Abstract
Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.
Highlights
IntroductionThe number of disabled people in China has topped 85 million in 2020 [1]
According to statistics, the number of disabled people in China has topped 85 million in 2020 [1]
To reduce the latency and energy consumption, we propose algorithms to determine whether the methods to be executed remotely or locally with deadline constraint of each task
Summary
The number of disabled people in China has topped 85 million in 2020 [1]. MEC is an efficient method to overcome the challenge by offloading some latency-sensitive tasks or computationintensive to nearby edge servers through wireless communication [10, 11]. The edge servers execute some of the received computation tasks and transmit the rest to resource-rich cloud infrastructures by low-latency connection. To take full advantage of the mobile edge computing, an effective collaboration between the intelligent prosthesis, the edge servers, and the cloud is an essential problem. By deploying the deep learningbased motion intent recognition algorithm on the intelligent prosthesis in MEC environment, we demonstrate the effectiveness of the proposed task offloading strategy in reducing the latency and energy consumption. The structure of this paper is as follows: Section 2 describes the related works on the intent recognition algorithm for intelligent prosthesis and some existing studies for applying AI technology in the MEC environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
