Proposal of a Contact Detection System using Micro-phones for a Chambara-based Augmented Sports

TL;DR

This paper introduces a contact detection system using microphones and machine learning for a chambara-based augmented sport, enabling real-time hit detection and enhancing gameplay fairness.

Contribution

It presents the Parablade Microphone Unit (PMU), a novel system combining multiple microphones and machine learning for accurate real-time hit detection in augmented sports.

Findings

Achieved 93.33% accuracy in hit event recognition

Utilized 10kHz sound data for improved detection

Demonstrated system's effectiveness in real-time gameplay

Abstract

This study presents a novel contact detection system for "Parablade," a chambara-based, sword-play augmented sport. Augmented sports combine physical activities with virtual parameters (VPs) to create a balanced and equitable gaming experience, irrespective of players' physical capabilities. The proposed Parablade Microphone Unit (PMU) employs multiple micro-phones and machine learning algorithms to detect and classify hit events through sound recogni-tion. This system aims to ensure real-time updates of VPs, thereby enhancing the gameplay expe-rience. Experimental results indicate that the PMU can accurately recognize the occurrence and location of hit events with a high accuracy rate of 93.33%, with the assistance of 10kHz additional sound generated from the sword.