以人为中心的设计为基础的轻量级可穿戴IMU人类姿势估计
Lidong Wang1, Juanjuan Liu1, Jingxuan Xue1
1School of Design, Sichuan Fine Arts Institute, Chongqing, 401331, China.
Scientific reports
|March 1, 2026
概括
本研究介绍了一种高效的可穿戴人类姿势估计框架,使用知识蒸和结构重新参数化. 该模型实现了高精度与亚毫秒的延迟,理想的实时应用在低功耗设备.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 人的姿势估计对于人与计算机的互动至关重要.
- 现有的方法往往需要大量的计算资源,限制了设备上部署.
- 基于可穿戴惯性测量单元 (IMU) 的系统提供了一个保护隐私的替代方案.
研究的目的:
- 开发一个高效的人体姿势估计框架,用于在设备上部署在可穿戴设备上.
- 为了实现实时应用的高精度和低延迟之间的平衡.
- 为了利用知识蒸和结构性重新参数化来优化模型.
主要方法:
- 一个基于变压器的教师模型学习时空表示.
- 基于卷积的学生模型使用输入适应运算符.
- 结构性重新参数化使训练图崩,以获得高效的推理.
- 知识蒸将学习从老师转移到学生模型.
主要成果:
- 拟议的框架实现了接近最先进的精度 (81毫米MPJPE在DIP-IMU上,94毫米在IMUPoser上).
- 在DIP-IMU上达到0.012毫秒,在IMUPoser上达到0.011毫秒的延迟.
- 与变压器基线相比,显示了显著的加速度 (大小的1到2个数量级).
- 该模型显示了强度和跨主题的概括性.
结论:
- 开发的框架是硬件友好的,适合低功耗的可穿戴设备.
- 它使边缘设备上的高效,实时的人体姿势估计成为可能.
- 这种方法有效地将训练表现力与推断效率分开.
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