Human activity recognition (HAR) aims to classify human activities or predict human behavior. Typical HAR systems use wearable sensors and/or mobile devices with built-in sensing capabilities. Several factors can influence activity recognition, such as classification models, sensors availability and size of data window.
Human activity recognition (HAR) is a classification task that aims to
classify human activities or predict human behavior by means of features
extracted from sensors data. Typical HAR systems use wearable sensors and/or
handheld and mobile devices with built-in sensing capabilities. Due to the
widespread use of smartphones and to the inclusion of various sensors in all
contemporary smartphones (e.g., accelerometers and gyroscopes), they are
commonly used for extracting and collecting data from sensors and even for
implementing HAR systems. When using mobile devices, e.g., smartphones, HAR
systems need to deal with several constraints regarding battery, computation
and memory. These constraints enforce the need of a system capable of managing
its resources and maintain acceptable levels of classification accuracy.
Moreover, several factors can influence activity recognition, such as
classification models, sensors availability and size of data window for feature
extraction, making stable accuracy a difficult task. In this paper, we present
a semi-supervised classifier and a study regarding the influence of
hyperparameter configuration in classification accuracy, depending on the user
and the activities performed by each user. This study focuses on sensing data
provided by the PAMAP2 dataset. Experimental results show that it is possible
to maintain classification accuracy by adjusting hyperparameters, like window
size and windows overlap factor, depending on user and activity performed.
These experiments motivate the development of a system able to automatically
adapt hyperparameter settings for the activity performed by each user.