Angle of Arrival Estimation for Gesture Recognition from reflective body-worn tags

TL;DR

This paper enhances gesture recognition accuracy by estimating and tracking the Angle of Arrival (AoA) from reflective body-worn tags, demonstrating significant performance improvements over traditional RSS and phase features.

Contribution

It introduces an AoA tracking method using Kalman smoothing and validates its effectiveness for gesture recognition with reflective body-worn tags.

Findings

AoA tracking improves gesture recognition accuracy by up to 15%.

MUSIC algorithm effectively estimates AoA with fixed tags.

AoA features distinguish gestures better than RSS and phase alone.

Abstract

We investigate hand gesture recognition by leveraging passive reflective tags worn on the body. Considering a large set of gestures, distinct patterns are difficult to be captured by learning algorithms using backscattered received signal strength (RSS) and phase signals. This is because these features often exhibit similarities across signals from different gestures. To address this limitation, we explore the estimation of Angle of Arrival (AoA) as a distinguishing feature, since AoA characteristically varies during body motion. To ensure reliable estimation in our system, which employs Smart Antenna Switching (SAS), we first validate AoA estimation using the Multiple SIgnal Classification (MUSIC) algorithm while the tags are fixed at specific angles. Building on this, we propose an AoA tracking method based on Kalman smoothing. Our analysis demonstrates that, while RSS and phase alone are insufficient for distinguishing certain gesture data, AoA tracking can effectively differentiate them. To evaluate the effectiveness of AoA tracking, we implement gesture recognition system benchmarks and show that incorporating AoA features significantly boosts their performance. Improvements of up to 15% confirm the value of AoA-based enhancement.