This study modified an electromyography-assisted optimization approach for predicting lumbar spine loading while walking with backpack loads. The modified-electromyography-assisted optimization approach eliminated the electromyography measurement at maximal voluntary contraction and adopted a linear electromyography-force relationship. Moreover, an optimal lower boundary condition for muscle gain was introduced to constrain the trunk muscle co-activation. Anthropometric information of 10 healthy young men as well as their kinematic, kinetic, and electromyography data obtained while walking with backpack loads were used as inputs in this study. A computational algorithm was used to find and analyse the sensitivity of the optimal lower boundary condition for achieving minimum deviation of the modified-electromyography-assisted optimization approach from the electromyography-assisted optimization approach for predicting lumbosacral joint compression force. Results validated that the modified-electromyography-assisted optimization approach (at optimal lower boundary condition of 0.92) predicted on average, a non-significant deviation in peak lumbosacral joint compression force of -18 N, a standard error of 9 N, and a root mean square difference in force profile of 73.8 N. The modified-electromyography-assisted optimization approach simplified the experimental process by eliminating the electromyography measurement at maximal voluntary contraction and provided comparable estimations for lumbosacral joint compression force that is also applicable to patients or individuals having difficulty in performing the maximal voluntary contraction activity.

中文翻译：

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改进的肌电辅助优化方法，可预测背负背包行走时的腰椎负荷。

这项研究修改了肌电辅助优化方法，以预测背负背包行走时的腰椎负荷。改进的肌电图辅助优化方法消除了最大自愿收缩时的肌电图测量，并采用了线性肌电图-力关系。此外，引入了用于肌肉获得的最佳下边界条件以约束躯干肌肉的共激活。这项研究使用了10名健康年轻人的人体测量学信息，以及在背负背包时行走时获得的运动学，动力学和肌电图数据。使用计算算法来发现和分析最佳下边界条件的敏感性，以实现改进的肌电图辅助优化方法与预测腰s关节压迫力的肌电图辅助优化方法的最小偏差。结果验证了改进的肌电辅助优化方法（在最佳下边界条件为0.92时）的平均预测结果，腰s关节峰值压缩力的无明显偏差为-18 N，标准误差为9 N和根力分布的均方差为73.8 N.