目的:本研究旨在从地板振动中提取人体步态参数。拟议的方法提供了一种关于乘员活动的创新方法,有助于更广泛地理解人类运动如何在其建筑环境中相互作用。
方法:开发了一种多级概率模型,通过分析步行引起的地板振动来估计步频和步行速度。该模型解决了与地板加速度信号中的缺失或不完整信息相关的挑战。遵循贝叶斯分析报告指南(BARG)的可重复性,通过27次步行实验对模型进行了评估,从动态帕金森病监测(APDM)可穿戴传感器捕获地板振动和数据。该模型在实时实现中进行了测试,其中记录了10个人以自己选择的速度行走。
结果:使用严格的组合决策标准,即95%的高后验密度(HPD)和BARG之后的实际等效范围(ROPE),结果表明,对于实际目的,估计值和目标值之间的一致性令人满意。值得注意的是,95%的HPD中有90%以上属于实际等效范围,有一个坚实的基础,接受估计与使用APDM传感器和视频记录的估计概率一致。
结论:这项研究通过分析地板振动来验证概率多水平模型在估计节奏和步行速度方面的有效性。证明了其与APDM传感器和视频记录等既定技术的令人满意的可比性。估计值和目标值之间的紧密一致强调了方法的有效性。所提出的模型有效地解决了与现实场景中数据缺失或不完整相关的普遍挑战,提高了从地板振动中得出的步态参数估计的准确性。
结论:从地板振动中提取步态参数可以提供一种非侵入性和连续的监测个体步态的方法,提供有价值的见解,流动性和神经系统疾病的潜在指标。这项研究的意义延伸到先进的步态分析工具的发展,提供评估和理解步行模式的新观点,以改善诊断和个性化医疗保健。临床和转化影响声明:本手稿介绍了一种用于无人值守步态评估的创新方法,对临床决策具有潜在的重要意义。通过利用地板振动来估计节奏和步行速度,该技术可以为临床医生提供对患者在现实生活中的移动性和功能能力的宝贵见解。在房屋或护理设施的地板下战略安装加速度计,允许在这些评估期间不间断的日常活动,减少对专业临床环境的依赖。这项技术可以随着时间的推移持续监测步态模式,并有可能集成到医疗保健平台中。这种集成可以增强远程监控,导致及时的干预和个性化的护理计划,最终改善临床结果。我们模型的概率性质使估计参数的不确定性量化,为临床医生提供对数据可靠性的细致理解。
OBJECTIVE: This research aims to extract human gait parameters from floor vibrations. The proposed approach provides an innovative methodology on occupant activity, contributing to a broader understanding of how human movements interact within their built environment.
METHODS: A multilevel probabilistic model was developed to estimate cadence and walking speed through the analysis of floor vibrations induced by walking. The model addresses challenges related to missing or incomplete information in the floor acceleration signals. Following the Bayesian Analysis Reporting Guidelines (BARG) for reproducibility, the model was evaluated through twenty-seven walking experiments, capturing floor vibration and data from Ambulatory Parkinson\'s Disease Monitoring (APDM) wearable sensors. The model was tested in a real-time implementation where ten individuals were recorded walking at their own selected pace.
RESULTS: Using a rigorous combined decision criteria of 95% high posterior density (HPD) and the Range of Practical Equivalence (ROPE) following BARG, the results demonstrate satisfactory alignment between estimations and target values for practical purposes. Notably, with over 90% of the 95% HPD falling within the region of practical equivalence, there is a solid basis for accepting the estimations as probabilistically aligned with the estimations using the APDM sensors and video recordings.
CONCLUSIONS: This research validates the probabilistic multilevel model in estimating cadence and walking speed by analyzing floor vibrations, demonstrating its satisfactory comparability with established technologies such as APDM sensors and video recordings. The close alignment between the estimations and target values emphasizes the approach\'s efficacy. The proposed model effectively tackles prevalent challenges associated with missing or incomplete data in real-world scenarios, enhancing the accuracy of gait parameter estimations derived from floor vibrations.
CONCLUSIONS: Extracting gait parameters from floor vibrations could provide a non-intrusive and continuous means of monitoring an individual\'s gait, offering valuable insights into mobility and potential indicators of neurological conditions. The implications of this research extend to the development of advanced gait analysis tools, offering new perspectives on assessing and understanding walking patterns for improved diagnostics and personalized healthcare.Clinical and Translational Impact Statement: This manuscript introduces an innovative approach for unattended gait assessments with potentially significant implications for clinical decision-making. By utilizing floor vibrations to estimate cadence and walking speed, the technology can provide clinicians with valuable insights into their patients\' mobility and functional abilities in real-life settings. The strategic installation of accelerometers beneath the flooring of homes or care facilities allows for uninterrupted daily activities during these assessments, reducing the reliance on specialized clinical environments. This technology enables continuous monitoring of gait patterns over time and has the potential for integration into healthcare platforms. Such integration can enhance remote monitoring, leading to timely interventions and personalized care plans, ultimately improving clinical outcomes. The probabilistic nature of our model enables uncertainty quantification in the estimated parameters, providing clinicians with a nuanced understanding of data reliability.