Activity monitoring using an intelligent mobile phone: a validation study

Yan Huang, Huiru Zheng, Chris D Nugent, Paul McCullagh, SM McDonough, Mark A Tully, Sean O Connor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

This research examines both the practicalities and feasibility of using a smart phone in the monitoring of gross daily activity, namely step counts. An Adaptive Step Detection (ASD) algorithm has been proposed and evaluated, based on where the phone is worn on the body. Experiments involved collection of data from a participant who wore two mobile phones (placed at difference positions) while walking on a treadmill at a controlled speed for periods of five minutes. A video recording and pedometer were used to independently record the number of steps in addition to a count by human observation. A step detection calibration factor was determined via a data driven approach, i.e, for each recording, a calibration factor was obtained by learning from two thirds of the acceleration data gleaned from the accelerometer within the smart phone. The remainder of the data was used to test the algorithm. The step counts from the acceleration sensor were validated by the video recordings, which were consistent with the pedometer and human observation. The results show that the step counts detected by the proposed algorithm achieved accuracy of 100% when the mobile phone was placed in the right thigh positions, and achieved above 95% accuracy when the mobile phone was placed in the right breast pocket, bag over right shoulder and right ankle.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of Publicationhttp://portal.acm.org/citation.cfm?doid=1839294.1839306
Pages1
Number of pages6
DOIs
Publication statusPublished - 23 Jun 2010
Event3rd International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2010 - Samos, Greece
Duration: 23 Jun 2010 → …

Conference

Conference3rd International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2010
Period23/06/10 → …

Fingerprint

Mobile phones
Video recording
Monitoring
Calibration
Exercise equipment
Accelerometers
Wear of materials
Sensors
Experiments

Cite this

Huang, Y., Zheng, H., Nugent, C. D., McCullagh, P., McDonough, SM., Tully, M. A., & Connor, S. O. (2010). Activity monitoring using an intelligent mobile phone: a validation study. In Unknown Host Publication (pp. 1). http://portal.acm.org/citation.cfm?doid=1839294.1839306. https://doi.org/10.1145/1839294.1839306
Huang, Yan ; Zheng, Huiru ; Nugent, Chris D ; McCullagh, Paul ; McDonough, SM ; Tully, Mark A ; Connor, Sean O. / Activity monitoring using an intelligent mobile phone: a validation study. Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839306, 2010. pp. 1
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Huang, Y, Zheng, H, Nugent, CD, McCullagh, P, McDonough, SM, Tully, MA & Connor, SO 2010, Activity monitoring using an intelligent mobile phone: a validation study. in Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839306, pp. 1, 3rd International Conference on Pervasive Technologies Related to Assistive Environments, PETRA 2010, 23/06/10. https://doi.org/10.1145/1839294.1839306

Activity monitoring using an intelligent mobile phone: a validation study. / Huang, Yan; Zheng, Huiru; Nugent, Chris D; McCullagh, Paul; McDonough, SM; Tully, Mark A; Connor, Sean O.

Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839306, 2010. p. 1.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - This research examines both the practicalities and feasibility of using a smart phone in the monitoring of gross daily activity, namely step counts. An Adaptive Step Detection (ASD) algorithm has been proposed and evaluated, based on where the phone is worn on the body. Experiments involved collection of data from a participant who wore two mobile phones (placed at difference positions) while walking on a treadmill at a controlled speed for periods of five minutes. A video recording and pedometer were used to independently record the number of steps in addition to a count by human observation. A step detection calibration factor was determined via a data driven approach, i.e, for each recording, a calibration factor was obtained by learning from two thirds of the acceleration data gleaned from the accelerometer within the smart phone. The remainder of the data was used to test the algorithm. The step counts from the acceleration sensor were validated by the video recordings, which were consistent with the pedometer and human observation. The results show that the step counts detected by the proposed algorithm achieved accuracy of 100% when the mobile phone was placed in the right thigh positions, and achieved above 95% accuracy when the mobile phone was placed in the right breast pocket, bag over right shoulder and right ankle.

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Huang Y, Zheng H, Nugent CD, McCullagh P, McDonough SM, Tully MA et al. Activity monitoring using an intelligent mobile phone: a validation study. In Unknown Host Publication. http://portal.acm.org/citation.cfm?doid=1839294.1839306. 2010. p. 1 https://doi.org/10.1145/1839294.1839306