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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

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Published on: December 11, 2015

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

Hikoto Iseda¹, Keiichi Yasumoto², Akira Uchiyama³

¹Department of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma 630-0192, Japan.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This study introduces a novel frequency-shift backscatter tag system for recognizing daily activities at home. The technology achieves high accuracy, even without direct line of sight, enhancing elderly care possibilities.

Keywords:

WiFi frequency-shift backscatter tag living activity recognition

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Area of Science:

Ubiquitous Computing
Wireless Sensor Networks
Human-Computer Interaction

Background:

Activity recognition is crucial for in-home services like elderly care.
Radio-based methods (WiFi CSI, RFID, backscatter) offer privacy and low maintenance but face environmental and proximity challenges.
Existing methods struggle with accuracy due to radio obstacles.

Purpose of the Study:

To propose and evaluate a frequency-shift backscatter tag-based method for in-home activity recognition.
To demonstrate the feasibility of this system in a realistic residential environment.
To overcome limitations of existing radio-based sensing technologies.

Main Methods:

Developed ultra-low power frequency-shift backscatter tags with antennas and switches.
Implemented a sensing system using software-defined WiFi (SD-WiFi) access points and physical switches.
Focused on detecting frequency shifts caused by tag movements for activity recognition.

Main Results:

Frequency shifts detected within 2m with 72% accuracy under line-of-sight (LoS) conditions.
Achieved 96.0% accuracy (F-score) in recognizing seven daily living activities with optimal receiver/transmitter setup.
Demonstrated successful frequency shift detection without LoS at 3-5m by increasing overlaying packets.

Conclusions:

Frequency-shift backscatter tags offer a robust and low-power solution for in-home activity recognition.
The proposed system shows significant potential for enhancing elderly care and other in-home services.
The method's accuracy and range can be improved, even in non-line-of-sight scenarios.