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

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Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. / Lee, H.; Park, J.W.; Helal, Sumi.

2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009. ed. / R. Fuller; X. D. Koutsoukos. Berlin : Springer, 2009. p. 148-162 (Lecture Notes in Computer Science; Vol. 5801).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Lee, H, Park, JW & Helal, S 2009, Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. in R Fuller & XD Koutsoukos (eds), 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009. Lecture Notes in Computer Science, vol. 5801, Springer, Berlin, pp. 148-162. https://doi.org/10.1007/978-3-642-04385-7_11

APA

Lee, H., Park, J. W., & Helal, S. (2009). Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. In R. Fuller, & X. D. Koutsoukos (Eds.), 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009 (pp. 148-162). (Lecture Notes in Computer Science; Vol. 5801). Springer. https://doi.org/10.1007/978-3-642-04385-7_11

Vancouver

Lee H, Park JW, Helal S. Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. In Fuller R, Koutsoukos XD, editors, 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009. Berlin: Springer. 2009. p. 148-162. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-04385-7_11

Author

Lee, H. ; Park, J.W. ; Helal, Sumi. / Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments. 2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009. editor / R. Fuller ; X. D. Koutsoukos. Berlin : Springer, 2009. pp. 148-162 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{51b52a5cacc645b2a5911dc10a0e2185,
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. {\textcopyright} 2009 Springer Berlin Heidelberg.",
keywords = "Caloric energy expenditure estimation, Indoor activity detection, Localization of footstep source, MEMS accelerometer, Personal health care, Sensor networks, Smart homes, Energy expenditure estimation, Indoor activities, Estimation, Global positioning system, Health, Health care, MEMS, Microelectromechanical devices, Signal detection, Wireless networks, Accelerometers",
author = "H. Lee and J.W. Park and Sumi Helal",
year = "2009",
doi = "10.1007/978-3-642-04385-7_11",
language = "English",
isbn = "364204378X ",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "148--162",
editor = "R. Fuller and Koutsoukos, {X. D.}",
booktitle = "2nd International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments, MELT 2009",

}

RIS

TY - GEN

T1 - Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments

AU - Lee, H.

AU - Park, J.W.

AU - Helal, Sumi

PY - 2009

Y1 - 2009

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. © 2009 Springer Berlin Heidelberg.

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. © 2009 Springer Berlin Heidelberg.

KW - Caloric energy expenditure estimation

KW - Indoor activity detection

KW - Localization of footstep source

KW - MEMS accelerometer

KW - Personal health care

KW - Sensor networks

KW - Smart homes

KW - Energy expenditure estimation

KW - Indoor activities

KW - Estimation

KW - Global positioning system

KW - Health

KW - Health care

KW - MEMS

KW - Microelectromechanical devices

KW - Signal detection

KW - Wireless networks

KW - Accelerometers

U2 - 10.1007/978-3-642-04385-7_11

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

M3 - Conference contribution/Paper

SN - 364204378X

SN - 9783642043789

T3 - Lecture Notes in Computer Science

SP - 148

EP - 162

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

A2 - Fuller, R.

A2 - Koutsoukos, X. D.

PB - Springer

CY - Berlin

ER -