使用可穿戴传感器识别家庭和社区的移动使用连续和直线步行时间
Lauren Gracey-McMinn1, David Loudon2, Alix Chadwell3
1Centre for Human Movement and Rehabilitation, School of Health and Society, University of Salford, Salford M6 6PU, UK.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究开发了一种使用可穿戴传感器的新模型, 以客观地测量社区参与, 高度准确地区分家庭和社区活动. 通过提供比主观报告更准确,更真实的数据,从而推进功能恢复评估.
科学领域:
- 生物医学工程
- 康复科学
- 可穿戴技术
背景情况:
- 对社区参与的客观测量对于评估临床环境中的功能恢复和干预效果至关重要.
- 目前依赖主观自我报告措施限制了评估现实活动的准确性和可靠性.
- 需要验证的方法来客观地量化身体行为和社区参与.
研究的目的:
- 开发和验证一个分类模型,以区分家庭和社区活动.
- 利用activPAL设备的步行和躺着数据进行客观的身体行为监测.
- 通过准确的姿势数据分析,加强功能移动性和社区参与的评估.
主要方法:
- 使用activPAL设备的步行和躺卧数据开发了一个分类模型.
- 使用网格搜索优化方法测试直线步进时间 (SLS) 和连续步进时间 (CSD) 的值组合.
- 在24名健康参与者身上验证了该模型,他们戴着activPAL 4+监测器,并在7天内完成活动日记.
主要成果:
- 在24小时的时间内,最佳模型在家庭与社区活动的分类中达到93.7%的准确性.
- 一个26秒的SLS值被确定为分类模型的最佳值.
- 该模型的准确性很高,预测和报告的社区参与时间之间的差异平均只有7分钟.
结论:
- 开发的模型提供了客观的物理行为监测的方法进步.
- 通过姿势数据准确地对家庭和社区活动进行分类,可以提高功能移动性和社区参与度的评估.
- 这种方法可以改善临床决策,康复规划和干预评估,解决衡量现实世界康复的关键差距.
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