A shoe-mounted infrared sensor-based instrumentation for locomotion identification using machine learning methods. (15th January 2021)

• Record Type: Journal Article
• Authors: Tiwari, Ashutosh; Pai, Ajey; Joshi, Deepak
• Is Part Of: Measurement. Volume 168(2021)
• Publication Date: 2021-01-15
• Subjects: Terrain identification -- Foot-to-ground angle -- Support vector machine -- Convolution neural network; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.108458
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Foot-to-ground angle kinematics changes significantly on different sloped surface. Most misclassification occurs between level ground and ramp due to similar structure. Lowest classification accuracy of level ground followed by ramp (ascent and descent) IR sensor-based locomotion classification is cost effective and efficient approach. Abstract: This paper deals with the identification of terrain that is crucial to trigger the damping in semi-active lower limb prosthesis. Objective: To identify level ground and ramp terrains using foot-to-ground angle (FGA) measurement. Methods: First, Instrumented shoe for FGA measurement was developed. Next, data collection from able-bodied (n = 5) and amputee (n = 1) participants was carried out. Finally, a comparison of identification accuracy using support vector machine (SVM) and convolution neural network (CNN) algorithms was done. Results: The average classification accuracy obtained for able-bodied participants and amputee is 79.57% ± 20.32% and 73.06% ± 12.70%, respectively using SVM, whereas it is 83.45% ± 14.50% and 80% ± 12.15% respectively using CNN. Our off-line analysis shows that overall, CNN outperformed SVM with an average of 4.86% increment in classification accuracy in able-bodied participants and 9.54% in an amputee. This study introduced a simplified, low-cost method for terrain identification in the prosthetic control application.