TY - GEN
T1 - Estimation of indoor physical activity level based on footstep vibration signal measured by mems accelerometer in smart home environments
AU - Lee, Heyoung
AU - Park, Jung Wook
AU - Helal, Abdelsalam
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.
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.
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
UR - https://www.scopus.com/pages/publications/76749135933
U2 - 10.1007/978-3-642-04385-7_11
DO - 10.1007/978-3-642-04385-7_11
M3 - Conference contribution
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)
SP - 148
EP - 162
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 -
