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1. JP2019030520 - MYOELECTRICITY MEASUREMENT APPARATUS, METHOD, AND PROGRAM

Office Japan
Application Number 2017153444
Application Date 08.08.2017
Publication Number 2019030520
Publication Date 28.02.2019
Grant Number 6857573
Grant Date 24.03.2021
Publication Kind B1
IPC
A61B 5/389
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
316Modalities, i.e. specific diagnostic methods
389Electromyography
A61B 5/24
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes; Identification of persons
24Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
Applicants NIPPON TELEGR & TELEPH CORP
日本電信電話株式会社
Inventors NIIJIMA ARINOBU
新島 有信
ISEZAKI TAKASHI
伊勢崎 隆司
AOKI RYOSUKE
青木 良輔
WATABE TOMOKI
渡部 智樹
YAMADA TOMOHIRO
山田 智広
Agents 蔵田 昌俊
野河 信久
峰 隆司
井上 正
Title
(EN) MYOELECTRICITY MEASUREMENT APPARATUS, METHOD, AND PROGRAM
(JA) 筋電計測装置、方法及びプログラム
Abstract
(EN)

PROBLEM TO BE SOLVED: To further improve analysis accuracy by enabling removal of a noise component incapable of being removed by a filter processing technology.

SOLUTION: A myoelectricity measurement apparatus acquires measurement data on surface Electromyography (sEMG) of each of a left and right temporal muscles; after performing on the measurement data band-pass filter processing of making only frequency components of 20 to 450 Hz pass and removing the other frequency components, calculates a RMS indicating a feature amount; calculates a correlation coefficient (R) between RMSs corresponding to the left and right temporal muscles; and, when the correlation coefficient (R) is equal to or smaller than a threshold, assumes that the RMSs include many residual noise components independent of the temporal muscles' motion before replacing values of the RMSs with a previously set base line value.

SELECTED DRAWING: Figure 2

COPYRIGHT: (C)2019,JPO&INPIT

(JA)

【課題】フィルタ処理技術では除去できないノイズ成分を除去できるようにし、これにより解析精度のさらなる向上を図る。
【解決手段】左右の各側頭筋の表面筋電図(sEMG)の計測データを取得し、これらの計測データに対し20−450Hzのみを通過させ他の周波数成分を除去するバンドパスフィルタ処理を行った後、特徴量を示すRMSを算出する。そして、上記左右の側頭筋に対応するRMS間の相関係数(R)を算出し、この相関係数(R)がしきい値以下の場合に、上記RMSには側頭筋の運動に寄らないノイズ成分が多く残留していると見做して、上記RMSの値を事前に設定しておいたベースライン値に置換する。
【選択図】図2