Does Wearable Cognitive Assistance Require Edge Computing?
Authors: 
Roger Iyengar, Qifei Dong, Chanh Nguyen, Padmanabhan Pillai, 
Mahadev SatyanarayananAuthors Info & Claims
Roger Iyengar, Qifei Dong, Chanh Nguyen, Padmanabhan Pillai, Mahadev SatyanarayananAuthors Info & ClaimsPage 144
Abstract
Wearable cognitive assistance (WCA) is an important emerging class of applications that are both bandwidth intensive and latency sensitive. This well published genre of edge native applications was originally introduced in 2014 and has since been extensively studied [2, 7]. These applications provide assistance with real world tasks to users who are wearing smart glasses. Here, we focus on an important subclass of WCA applications that provide step-by-step guidance through physical assembly tasks. Applications in this subclass carry out a back and forth process with the user. First, the application gives the user an instruction for completing a step of the task. The user then completes this step, and then the application gives the user the next instruction. The camera on the glasses captures image frames of a user's progress through the task. The application processes these frames using deep neural network (DNN)-based computer vision, to determine when a task step has been completed. If the DNN misclassifies a frame, the user might not be given the next instruction at the correct time, or the user may be given an incorrect instruction.
References
[1]
Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, and Li Fei-Fei. Fine-grained car detection for visual census estimation. In AAAI Conference on Artificial Intelligence, 2017.
[2]
Kiryong Ha, Zhuo Chen, Wenlu Hu, Wolfgang Richter, Padmanabhan Pillai, and Mahadev Satyanarayanan. Towards Wearable Cognitive Assistance. In International Conference on Mobile Systems, Applications, and Services, Bretton Woods, NH, June 2014.
[3]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Computer Vision and Pattern Recognition (CVPR), 2016.
[4]
Guangli Li, Lei Liu, Xueying Wang, Xiao Dong, Peng Zhao, and Xiaobing Feng. Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge. In Int. Conference on Artiicial Neural Networks, 2018.
[5]
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems, 2015.
[6]
Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. In Computer Vision and Pattern Recognition (CVPR), June 2020.
[7]
Junjue Wang, Ziqiang Feng, Shilpa George, Roger Iyengar, Padmanabhan Pillai, and Mahadev Satyanarayanan. Towards Scalable Edge-Native Applications. In Symposium on Edge Computing (SEC), November 2019.
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- Does Wearable Cognitive Assistance Require Edge Computing?
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Published In
February 2023
306 pages
ISBN:9798400700170
DOI:10.1145/3572864
Copyright © 2023 Owner/Author.
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Published: 22 February 2023
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