A universal, accurate intensity‐based classification of different physical activities using raw data of accelerometer

Accelerometers are increasingly used for objective assessment of physical activity. However, because of lack of the proprietary analysis algorithms, direct comparisons between accelerometer brands are difficult. In this study, we propose and evaluate open source methods for commensurate assessment of raw accelerometer data irrespective of the brand.

[1]  R. Laukkanen,et al.  Use of heart rate to predict energy expenditure from low to high activity levels. , 2003, International journal of sports medicine.

[2]  D. Bassett,et al.  Estimating energy expenditure using accelerometers , 2006, European Journal of Applied Physiology.

[3]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[4]  Mark S Tremblay,et al.  Physical activity and inactivity profiling: the next generation. , 2007, Canadian journal of public health = Revue canadienne de sante publique.

[5]  Timothy R Derrick,et al.  Shock and impact reduction in moderate and strenuous landing activities , 2008, Sports biomechanics.

[6]  Luc Cluitmans,et al.  Advancing from offline to online activity recognition with wearable sensors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Leena Choi,et al.  Validity of Physical Activity Intensity Predictions by ActiGraph, Actical, and RT3 Accelerometers , 2008, Obesity.

[8]  A. Goris,et al.  Detection of type, duration, and intensity of physical activity using an accelerometer. , 2009, Medicine and science in sports and exercise.

[9]  Melody Oliver,et al.  Utility of accelerometer thresholds for classifying sitting in office workers. , 2010, Preventive medicine.

[10]  R. J. Shephard,et al.  Breaks in Sedentary Time: Beneficial associations with metabolic risk , 2010 .

[11]  Kate Lyden,et al.  Accelerometer output and MET values of common physical activities. , 2010, Medicine and science in sports and exercise.

[12]  Timothy C Sell,et al.  Running Kinematics and Shock Absorption Do Not Change After Brief Exhaustive Running , 2011, Journal of strength and conditioning research.

[13]  Francisca Galindo Garre,et al.  Evaluation of neural networks to identify types of activity using accelerometers. , 2011, Medicine and science in sports and exercise.

[14]  Andrew J Young.  Their efforts deserve your appreciation. , 2012, Medicine and science in sports and exercise.

[15]  C. Matthews,et al.  Best practices for using physical activity monitors in population-based research. , 2012, Medicine and science in sports and exercise.

[16]  John Staudenmayer,et al.  Statistical considerations in the analysis of accelerometry-based activity monitor data. , 2012, Medicine and science in sports and exercise.

[17]  Patty Freedson,et al.  Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. , 2012, Medicine and science in sports and exercise.

[18]  L. Straker,et al.  Translation equations to compare ActiGraph GT3X and Actical accelerometers activity counts , 2012, BMC Medical Research Methodology.

[19]  Michael Marschollek,et al.  A Semi-Quantitative Method to Denote Generic Physical Activity Phenotypes from Long-Term Accelerometer Data – The ATLAS Index , 2013, PloS one.