由5G与移动边缘计算赋能的半自主假肢控制

中文摘要 AI 辅助

配备摄像头的假手可利用计算机视觉自动规划抓握动作，从而减少认知负荷。然而，由于功耗和处理能力的限制，在可穿戴设备上运行现代视觉模型并不现实。我们推出了首个支持5G连接、基于移动边缘计算（MEC）的半自主假手原型，该原型会将RGB-D图像流式传输至边缘服务器以进行实时抓握规划。13名健全受试者在六种条件下完成了抓取-放置任务：肌电手动控制、设备端推理、有线以太网连接，以及三种5G连接配置（私有20MHz网络、私有100MHz网络和商用5G链路）连接至服务器。所有基于网络的条件表现相似，任务时间约为8.6秒（比手动控制快34%），失败率为20%-38%，整体工作负荷降低62%。设备端处理表现最差，任务时间为10.3秒，失败率达76%，原因是嵌入式推理速度较慢（每秒3帧，而网络连接下为每秒6至20帧）。私有5G的网络延迟保持在180毫秒以下，商用5G则为270毫秒以下。所有5G配置，包括带宽受限和商用可变网络，均与有线以太网性能相当，且显著优于手动控制和本地处理，证明5G边缘卸载是部署计算密集型假肢控制的可行途径。

英文摘要

Prosthetic hands equipped with cameras can use computer vision to plan grasps automatically, reducing cognitive effort. However, running modern vision models on wearable devices is impractical due to power and processing constraints. We present the first prototype of a 5G-connected mobile edge computing (MEC)-enabled semi-autonomous prosthetic hand, which streams RGB-D images to an edge server for real-time grasp planning. Thirteen able-bodied participants performed pick-and-place tasks under six conditions: manual EMG control, on-device inference, wired Ethernet connectivity, and three 5G connectivity configurations (private 20 MHz network, private 100 MHz network, and a commercial 5G link) to the server. All network-based conditions performed similarly, achieving task times around 8.6 s (34% faster than manual control), failure rates of 20-38%, and 62% lower overall workload. On-device processing performed the worst with 10.3 s task time and a 76% failure rate due to slow embedded inference (3 fps vs. 6-20 fps over the network). Network latencies remained below 180 ms for private 5G and 270 ms for commercial 5G. All 5G configurations, including bandwidth-constrained and commercially variable networks, matched wired Ethernet performance while significantly outperforming both manual control and local processing, establishing 5G edge-offloading as a practical path to deploying compute-intensive prosthesis control.

发表机构

• Aalborg University（奥尔堡大学）

机构由 AI 辅助整理，请以论文原文为准。