@inproceedings {915,
	title = {Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons},
	booktitle = {Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)},
	year = {2021},
	month = {04/2021},
	publisher = {IEEE },
	organization = {IEEE },
	abstract = {<p>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., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73\% image classification accuracy. While these preliminary results benchmark EfficientNetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.</p>
},
	keywords = {Biomechatronics, Computer Vision, Deep Learning, Exoskeletons, Rehabilitation, Robotics},
	doi = {10.1109/EMBC46164.2021.9630064},
	url = {https://ieeexplore.ieee.org/document/9630064},
	author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee}
}
@inproceedings {893,
	title = {Comparative Analysis of Environment Recognition Systems for Control of Lower-Limb Exoskeletons and Prostheses},
	booktitle = {IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)},
	year = {2020},
	month = {10/2020},
	publisher = {IEEE},
	organization = {IEEE},
	address = {New York City, NY, USA},
	abstract = {<p>Environment recognition systems can facilitate the predictive control of lower-limb exoskeletons and prostheses by recognizing the oncoming walking environment prior to physical interactions. While many environment recognition systems have been developed using different wearable technology and classification algorithms, their relative operational performances have not been evaluated. Motivated to determine the state-of-the-science and propose future directions for research innovation, we conducted an extensive comparative analysis of the wearable technology, training datasets, and classification algorithms used for vision-based environment recognition. The advantages and drawbacks of different wearable cameras and training datasets were reviewed. Environment recognition systems using pattern recognition, machine learning, and convolutional neural networks for image classification were compared. We evaluated the performances of different deep learning networks using a novel balanced metric called \&ldquo;NetScore\&rdquo;, which considers the image classification accuracy, and computational and memory storage requirements. Based on our analysis, future research in environment recognition systems for lower-limb exoskeletons and prostheses should consider developing 1) efficient deep convolutional neural networks for onboard classification, and 2) large-scale open-source datasets for training and comparing image classification algorithms from different researchers.\&nbsp;</p>
},
	keywords = {Artificial Intelligence, Biomechatronics, Computer Vision, Environment Recognition, Exoskeletons, Image Classification, Prostheses, Rehabilitation, Wearable Technology},
	doi = {10.1109/BioRob49111.2020.9224364},
	url = {https://ieeexplore.ieee.org/document/9224364},
	author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee}
}
