盲人步行者的步骤长度估计
Fatemeh Elyasi1, Roberto Manduchi1
1University of California, Santa Cruz, USA.
Computers helping people with special needs : ... International Conference, ICCHP ... : proceedings. International Conference on Computers Helping People with Special Needs
|August 6, 2024
概括
在盲人行人中估计步骤长度的机器学习模型需要重新训练. 在有视力的步行者身上训练的模型表现不佳;用盲人步行者的数据进行再训练显著提高了准确性,以获得更好的移动独立性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人与计算机的交互
- 机器学习 机器学习
背景情况:
- 智能手机惯性数据辅助行人死亡计数 (PDR) 对于盲人行人移动.
- 准确的步骤长度估计对于PDR定位至关重要.
- 以前的步数长度预测模型仅在有视力的个体上进行训练.
研究的目的:
- 评估现有的步骤长度估计模型在盲人行人上的性能.
- 开发和验证一个经过重新训练的模型,以改善盲人阶段长度预测.
主要方法:
- 从使用智能手机的盲人行人那里收集惯性数据.
- 训练并测试机器学习模型用于步骤长度估计.
- 使用可见者与盲人步行者的数据来比较模型性能.
主要成果:
- 在有视力步行者身上训练的步骤长度估计模型在应用到盲人步行者身上时显示出较差的准确性.
- 用盲人步行者的数据重新训练模型显著提高了预测准确性.
- 该研究强调了步态差异影响PDR性能.
结论:
- 现有的步骤长度估计模型需要适应盲人行人.
- 用特定用户群数据重新训练机器学习模型对于准确的PDR至关重要.
- 提高PDR准确度可以提高盲人的移动独立性.
相关概念视频
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