Name
Title | ||
|---|---|---|
Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons |
Abstract | ||
|---|---|---|
Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., intelligent high-level controllers), we designed an environment recognition system using computer vision and deep learning. Here we first reviewed the development of the "ExoNet" database - the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labelling architecture. We then trained and tested the EfficientNetB0 convolutional neural network, which was optimized for efficiency using neural architecture search, to forward predict the walking environments. Our environment recognition system achieved similar to 73% image classification accuracy. These results provide the inaugural benchmark performance on the ExoNet database. Future research should evaluate and compare different convolutional neural networks to develop an accurate and realtime environment-adaptive locomotion mode recognition system for robotic exoskeleton control. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/EMBC46164.2021.9630064 | 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) |
DocType | Volume | ISSN |
Conference | 2021 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brokoslaw Laschowski | 1 | 3 | 0.81 |
| William McNally | 2 | 4 | 1.84 |
| Alexander Wong | 3 | 351 | 69.61 |
| John McPhee | 4 | 4 | 1.18 |