A Quantitative Approach for Evaluating the Quality of Experience of Smart-Wearables From the Quality of Data and Quality of Information: An End User Perspective

A rapid growth in the smart-wearable industry is making it increasingly important to cater to the quality of experience (QoE) requirements of the end users. In this paper, we try to model the relationship between human experience and quality perception in relation to the smart-wearable segment. For this, the concepts of quality of data (QoD) and quality of information (QoI) are used. While QoD is concerned with the accuracy and precision of the data collected by the smart-wearables, QoI relates to the useful information that is obtained from the raw data captured by the devices via the companion applications of each wearable installed on a smartphone. A subjective experiment comprising of 40 participants and 5 wearable devices is performed in a free-living condition in order to create the QoE model. Four different approaches for weight determination are presented in this paper: balanced weight distribution, correlation-based distribution, hybrid distribution, and priority-based distribution while proposing the QoE model. Our results show that the priority-based distribution approach performs slightly better than the rest of the techniques and has the best correlation to the subjective QoE when compared against others. Based on the results, the appropriate recommendations are provided to the different smart-wearable vendors for improving their products, thereby ensuring greater user adoption.