Abstract
ExoNet Database: Wearable Camera Images of Human Locomotion Environments.
Highlights
Hundreds of millions of individuals worldwide have mobility impairments resulting from degenerative aging and/or neuro-musculoskeletal disorders (Grimmer et al, 2019)
Several researchers have combined mechanical sensors with surface EMG for automated locomotion mode recognition. Such neuromuscular-mechanical data fusion has improved the locomotion mode recognition accuracies and decision times compared to implementing either system individually (Huang et al, 2011; Du et al, 2012; Wang et al, 2013; Liu et al, 2016)
Drawing inspiration from ImageNet, we developed ExoNet–the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human walking environments
Summary
Hundreds of millions of individuals worldwide have mobility impairments resulting from degenerative aging and/or neuro-musculoskeletal disorders (Grimmer et al, 2019). Researchers have recently developed automated locomotion mode recognition systems using wearable sensors like inertial measurement units (IMUs) and surface electromyography (EMG) to automatically switch between different locomotion modes (Tucker et al, 2015; Young and Ferris, 2017; Laschowski and Andrysek, 2018; Krausz and Hargrove, 2019; Zhang et al, 2019a). Such neuromuscular-mechanical data fusion has improved the locomotion mode recognition accuracies and decision times compared to implementing either system individually (Huang et al, 2011; Du et al, 2012; Wang et al, 2013; Liu et al, 2016) These measurements are still patient-dependent, and surface EMG are susceptible to fatigue, changes in electrode-skin conductivity, and crosstalk from adjacent muscles (Tucker et al, 2015). Benchmark performance and analyses of the ExoNet database for human locomotion environment classification will be presented in future work
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