Terrain and Direction Classification of Locomotion Transitions Using Neuromuscular and Mechanical Input

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• 作者：Deepak Joshi ; Michael E. Hahn
• 关键词：Stair ; Ramp ; Gait ; Detection ; Electromyography ; Accelerometry ; Linear discriminant analysis ; Support vector machine
• 刊名：Annals of Biomedical Engineering
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：44
• 期：4
• 页码：1275-1284
• 全文大小：1,795 KB
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• 作者单位：Deepak Joshi (1) (2)
  Michael E. Hahn (2)
  
  1. Department of Electrical and Electronics Engineering, Graphic Era University, Dehradun, India
  2. Department of Human Physiology, University of Oregon, 122 Esslinger Hall, 1240 University of Oregon, Eugene, OR, 97401, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Biomedicine
  Biomedical Engineering
  Biophysics and Biomedical Physics
  Mechanics
  Biochemistry
  
• 出版者：Springer Netherlands
• ISSN：1573-9686

文摘

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.