# Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags

## TL;DR

This paper introduces a WiFi-based system using frequency-shift backscatter tags to recognize daily activities in homes, offering privacy and low power consumption.

## Contribution

A novel frequency-shift backscatter tag-based method for in-home activity recognition with demonstrated robustness and accuracy.

## Key findings

- Frequency shifts can be detected within a 2 m range with 72% accuracy under line-of-sight conditions.
- The system achieves 96.0% accuracy (F-score) in recognizing seven daily living activities.
- Frequency shift detection is possible without line-of-sight at 3–5 m with increased packet overlaying.

## Abstract

To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3–5 m.