Because of that current computing device (mobile devices) are equipped with powerful built-in sensors such as acceleration sensors (I e. accelerometers), gps sensors, audio sensors (I e. microphones), image sensors

As previously highlighted, since most individuals use their computing devices every day and their usage behavior is reflected on their computing device use, studying ways of aiding ergonomic advisory system through thus computing device is important. To do this, the data collected from the respondents and analysis results presented in chapter 4 provide a foundation for us to design a real-time ergonomic advisory system. Therefore, an appropriate mediation mechanism in light of the ergonomic principle is necessary to support computing device users in developing healthy usage habits.

Because of that current computing device (mobile devices) are equipped with powerful built-in sensors such as acceleration sensors (i.e. accelerometers), GPS sensors, audio sensors (i.e. microphones), image sensors (i.e. cameras), light sensors, temperature sensors, and direction sensors (i.e. compasses), etc. [4] [38], it is now possible to infer a range of user behavior on the mobile devices in a real-time fashion, allowing users to receive notifications/feedbacks in response to everyday lifestyle choices and operations that enable them to better manage their ergonomic mobile phone use.

To this end, the proposed framework architecture is depicted in fig 5. The framework is deployed at the user/client-side and server-side consisting of several modules and components described in detail in the following subsections.