— Human activity recognition is influential subject in different fields of human daily life especially in the mobile health. As the smartphone becomes an integrated part of human daily life which has the ability of complex computation, internet connection and also contains a large number of hardware sensors, encourage implementation of the human activity recognition system. Most of the works done in this field imposed the restriction of firmly fixing the smartphone in a certain position on the human body, together with machine learning mechanism, to facilitate the process of classifying human activities from the smartphone sensors raw data. To overcome this restriction, the proposed approach incorporated a smartwatch, fixed on the human ankle, together with smartphone freely carried by the user. The use of smartwatch assisted in providing distinctly separable signal variation from the smartwatch accelerometer and gyroscope sensors raw data which in turn facilitated the use of a threshold-based mechanism to classify 20 various human activities. Furthermore, this work provides a service for remotely real-time monitoring of the user human activities the system is tested with different subjects and achieved an accuracy of 97.5%. .