Physical Activity Classification Using the GENEA Wrist-Worn Accelerometer

Abstract

We have successfully developed algorithms suitable for use with wrist-worn accelerometers for detecting certain types of physical activities; the performance is comparable to waist-worn accelerometers for assessment of physical activity.

LifeLogging: Personal Big Data

“…For example, the capture frequency from sensors can range from +30Hz for devices such as accelerometers, Zhang et al (2012), to less than one reading per day for course-grained location changes or personal encounters, Byrne et al (2007).…”

Guide to the Assessment of Physical Activity: Clinical and Research Applications

“…[125][126][127]213 Several groups [128][129][130]159,214,215 have investigated how to extract and use more of the accelerometer signal using machinelearning algorithms to process data. These analyses provide detailed information about overall physical activity behavior, including time spent in different intensities of physical activity and activity type.…”

Individual classification of elementary school children’s physical activity: A time-efficient, group-based approach to reference measurements

“…In principle, different methods are suited for classification (for an extensive overview of classification techniques, see Preece et al, 2009). The methods used include k-nearest neighbor (e.g., Bao & Intille, 2004;Zhang, Rowlands, Murray, & Hurst, 2012), hidden Markov models (e.g., Pober, Staudenmayer, Raphael, & Freedson, 2006), and artificial neural networks (e.g., Hagenbuchner, Cliff, Trost, van Tuc, & Peoples, 2015;Staudenmayer, Pober, Crouter, Bassett, & Freedson, 2009). The Bstate-of-the-art^method that has proven effective for such tasks is the use of support vector machines (SVMs; e.g., He & Jin, 2009).…”