SKS变压器:用于基于惯性传感器的活动识别的多尺度和方向感知注意力
Chengwei Feng1, Boris Bačić1,2,3,4, Weihua Li1,2
1School of Engineering, Computer and Mathematical Sciences (ECMS), Auckland University of Technology, Auckland, New Zealand.
Frontiers in sports and active living
|February 9, 2026
概括
一个新的可扩展的人类活动识别 (HAR) 系统使用SKS-变压器模型和可穿戴的惯性测量单元 (IMU) 准确识别活动和高尔夫球摆动错误.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 人类活动识别 (HAR) 对医疗保健和体育分析等应用至关重要.
- 可扩展和准确的HAR系统,在各种场景中概括,仍然是一个挑战.
研究的目的:
- 提出一个可扩展的HAR系统,将一个新的SKS-变压器模型与一个定制可穿戴的惯性测量单元 (IMU) 集成在一起.
- 为了提高HAR的准确性和可扩展性,用于各种活动和细粒度错误检测.
主要方法:
- 开发了一个定制的可穿戴IMU,配备ESP8266和JY61传感器,用于无线移动数据采集.
- 提出了采用选择性内核网络和挤压增强轴向注意力的SKS-变压器模型,用于多尺度的时间和方向依赖性捕获.
- 实现了一个数据预处理管道 (消除噪音,细分,规范化) 和一个可学习的门机制,以适应动作模式的融合.
主要成果:
- 在公共数据集 (UCI-HAR,PAMAP2) 上,SKS-Transformer 模型实现了最先进的性能,超过了其他 11 种模型.
- 在收集的HAR数据上达到高达98.10%的准确性,在高尔夫球摆错误检测中达到100%的准确性.
- 废弃性研究验证了每个SKS-变压器组件的贡献.
结论:
- 拟议的SKS-Transformer模型显著提高了HAR系统的性能和可扩展性.
- 该系统在识别日常活动和检测特定错误 (如高尔夫球摆动故障) 方面表现出高度准确性.
- 未来的工作包括将该系统应用于智能医疗保健,体育监测和可穿戴计算,并公开提供源代码.
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