Sheng Wentao, Gao Tianyu, Liang Keyao, Wang Yumo
School of Mechanical Engineering, Jiangsu University of Technology (JSUT), Changzhou 213001, China.
School of Intelligent Manufacturing, Nanjing University of Science and Technology (NJUST), Nanjing 210094, China.
Biomimetics (Basel). 2024 Apr 27;9(5):266. doi: 10.3390/biomimetics9050266.
The prediction of upcoming circular walking during linear walking is important for the usability and safety of the interaction between a lower limb assistive device and the wearer. This study aims to build a bilateral elimination rule-based finite class Bayesian inference system (BER-FC-BesIS) with the ability to predict the transition between circular walking and linear walking using inertial measurement units.
Bilateral motion data of the human body were used to improve the recognition and prediction accuracy of BER-FC-BesIS.
The mean predicted time of BER-FC-BesIS in predicting the left and right lower limbs' upcoming steady walking activities is 119.32 ± 9.71 ms and 113.75 ± 11.83 ms, respectively. The mean time differences between the predicted time and the real time of BER-FC-BesIS in the left and right lower limbs' prediction are 14.22 ± 3.74 ms and 13.59 ± 4.92 ms, respectively. The prediction accuracy of BER-FC-BesIS is 93.98%.
Upcoming steady walking activities (e.g., linear walking and circular walking) can be accurately predicted by BER-FC-BesIS innovatively.
This study could be helpful and instructional to improve the lower limb assistive devices' capabilities of walking activity prediction with emphasis on non-linear walking activities in daily living.
预测直线行走过程中即将发生的圆周行走对于下肢辅助装置与穿戴者之间交互的可用性和安全性至关重要。本研究旨在构建一种基于双边消除规则的有限类贝叶斯推理系统（BER-FC-BesIS），该系统能够利用惯性测量单元预测圆周行走和直线行走之间的转换。
使用人体的双边运动数据来提高BER-FC-BesIS的识别和预测准确性。
BER-FC-BesIS预测左右下肢即将进行的稳定行走活动的平均预测时间分别为119.32±9.71毫秒和113.75±11.83毫秒。BER-FC-BesIS在左右下肢预测中预测时间与实际时间的平均时间差分别为14.22±3.74毫秒和13.59±4.92毫秒。BER-FC-BesIS的预测准确率为93.98%。
BER-FC-BesIS能够创新性地准确预测即将进行的稳定行走活动（如直线行走和圆周行走）。
本研究对于提高下肢辅助装置预测行走活动的能力，尤其是日常生活中的非线性行走活动，可能会有所帮助并具有指导意义。
