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步骤长度是比步行速度更可靠的测量步行者死亡计算
Fatemeh Elyasi1, Roberto Manduchi1
1Department of Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, USA.
概括
使用智能手机惯性数据预测步骤长度比使用智能手机惯性数据预测行走速度更可靠. 这一发现改善了PDR准确性和导航系统.
科学领域:
- 计算机科学 计算机科学
- 机器人技术 机器人技术 机器人技术
- 信号处理 信号处理
背景情况:
- 对于室内和室外航行来说,行人死亡计算 (PDR) 是非常重要的.
- PDR算法通过智能手机等设备的惯性传感器数据来估计用户路径.
- 目前的方法经常估计步骤长度或步行速度,准确度各不相同.
研究的目的:
- 为了比较预测步长与步行速度的可靠性,用于PDR.
- 分析特定神经网络架构 (LSTM + 完全连接层) 对这些预测任务的性能.
主要方法:
- 采用了循环神经网络架构:长短期内存 (LSTM),其次是四个完全连接的层.
- 从十二名参与者收集的惯性数据数据集上训练和评估模型.
- 评估了步骤长度和步行速度预测的重建质量.
主要成果:
- 研究发现,与预测每个步骤的平均步行速度相比,预测步骤长度产生了更可靠的结果.
- 选择的基于LSTM的架构在重建步骤长度方面表现出更高的准确性.
结论:
- 直接预测步骤长度是PDR比预测步行速度更强大的方法.
- 这种提高了步骤长度估计的可靠性,可以提高基于PDR的导航系统的准确性.
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