A Subvision System for Enhancing the Environmental Adaptability of the Powered Transfemoral Prosthesis

Zhang, Kuangen; Luo, Jianwen; Xiao, Wentao; Wen, Zhang; Liu, Haiyuan; Zhu, Jiale; Lu, Zeyu; Ye, Rong; Silva, Clarence W. de; Fu, Chenglong

doi:10.1109/tcyb.2020.2978216

Cited by 62 publications

(30 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison, the previous largest dataset contained ∼402,000 images (Massalin et al, 2018). While most environment recognition systems have included fewer than six classes Varol and Massalin, 2016;Massalin et al, 2018;Khademi and Simon, 2019;Laschowski et al, 2019b;Novo-Torres et al, 2019;Zhang et al, 2019b,c,d;Zhang et al, 2020), the ExoNet database features a 12-class hierarchical labeling architecture. These differences have real-world implications given that learning-based algorithms like convolutional neural networks require significant and diverse training images (LeCun et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Similar to the human visual system, environment sensing would precede modulation of the patient's muscle activations and/or walking biomechanics, therein enabling more accurate and real-time locomotion mode transitions. Environment sensing could also be used to adapt low-level reference trajectories (e.g., changing toe clearance corresponding to an obstacle height) (Zhang et al, 2020) and optimal path planning (e.g., identifying opportunities for energy regeneration) (Laschowski et al, 2019a(Laschowski et al, , 2020a. Preliminary research has shown that supplementing an automated locomotion mode recognition system with environment information can improve the classification accuracies and decision times compared to excluding terrain information (Huang et al, 2011;Wang et al, 2013;Liu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

et al. 2020

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

et al. 2020

View full text Add to dashboard Cite

“…In comparison, the previous largest dataset contained approximately 402,000 images (Massalin et al, 2018). While most environment recognition systems have included fewer than 6 classes (Khademi and Simon, 2019; Krausz and Hargrove, 2015; Krausz et al, 2015; 2019; Laschowski et al, 2019b; Massalin et al, 2018; Novo-Torres et al, 2019; Varol and Massalin, 2016; Zhang et al, 2019b; 2019c; 2019d; 2020), the ExoNet database features a 12-class hierarchical labelling architecture. These differences have practical implications given that learning-based algorithms like deep convolutional neural networks require significant and diverse training images (LeCun et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Analogous to the human visual system, environment sensing would precede modulation of the patient’s muscle activations and/or walking biomechanics, therein enabling more accurate and real-time locomotion mode transitions. Environment sensing can also be used to adapt low-level reference trajectories (e.g., changing toe clearance corresponding to an obstacle height) (Zhang et al, 2020) and optimal path planning (e.g., identifying opportunities for energy regeneration) (Laschowski et al, 2019a; 2020a). Preliminary research has shown that supplementing a locomotion mode recognition system with environment information can improve the classification accuracies and decision times compared to excluding terrain information (Huang et al, 2011; Liu et al, 2016; Wang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Laschowski

McNally

Wong

et al. 2020

Preprint

View full text Add to dashboard Cite

Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.

show abstract

“…A UAV that is used in our laboratory is shown in Figure 2. A similar UAV is used in our collaborative project in environmental monitoring and field mapping [5] . Due to their capabilities of autonomous navigation in extensive and traditionally unreachable regions and advanced sensing (such as optical and thermal imaging and laser scanning), UAVs are replacing costly, hazardous, and manual ways of inspection and surveillance in various applications.…”

Section: The State-of-the-artmentioning

confidence: 99%

Intelligent robotics - misconceptions, current trends, and opportunities

Silva¹

2021

Self Cite

View full text Add to dashboard Cite

The concepts of "Robots" have been of interest to humans from historical times, initially with the desire to create "artificial slaves". Since the technology was not developing to keep up with the "dreams", initially, Robotics was primarily of entertainment value, relegated to plays, movies, stories, etc. The practical applications started in the late 1950s and the 1960s with the development of programmable devices for factories and assembly lines as flexible automation. However, since the expectations were not adequately realized, the general enthusiasm and funding for Robotics subsided to some extent. With subsequent research, developments, and curricular enhancement in Engineering and Computer Science and the resurgence of Artificial Intelligence, particularly machine learning, Robotics has found numerous practical applications today, in industry, medicine, household, the service sector, and the general society. Important developments and practical strides are being made, particularly in Soft Robotics, Mobile Robotics (Aerial -drones, Underwater, Ground-based -autonomous vehicles in particular), Swarm Robotics, Homecare, Surgery, Assistive Devices, and Active Prosthesis. This perspective paper starts with a brief history of Robotics while indicating some associated myths and unfair expectations. Next, it will outline key developments in the area. In particular, some important practical applications of Intelligent Robotics, as developed by groups worldwide, including the Industrial Automation Laboratory at the University of British Columbia, headed by the author, are indicated. Finally, some misconceptions and shortcomings concerning Intelligent Robotics are pointed out. The main shortcomings concern the mechanical capabilities and the nature of intelligence. The paper concludes by mentioning future trends and key opportunities available in Intelligent Robotics for both developed and developing counties.

show abstract

A Subvision System for Enhancing the Environmental Adaptability of the Powered Transfemoral Prosthesis

Cited by 62 publications

References 34 publications

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Intelligent robotics - misconceptions, current trends, and opportunities

Contact Info

Product

Resources

About