Physical activity recognition using embedded sensors has enabled by many context-aware applications in different areas. In sequential acceleration data there is a natural dependence between observations of movement or behavior, a fact that has been largely ignored in most analyses. In this paper, investigate the role that smart devices, including smartphones, can play in identifying activities of daily living. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g. We show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We show that the system can be used accurately to monitor both feet movement and use this result in many applications such as any playing. Time and frequency domain features of the signal were used to discriminate between activities, it demonstrates accuracy of 93% when employing a random forest analytical approach.