Donahue Seth R, Hahn Michael E
Bowerman Sports Science Center, Department of Human Physiology, University of Oregon, Eugene, OR, United States.
Front Sports Act Living. 2023 Feb 13;5:974186. doi: 10.3389/fspor.2023.974186. eCollection 2023.
In laboratory experiments, biomechanical data collections with wearable technologies and machine learning have been promising. Despite the development of lightweight portable sensors and algorithms for the identification of gait events and estimation of kinetic waveforms, machine learning models have yet to be used to full potential. We propose the use of a Long Short Term Memory network to map inertial data to ground reaction force data gathered in a semi-uncontrolled environment. Fifteen healthy runners were recruited for this study, with varied running experience: novice to highly trained runners (<15 min 5 km race), and ages ranging from 18 to 64 years old. Force sensing insoles were used to measure normal foot-shoe forces, providing the standard for identification of gait events and measurement of kinetic waveforms. Three inertial measurement units (IMUs) were mounted to each participant, two bilaterally on the dorsal aspect of the foot and one clipped to the back of each participant's waistband, approximating their sacrum. Data input into the Long Short Term Memory network were from the three IMUs and output were estimated kinetic waveforms, compared against the standard of the force sensing insoles. The range of RMSE for each stance phase was from 0.189-0.288 BW, which is similar to multiple previous studies. Estimation of foot contact had an = 0.795. Estimation of kinetic variables varied, with peak force presenting the best output with an = 0.614. In conclusion, we have shown that at controlled paces over level ground a Long Short Term Memory network can estimate 4 s temporal windows of ground reaction force data across a range of running speeds.
在实验室实验中，利用可穿戴技术和机器学习进行生物力学数据采集一直很有前景。尽管已经开发出了用于识别步态事件和估计动力学波形的轻质便携式传感器及算法，但机器学习模型尚未得到充分利用。我们建议使用长短期记忆网络将惯性数据映射到在半受控环境中收集的地面反作用力数据。本研究招募了15名健康跑步者，他们的跑步经验各不相同：从新手到训练有素的跑步者（5公里比赛用时不到15分钟），年龄在18岁至64岁之间。力感应鞋垫用于测量足部与鞋子之间的垂直力，为步态事件识别和动力学波形测量提供标准。为每位参与者安装了三个惯性测量单元（IMU），其中两个双侧安装在足背，另一个夹在每位参与者的腰带后部，靠近骶骨。输入长短期记忆网络的数据来自三个IMU，输出为估计的动力学波形，并与力感应鞋垫的标准进行比较。每个站立阶段的均方根误差范围为0.189 - 0.288倍体重，这与之前的多项研究相似。足部接触的估计值为0.795。动力学变量的估计值各不相同，峰值力的输出最佳，为0.614。总之，我们已经表明，在水平地面上以受控速度跑步时，长短期记忆网络可以估计一系列跑步速度下4秒时间窗口的地面反作用力数据。
