Abstract
The paper puts forward an on-board strategy for a training model and develops a real-time human locomotion mode recognition study based on a trained model utilizing two inertial measurement units (IMUs) of robotic transtibial prosthesis. Three transtibial amputees were recruited as subjects in this study to finish five locomotion modes (level ground walking, stair ascending, stair descending, ramp ascending, and ramp descending) with robotic prostheses. An interaction interface was designed to collect sensors' data and instruct to train model and recognition. In this study, the analysis of variance ratio (no more than 0.05) reflects the good repeatability of gait. The on-board training time for SVM (Support Vector Machines), QDA (Quadratic Discriminant Analysis), and LDA (Linear discriminant analysis) are 89, 25, and 10 s based on a 10,000 × 80 training data set, respectively. It costs about 13.4, 5.36, and 0.067 ms for SVM, QDA, and LDA for each recognition process. Taking the recognition accuracy of some previous studies and time consumption into consideration, we choose QDA for real-time recognition study. The real-time recognition accuracies are 97.19 ± 0.36% based on QDA, and we can achieve more than 95% recognition accuracy for each locomotion mode. The receiver operating characteristic also shows the good quality of QDA classifiers. This study provides a preliminary interaction design for human–machine prosthetics in future clinical application. This study just adopts two IMUs not multi-type sensors fusion to improve the integration and wearing convenience, and it maintains comparable recognition accuracy with multi-type sensors fusion at the same time.
Highlights
Robotic prosthetics plays an important role in assisting with the daily walking of lower-limb amputees
For Level Ground walking (LG) and Stair Ascending (SA), the shaded area corresponding to S2 was smaller on the whole, and S2 could achieve smaller variance ratio values (0.006 and 0.01), as shown in Table 1, which indicated better repeatability than S1 and S3
The paper puts forward an on-board training based on robotic transtibial prosthesis and develops the real-time human locomotion mode recognition based on the trained model
Summary
Robotic prosthetics plays an important role in assisting with the daily walking of lower-limb amputees It can restore the functions of missed limb(s) and help to improve an amputee’s balance and reduce the walking metabolic by adopting different control strategies (Au et al, 2009; Shultz et al, 2016; Feng and Wang, 2017; Kim and Collins, 2017). Some studies tend to fuse different sensor signals together to recognize locomotion intents and realize control (Novak and Riener, 2015) [e.g., sEMG signals and mechanical signals (Young et al, 2014a; Joshi and Hahn, 2016), mechanical signals and capacitive signals (Zheng and Wang, 2016), etc.]
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