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      Optimal Placement of Accelerometers for the Detection of Everyday Activities

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          Abstract

          This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

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          Most cited references27

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          Activity Recognition from User-Annotated Acceleration Data

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            A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity.

            The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (< 3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r = 0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.
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              Activity identification using body-mounted sensors--a review of classification techniques.

              With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                July 2013
                17 July 2013
                : 13
                : 7
                : 9183-9200
                Affiliations
                [1 ] School of Computing and Mathematics, University of Ulster, Jordanstown, Co. Antrim, Northern Ireland BT37 0QB, UK; E-Mails: cd.nugent@ 123456ulster.ac.uk (C.N.); d.finlay@ 123456ulster.ac.uk (D.F.)
                [2 ] Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå 971 87, Sweden; E-Mails: basel.kikhia@ 123456ltu.se (B.K.); andrey.boytsov@ 123456ltu.se (A.B.); Josef.Hallberg@ 123456ltu.se (J.H.); Kare.Synnes@ 123456ltu.se (K.S.)
                [3 ] Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, Northern Ireland BT52 1SA, UK; E-Mail: si.mcclean@ 123456ulster.ac.uk
                Author notes
                [* ] Author to whom correspondence should be addressed; E-Mail: i.cleland@ 123456ulster.ac.uk ; Tel.: +44-289-036-8840.
                Article
                sensors-13-09183
                10.3390/s130709183
                3758644
                23867744
                b455ec07-c882-47aa-bc43-3fa3029ac6de
                © 2013 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 27 April 2013
                : 28 June 2013
                : 09 July 2013
                Categories
                Article

                Biomedical engineering
                activity recognition,accelerometery,wearable technology,classification models

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