Literature DB >> 23851946

Sensor positioning for activity recognition using wearable accelerometers.

L Atallah, B Lo, R King.   

Abstract

Activities of daily living are important for assessing changes in physical and behavioral profiles of the general population over time, particularly for the elderly and patients with chronic diseases. Although accelerometers have been used widely in wearable devices for activity classification, the positioning of the sensors and the selection of relevant features for different activity groups still pose significant research challenges. This paper investigates wearable sensor placement at different body positions and aims to provide a systematic framework that can answer the following questions: 1) What is the ideal sensor location for a given group of activities? and 2) Of the different time-frequency features that can be extracted from wearable accelerometers, which ones are the most relevant for discriminating different activity types?

Entities:  

Year:  2011        PMID: 23851946     DOI: 10.1109/TBCAS.2011.2160540

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  47 in total

1.  Implementation study of wearable sensors for activity recognition systems.

Authors:  Hamed Rezaie; Mona Ghassemian
Journal:  Healthc Technol Lett       Date:  2015-07-13

Review 2.  Unobtrusive sensing and wearable devices for health informatics.

Authors:  Ya-Li Zheng; Xiao-Rong Ding; Carmen Chung Yan Poon; Benny Ping Lai Lo; Heye Zhang; Xiao-Lin Zhou; Guang-Zhong Yang; Ni Zhao; Yuan-Ting Zhang
Journal:  IEEE Trans Biomed Eng       Date:  2014-05       Impact factor: 4.538

3.  A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.

Authors:  Katherine Ellis; Jacqueline Kerr; Suneeta Godbole; Gert Lanckriet; David Wing; Simon Marshall
Journal:  Physiol Meas       Date:  2014-10-23       Impact factor: 2.833

4.  Posture and movement classification: the comparison of tri-axial accelerometer numbers and anatomical placement.

Authors:  Emma Fortune; Vipul A Lugade; Kenton R Kaufman
Journal:  J Biomech Eng       Date:  2014-05       Impact factor: 2.097

5.  Wrist-based cut-points for moderate- and vigorous-intensity physical activity for the Actical accelerometer in adults.

Authors:  Keith M Diaz; David J Krupka; Melinda J Chang; Ian M Kronish; Natalie Moise; Jeff Goldsmith; Joseph E Schwartz
Journal:  J Sports Sci       Date:  2017-02-23       Impact factor: 3.337

6.  Estimating activity and sedentary behavior from an accelerometer on the hip or wrist.

Authors:  Mary E Rosenberger; William L Haskell; Fahd Albinali; Selene Mota; Jason Nawyn; Stephen Intille
Journal:  Med Sci Sports Exerc       Date:  2013-05       Impact factor: 5.411

7.  Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities.

Authors:  Emma Fortune; Vipul Lugade; Melissa Morrow; Kenton Kaufman
Journal:  Med Eng Phys       Date:  2014-03-20       Impact factor: 2.242

8.  Step detection using multi- versus single tri-axial accelerometer-based systems.

Authors:  E Fortune; V A Lugade; S Amin; K R Kaufman
Journal:  Physiol Meas       Date:  2015-11-23       Impact factor: 2.833

9.  Activity recognition using a single accelerometer placed at the wrist or ankle.

Authors:  Andrea Mannini; Stephen S Intille; Mary Rosenberger; Angelo M Sabatini; William Haskell
Journal:  Med Sci Sports Exerc       Date:  2013-11       Impact factor: 5.411

10.  Energy-aware Activity Classification using Wearable Sensor Networks.

Authors:  Bo Dong; Alexander Montoye; Rebecca Moore; Karin Pfeiffer; Subir Biswas
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2013-05-29
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