探索NULL类对野生人类活动识别的影响
Josh Cherian1, Samantha Ray1, Paul Taele1
1Department of Computer Science & Engineering, Texas A&M University, College Station, TX 77843, USA.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究改进了机器学习,用于使用不平衡的现实数据监控日常生活活动 (ADLs). 增强回忆和精度的技术可以可靠地识别罕见的,自然的日常活动.
科学领域:
- 机器学习 机器学习
- 人与计算机的交互
- 老年学是一门学科.
背景情况:
- 监测日常生活活动 (ADLs) 对于评估和响应个体的基本身体需求至关重要.
- 现有的ADL识别系统与自然主义,不经常发生的活动和现实环境中常见的不平衡数据集作斗争.
- 当前的方法往往侧重于受控活动或平衡的数据集,限制其适用于真实设置.
研究的目的:
- 研究将机器学习应用于ADL监控的不平衡,野生数据集的挑战.
- 在现实的场景中开发和评估技术,以提高ADL识别系统的性能.
- 提高用于ADL监控的机器学习模型的可靠性和部署准备.
主要方法:
- 利用一个完全在野外的数据集,其中包含自然主义活动与罕见的事件.
- 应用了预处理技术的组合来增强回忆和后处理技术来提高精度.
- 进行了独立于用户的评估,以评估模型在各种现实数据上的性能.
主要成果:
- 开发的方法显著提高了对不平衡的野生数据的ADL识别精度.
- 在刷牙,理发,散步和洗手等活动中获得了超过0.9的基于事件的F1分数.
- 证明了结合前处理和后处理的有效性,以进行强大的ADL监测.
结论:
- 预处理和后处理技术对于在现实环境中创建有效的机器学习模型来监控ADL至关重要.
- 解决数据不平衡和活动不频率是部署可靠的ADL识别系统的关键.
- 这项研究解决了在医疗保健和辅助生活中实际应用的基本机器学习挑战.
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