人类活动预测基于由序列对序列深度神经网络预测的IMU活动信号
Ismael Espinoza Jaramillo1, Channabasava Chola1, Jin-Gyun Jeong1
1Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一个新的人类活动预测 (HAP) 系统,使用预测的惯性测量单位 (IMU) 数据. 该系统准确地预测了未来的人类活动,为加强安全和健康监测提供了潜力.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 人类活动识别 (HAR) 传统上使用过去的传感器数据.
- 预测未来的人类活动 (HAP) 的研究较少,但对于诸如落检测等应用至关重要.
- 现有的HAR系统缺乏主动干预的预测能力.
研究的目的:
- 开发和评估一个新的人类活动预测 (HAP) 系统.
- 为了利用预测的惯性测量单位 (IMU) 数据来预测未来的人类活动.
- 通过准确的活动预测,加强主动的安全和健康监测.
主要方法:
- 开发了一个基于Sequence-to-Sequence架构的深度学习预测器,具有注意力和位置编码.
- 使用预先训练的深度学习Bi-LSTM分类器,从预测的IMU数据中识别未来的活动.
- 该系统使用两个三轴IMU传感器测试了五种日常活动.
主要成果:
- 预测的IMU信号与实际测量信号的平均相关性高达91.6%.
- 在预测未来活动方面,HAP系统取得了令人印象深刻的97.96%的平均准确率.
- 深度学习模型在预测IMU信号和分类未来活动方面都表现得很好.
结论:
- 拟议的HAP系统有效地利用预测的IMU数据预测未来的人类活动.
- 这种方法在传统的HAR系统上提供了显著的进步,因为它允许主动干预.
- 高精度和相关性表明,在医疗保健和安全领域的真实应用有很大的潜力.
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