Literature DB >> 19162860

Frequency domain approach for activity classification using accelerometer.

Wan-Young Chung1, Amit Purwar, Annapurna Sharma.   

Abstract

Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.

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Year:  2008        PMID: 19162860     DOI: 10.1109/IEMBS.2008.4649357

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  3 in total

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Journal:  Sports Med       Date:  2014-05       Impact factor: 11.136

2.  Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches.

Authors:  Kishan Bakrania; Thomas Yates; Alex V Rowlands; Dale W Esliger; Sarah Bunnewell; James Sanders; Melanie Davies; Kamlesh Khunti; Charlotte L Edwardson
Journal:  PLoS One       Date:  2016-10-05       Impact factor: 3.240

3.  Comparison of median frequency between traditional and functional sensor placements during activity monitoring.

Authors:  Jeroen H M Bergmann; Selina Graham; Newton Howard; Alison McGregor
Journal:  Measurement (Lond)       Date:  2013-08       Impact factor: 3.927

  3 in total

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