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However, deep learning\u2019s 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\u2010time 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\u2010based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility\u2010aware energy consumption model and latency model are proposed. By deploying the deep learning\u2010based motion intent recognition algorithm on intelligent prosthesis in a real\u2010world 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.<\/jats:p>","DOI":"10.1155\/2022\/2890473","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T20:50:06Z","timestamp":1641588606000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6049-9729","authenticated-orcid":false,"given":"Ping","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"key":"e_1_2_10_1_2","article-title":"Intent recognition of power lower-limb prosthesis based on improved convolutional neural network","author":"Ben-yue S. 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