Floor vibration-based methods to track human activity are becoming popular for applications in healthcare monitoring, security, and occupant detection. Popular techniques such as time of arrival (TOA) methods face wave dispersion and multiple-path fading challenges for localization. Data-driven methodologies such as the FEEL Algorithm rely exclusively on the system dynamic properties, an advantage over other methods. However, FEEL’s calibration process requires recording force input to the structure, which can become labor-intensive and time-consuming for applications that require a high localization accuracy and does not require force estimates. An alternative approach is proposed to use the system’s acceleration response exclusively, creating an output-to-output transfer function. This modification was tested against the 3575 impact Human-Induced Vibration Benchmark dataset containing seven impact types across five locations, the same dataset FEEL was originally developed with. The results demonstrated the acceleration-calibrated FEEL effectiveness with 99.9% localization accuracy compared to force-calibrated FEEL’s accuracy of 96.4%.

基于地板振动的人类活动跟踪方法在医疗保健监控、安全和人员检测等应用中越来越受欢迎。到达时间 (TOA) 方法等流行技术面临着波色散和多径衰落的定位挑战。FEEL 算法等数据驱动方法完全依赖于系统动态属性，这是优于其他方法的优势。然而，FEEL 的校准过程需要记录输入到结构中的力，这对于需要高定位精度且不需要力估计的应用来说可能会变得劳动密集型且耗时。提出了一种替代方法，专门使用系统的加速度响应，创建输出到输出的传递函数。此修改针对 3575 个影响人为振动基准数据集进行了测试，该数据集包含五个位置的七种影响类型，与最初开发 FEEL 的数据集相同。结果表明，加速度校准 FEEL 的有效性为 99.9%，而力校准 FEEL 的定位精度为 96.4%。