Abstract
The availability of diverse and powerful sensors embedded in modern Smartphones/mobile devices has created exciting opportunities for developing context-aware applications. Although there is good capacity for collecting and classifying human activity data with such devices, data pre-processing and model building techniques that achieve this goal are required to operate while meeting hardware resource constraints, particularly for real-time applications. In this paper, we present a comparison study for HAR exploiting feature selection approaches to reduce the computation and training time needed for the discrimination of targeted activities while maintaining significant accuracy. We validated our approach on a publicly available dataset. Results show that Recursive Feature Elimination method combined with Radial Basis Function Support Vector Machine classifier offered the best tradeoff between training time/recognition performance.
Highlights
Researchers are developing many new challenging application scenarios based on mobile phone sensors in various aspects related to the Smart City concept such as in healthcare, in smart homes and in smart transportation
We will present the smartphone internal sensors used for Human Activity Recognition (HAR) in our study, overview the current trends and constraints related to activity recognition process
We obtained 7 features in the subset selected by the CBF algorithm and 20 features in the subset selected by the MDA
Summary
Researchers are developing many new challenging application scenarios based on mobile phone sensors in various aspects related to the Smart City concept such as in healthcare (e.g., fitness, diabetes, elderly and obesity assisted surveillance), in smart homes (e.g., context aware indoor air quality and thermal comfort control) and in smart transportation (e.g., traffic congestion). It turns out that modern smartphones/mobile devices can play a key role in the recognition of complex states of the user and its environment, the inference of her physical activity thanks to the multitude of embedded sensors and Machine Learning techniques (ML). Human Activity Recognition (HAR) using Smartphones has been widely studied during recent years mainly because Smartphones are not intrusive, widely used in everyday life, and wearable. Modern Smartphones devices integrate powerful processors, multiple communication technologies, multimedia capability and memory storage. The variety of available wireless access and communication technologies implemented provide means of long-distance communication to the other user’s body wearable sensors. We will present the smartphone internal sensors used for HAR in our study, overview the current trends and constraints related to activity recognition process
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