7 to 3.3 kg. Calculations with individual or group MVT resulted in significant overprediction of eccentric–concentric 1RM (individual MVT: difference = 0.76 kg, p = 0.020, d = 0.17; group MVT: difference = 0.72 kg, p = 0.023, d = 0.17). The multilinear and regularized regression both resulted in the lowest errors and highest correlations. The results demonstrated that bench-press 1RM can be accurately estimated from load–velocity data derived from submaximal loads and without MVT. In addition, results showed that multilinear regression can be used to estimate bench-press 1RM. Collectively, the findings and resulting equations should be helpful for strength and conditioning coaches as they would help estimating 1RM without MVT data.

中文翻译：

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使用机器学习和速度分析来估计卧推练习的两个变体的1-重复最大值

本研究的目的是比较五种不同方法从载荷－速度剖面数据中估计偏心-同心和仅同心台式压力机1RM的能力。Smith机器卧推试验以偏心-同心（n = 192）和仅同心的方式（n = 176）使用线性位置传感器记录平均同心速度。载荷-速度曲线是从增量最大载荷（40-80％1RM）测试中得出的。根据载荷-速度曲线，使用了五种不同的方法来计算1RM的斜率，截距和1RM的速度（最小速度阈值-MVT）：使用单个MVT进行计算，使用组MVT进行计算，不使用MVT进行多线性回归，正则回归没有MVT的人，以及没有MVT的人工神经网络。所有方法的平均平均误差范围为2.7至3.3千克。使用单个或一组MVT进行的计算会导致偏心-同心1RM的严重过高预测（单个MVT：差异= 0.76 kg，p = 0.020，d = 0.17； MVT组：差异= 0.72 kg，p = 0.023，d = 0.17）。多线性和正则回归均导致最低的误差和最高的相关性。结果表明，卧推压力机1RM可以根据最大负载而没有MVT的负载-速度数据准确估算。此外，结果表明，多元线性回归可用于估计卧推1RM。总的来说，这些发现和得出的方程式将对力量和条件教练有帮助，因为他们将有助于在没有MVT数据的情况下估计1RM。