S. de Vries

Evaluation of Neural Networks to Identify Types of Activity Among Children Using Accelerometers, Global Positioning Systems and Heart Rate Monitors

There is a growing awareness of the health benefits of physical activity during childhood and adolescence. However, there is still much to be learned about the nature of children’s physical activity patterns and the mechanisms underlying these benefits (Rowland, 2007; Twisk, 2001). The physical activity pattern of children is very different from that of adults. Children’s physical activity pattern is characterized by frequent spasmodic bursts of short duration (Baquet et al., 2007). They participate in intermittent and unstructured activities and the type of activities children engage in changes as they develop, going from informal active play during early childhood to activities that begin to mirror those of adults during adolescence (Salmon & Timperio, 2007). The understanding of children’s physical activity pattern has been hampered by the lack of satisfactory instruments for measuring it. Children’s physical activity has traditionally been measured with self-reports. Self-reports are easily administered, low-cost measurements. However, they do not capture the sporadic short-burst nature of children’s physical activity very well (Baquet et al., 2007). Furthermore, self-reports are influenced by recall bias or social desirability. Accelerometers have therefore, in recent times, become the method of choice in physical activity research. These lightweight, unobtrusive devices provide objective information about the frequency, intensity, and duration of physical activity. In most studies, the raw acceleration signal is converted into activity counts. Total or mean activity counts per day and minutes per day spent above a certain intensity threshold are reported. Activity counts are linearly related to energy expenditure. This does not value the richness of accelerometer data (Esliger et al., 2005) because this approach is unable to correctly distinguish between different types of activities with different levels of energy expenditure but that produce similar mean activity counts over time. A solution for this problem is to use not only the mean counts over a certain time period, but also more information about the distribution of these counts (i.e. standard deviation, percentiles) over time. Recently, statistical models have been developed to identify specific types of physical activities based on a new methodology for processing accelerometer data. The most common classification algorithms have been developed by using the pattern-recognition or