Chen Vincent C F, Chen Shih-Wei
Engineering Science, Loyola University Chicago, Chicago, IL, USA.
Engineering Science, Loyola University Chicago, Chicago, IL, USA.
Clin Biomech (Bristol). 2018 Dec;60:30-38. doi: 10.1016/j.clinbiomech.2018.10.003. Epub 2018 Oct 4.
In this study, we seek to replace conventional force platforms with a single accelerometer for measuring Center of Pressure trajectories, in order to achieve portability and convenience without sacrificing accuracy.
We measure the actual Anterior/Posterior and Medial/Lateral Center of Pressure trajectories of ten healthy young subjects using a force platform, and compare them with estimated measurements derived from accelerometer signals collected from three body locations (upper trunk, waist, and lower thigh) using three machine learning algorithms (Neural Network, Genetic Algorithm, and Adaptive Network-based Fuzzy Inference System). Error ratios and correlation coefficients corresponding to body locations were compared via one-way repeated-measures ANOVA. The ratios and coefficients corresponding to the three algorithms were also compared using the same approach.
Estimated Anterior/Posterior trajectories indicated that measurements collected from the waist provided the lowest margins of error (8.1-8.4% v. 12.1-13.4%, P ≤ .001) and the highest correlation (.95 v. .82-.86, P ≤ .032). Estimated Medial/Lateral trajectories indicated that measurements collected from both the waist and thigh, as compared to the upper trunk, provided lower margins of error (7.0-7.3% v. 8.5-10.8%). In general, the waist is the better accelerometer attachment location.
The results of our study corroborate our deduction that the high correlation between Center of Pressure and body's Center of Mass provides the rationale to place the single accelerometer close to the waist for Center of Pressure estimations. This study also supports the feasibility of using one single accelerometer programmed with algorithms for similar clinical applications.
在本研究中,我们试图用单个加速度计取代传统的测力平台来测量压力中心轨迹,以便在不牺牲准确性的前提下实现便携性和便利性。
我们使用测力平台测量了10名健康年轻受试者的实际前后和内外侧压力中心轨迹,并将其与使用三种机器学习算法(神经网络、遗传算法和基于自适应网络的模糊推理系统)从三个身体部位(上躯干、腰部和大腿下部)收集的加速度计信号得出的估计测量值进行比较。通过单向重复测量方差分析比较了与身体部位对应的误差率和相关系数。还使用相同方法比较了与三种算法对应的比率和系数。
估计的前后轨迹表明,从腰部收集的测量值误差幅度最低(8.1 - 8.4%对12.1 - 13.4%,P≤.001)且相关性最高(.95对.82 -.86,P≤.032)。估计的内外侧轨迹表明,与上躯干相比,从腰部和大腿收集的测量值误差幅度更低(7.0 - 7.3%对8.5 - 10.8%)。一般来说,腰部是更好的加速度计附着位置。
我们的研究结果证实了我们的推断,即压力中心与身体质心之间的高度相关性为将单个加速度计放置在靠近腰部位置以进行压力中心估计提供了理论依据。本研究还支持了使用一个通过算法编程的单个加速度计用于类似临床应用的可行性。