Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments

Heyoung Lee; Jung Wook Park; Abdelsalam Helal

doi:10.1007/978-3-642-04385-7_11

Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments

Heyoung Lee
, Jung Wook Park
, Abdelsalam Helal

Dept. of Electrical and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

A smart home environment equipped with pervasive net- worked-sensors enables us to measure and analyze various vital signals related to personal health. For example, foot stepping, gait pattern, and posture can be used for assessing the level of activities and health state among the elderly and disabled people. In this paper, we sense and use footstep vibration signals measured by floor-mounted, MEMS accelerometers deployed tangent to wall sides, for estimating the level of indoor physical activity. With growing concern towards obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in human body. It also supports the localization of footstep sources, extraction of statistical parameters on daily living pattern, and identification of pathological gait pattern. Unlike other sensors such as cameras or microphones, MEMS accelerometer sensor can measure many biomedical signatures without invoking personal privacy concerns.

Original language	English
Title of host publication	Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings
Pages	148-162
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-642-04385-7_11
State	Published - 2009
Event	2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009 - Orlando, FL, United States Duration: 30 Sep 2009 → 30 Sep 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5801 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009
Country/Territory	United States
City	Orlando, FL
Period	30/09/09 → 30/09/09

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Caloric energy expenditure estimation
Indoor activity detection
Localization of footstep source
MEMS accelerometer
Personal health care
Sensor networks
Smart homes

Access to Document

10.1007/978-3-642-04385-7_11

Cite this

Lee, H., Park, J. W., & Helal, A. (2009). Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. In Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings (pp. 148-162). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5801 LNCS). https://doi.org/10.1007/978-3-642-04385-7_11

Lee, Heyoung ; Park, Jung Wook ; Helal, Abdelsalam. / Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings. 2009. pp. 148-162 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments",

abstract = "A smart home environment equipped with pervasive net- worked-sensors enables us to measure and analyze various vital signals related to personal health. For example, foot stepping, gait pattern, and posture can be used for assessing the level of activities and health state among the elderly and disabled people. In this paper, we sense and use footstep vibration signals measured by floor-mounted, MEMS accelerometers deployed tangent to wall sides, for estimating the level of indoor physical activity. With growing concern towards obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in human body. It also supports the localization of footstep sources, extraction of statistical parameters on daily living pattern, and identification of pathological gait pattern. Unlike other sensors such as cameras or microphones, MEMS accelerometer sensor can measure many biomedical signatures without invoking personal privacy concerns.",

keywords = "Caloric energy expenditure estimation, Indoor activity detection, Localization of footstep source, MEMS accelerometer, Personal health care, Sensor networks, Smart homes",

author = "Heyoung Lee and Park, \{Jung Wook\} and Abdelsalam Helal",

year = "2009",

doi = "10.1007/978-3-642-04385-7\_11",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "148--162",

booktitle = "Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings",

note = "2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009 ; Conference date: 30-09-2009 Through 30-09-2009",

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Lee, H, Park, JW & Helal, A 2009, Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. in Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5801 LNCS, pp. 148-162, 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009, Orlando, FL, United States, 30/09/09. https://doi.org/10.1007/978-3-642-04385-7_11

Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. / Lee, Heyoung; Park, Jung Wook; Helal, Abdelsalam.
Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings. 2009. p. 148-162 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5801 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Lee, Heyoung

AU - Park, Jung Wook

AU - Helal, Abdelsalam

PY - 2009

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N2 - A smart home environment equipped with pervasive net- worked-sensors enables us to measure and analyze various vital signals related to personal health. For example, foot stepping, gait pattern, and posture can be used for assessing the level of activities and health state among the elderly and disabled people. In this paper, we sense and use footstep vibration signals measured by floor-mounted, MEMS accelerometers deployed tangent to wall sides, for estimating the level of indoor physical activity. With growing concern towards obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in human body. It also supports the localization of footstep sources, extraction of statistical parameters on daily living pattern, and identification of pathological gait pattern. Unlike other sensors such as cameras or microphones, MEMS accelerometer sensor can measure many biomedical signatures without invoking personal privacy concerns.

AB - A smart home environment equipped with pervasive net- worked-sensors enables us to measure and analyze various vital signals related to personal health. For example, foot stepping, gait pattern, and posture can be used for assessing the level of activities and health state among the elderly and disabled people. In this paper, we sense and use footstep vibration signals measured by floor-mounted, MEMS accelerometers deployed tangent to wall sides, for estimating the level of indoor physical activity. With growing concern towards obesity in older adults and disabled people, this paper deals primarily with the estimation of energy expenditure in human body. It also supports the localization of footstep sources, extraction of statistical parameters on daily living pattern, and identification of pathological gait pattern. Unlike other sensors such as cameras or microphones, MEMS accelerometer sensor can measure many biomedical signatures without invoking personal privacy concerns.

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KW - Localization of footstep source

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KW - Sensor networks

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AN - SCOPUS:76749135933

SN - 364204378X

SN - 9783642043789

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings

T2 - 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009

Y2 - 30 September 2009 through 30 September 2009

ER -

Lee H, Park JW, Helal A. Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. In Mobile Entity Localization and Tracking in GPS-less Environments - Second International Workshop, MELT 2009, Proceedings. 2009. p. 148-162. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04385-7_11

Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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