WiSpeed: A Statistical Electromagnetic Approach for Device-Free Indoor Speed Estimation

TL;DR

WiSpeed is a novel electromagnetic-based system that accurately estimates indoor human speed and detects falls without requiring line-of-sight or extensive calibration, working in device-free and device-based scenarios.

Contribution

WiSpeed introduces a universal, calibration-free electromagnetic approach for indoor speed estimation that works in multipath-rich environments and can also detect abnormal activities like falls.

Findings

Achieves 4.85% mean absolute percentage error in speed estimation.

Detects falls with 95% detection rate and no false alarms.

Works effectively in non-line-of-sight, multipath indoor environments.

Abstract

Due to the severe multipath effect, no satisfactory device-free methods have ever been found for indoor speed estimation problem, especially in non-line-of-sight scenarios, where the direct path between the source and observer is blocked. In this paper, we present WiSpeed, a universal low-complexity indoor speed estimation system leveraging radio signals, such as commercial WiFi, LTE, 5G, etc., which can work in both device-free and device-based situations. By exploiting the statistical theory of electromagnetic waves, we establish a link between the autocorrelation function of the physical layer channel state information and the speed of a moving object, which lays the foundation of WiSpeed. WiSpeed differs from the other schemes requiring strong line-of-sight conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, WiSpeed saves the users' efforts from large-scale training and fine-tuning of system parameters. In addition, WiSpeed could extract the stride length as well as detect abnormal activities such as falling down, a major threat to seniors that leads to a large number of fatalities every year. Extensive experiments show that WiSpeed achieves a mean absolute percentage error of 4.85% for device-free human walking speed estimation and 4.62% for device-based speed estimation, and a detection rate of 95% without false alarms for fall detection.