Human action recognition using wireless wearable in-ear microphone

Jun Nishimura, Tadahiro Kuroda

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: "chew-like" signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.

Original languageEnglish
Pages (from-to)1570-1576
Number of pages7
JournalIEEJ Transactions on Electronics, Information and Systems
Volume131
Issue number9
DOIs
Publication statusPublished - 2011

Fingerprint

Mastication
Microphones
Acoustic waves
Bluetooth
Signal detection
Feedback
Monitoring

Keywords

  • Chewing action recognition
  • Eating habits monitoring
  • In-ear microphone
  • In-ear sound recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Human action recognition using wireless wearable in-ear microphone. / Nishimura, Jun; Kuroda, Tadahiro.

In: IEEJ Transactions on Electronics, Information and Systems, Vol. 131, No. 9, 2011, p. 1570-1576.

Research output: Contribution to journalArticle

@article{a09c0ee085594186b6d2a0986772914c,
title = "Human action recognition using wireless wearable in-ear microphone",
abstract = "To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: {"}chew-like{"} signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93{\%} is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.",
keywords = "Chewing action recognition, Eating habits monitoring, In-ear microphone, In-ear sound recognition",
author = "Jun Nishimura and Tadahiro Kuroda",
year = "2011",
doi = "10.1541/ieejeiss.131.1570",
language = "English",
volume = "131",
pages = "1570--1576",
journal = "IEEJ Transactions on Electronics, Information and Systems",
issn = "0385-4221",
publisher = "The Institute of Electrical Engineers of Japan",
number = "9",

}

TY - JOUR

T1 - Human action recognition using wireless wearable in-ear microphone

AU - Nishimura, Jun

AU - Kuroda, Tadahiro

PY - 2011

Y1 - 2011

N2 - To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: "chew-like" signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.

AB - To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: "chew-like" signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.

KW - Chewing action recognition

KW - Eating habits monitoring

KW - In-ear microphone

KW - In-ear sound recognition

UR - http://www.scopus.com/inward/record.url?scp=82255166923&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=82255166923&partnerID=8YFLogxK

U2 - 10.1541/ieejeiss.131.1570

DO - 10.1541/ieejeiss.131.1570

M3 - Article

VL - 131

SP - 1570

EP - 1576

JO - IEEJ Transactions on Electronics, Information and Systems

JF - IEEJ Transactions on Electronics, Information and Systems

SN - 0385-4221

IS - 9

ER -