title: Terrain and Direction Classification of Locomotion Transitions Using Neuromuscular and Mechanical Input.
abstract: To perform seamless transitions in powered lower limb prostheses, accurate classification of transition type is required a priori. We propose a structure to detect direction (ascent or descent) and terrain (ramp or stairs) patterns when a person transitions from over ground to stairs or ramp locomotion. We compared electromyography (EMG) and accelerometry performance with an emphasis on sensor fusion for improving classification. Seven healthy subjects were recruited for this initial study. Data were collected with accelerometers and EMG electrodes on the dominant leg, while subjects transitioned from over ground to ramp (ascent and descent) and stair (ascent and descent) locomotion. Linear discriminant analysis and support vector machine approaches were used as classifiers using feature spaces of both sensor types. The results indicate that transitions are better classified as terrain type than direction type (p < 0.001), suggesting a terrain focused approach for an efficient structure. We also show that EMG and accelerometry data sources are complementary across the transitional gait cycle, suggesting sensor fusion for robust classification. These findings suggest that a terrain and direction focused classification approach will be useful for inclusion in classification approaches utilized in lower limb amputee samples.
authors.affiliation: Department of Electrical and Electronics Engineering, Graphic Era University, Dehradun, India.|Department of Human Physiology, University of Oregon, 122 Esslinger Hall, 1240 University of Oregon, Eugene, OR, 97401, USA.
authors.forename: Deepak
authors.initials: D
authors.lastname: Joshi
authors.affiliation: Department of Human Physiology, University of Oregon, 122 Esslinger Hall, 1240 University of Oregon, Eugene, OR, 97401, USA. mhahn@uoregon.edu.
authors.forename: Michael E
authors.identifier: http://orcid.org/0000-0001-5024-700X
authors.initials: ME
authors.lastname: Hahn
mesh_terms: D061725:Accelerometry; D000328:Adult; D016002:Discriminant Analysis; D004576:Electromyography; D005260:Female; D006801:Humans; D007866:Leg / Q000502:physiology*; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D016138:Walking / Q000502:physiology*; D055815:Young Adult
keywords: Accelerometry; Detection; Electromyography; Gait; Linear discriminant analysis; Ramp; Stair; Support vector machine