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PhysioKit:一个开源的,低成本的生理计算工具包,用于单个和多用户研究
Jitesh Joshi1, Katherine Wang1, Youngjun Cho1
1Department of Computer Science, University College London, London NW1 2AE, UK.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
PhysioKit是一个开源的,低成本的工具包用于生理计算. 它提供了一个模块化的传感层和用于数据采集,可视化和基于机器学习的质量评估的软件,与研究级传感器进行验证.
科学领域:
- 物理计算的物理计算.
- 人与计算机的互动.
- 生物医学工程 生物医学工程
背景情况:
- 生理学传感器提供了新的研究途径,但商业选择是昂贵的,低成本的传感器需要大量的设置.
- 获取原始生理波形数据对于深入分析和用户体验评估至关重要.
- 现有的解决方案往往在成本,数据可访问性和易于实施之间进行权衡.
研究的目的:
- 介绍 PhysioKit,一个开源的,低成本的生理计算工具包.
- 提供一个全面的管道来获取,处理和分析生理数据.
- 在研究环境中验证PhysioKit的性能和可用性.
主要方法:
- 开发了一个模块化的传感和数据采集层,以实现灵活的配置.
- 创建了一个软件应用层,支持实时可视化和机器学习 (ML) 支持的信号质量评估.
- 集成的基本视觉生物反和同步的多用户获取能力.
- 进行了一项验证研究,将PhysioKit与研究级心率和心率变化传感器进行比较.
- 在10个项目团队 (44名成员) 中使用PhysioKit进行了4-6周的可用性调查.
主要成果:
- 在测量心率和心率变化方面,PhysioKit与研究级传感器有很强的一致性.
- 16名参与者的验证研究证实了工具包的准确性.
- 可用性调查为PhysioKit的用例和研究效益提供了积极的见解.
- 该工具包支持同步获取共定位和远程多用户设置.
结论:
- PhysioKit为生理计算研究提供了一个可行的,低成本和可访问的解决方案.
- 该工具包的模块化设计和支持机器学习的功能增强了数据采集和分析.
- PhysioKit有可能对生理计算研究产生重大影响并扩大参与范围.
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