@article{Mohd Kamarolzaman_Yeong_Lee Ming Su_Fei Chik_Duan_Chuan Tan_Hua Tan_Hua Chin_2017, title={Smart Advertising Robot with Image Recognition for Data Analytics}, volume={16}, url={https://elektrika.utm.my/index.php/ELEKTRIKA_Journal/article/view/44}, DOI={10.11113/elektrika.v16n3.44}, abstractNote={Conventional advertisement methods has the drawback of targeting inappropriate customer segments. A Smart Advertising Robot (SMADBOT) is proposed, aims to improve the effectiveness of advertising through targeted advertising. SMADBOT is a robot implemented with digital display which provides targeted advertisements through data analytics using data obtained from machine vision. Microsoft Cognitive Services is used to predict emotion, gender and age group of people without the need of a self-trained model. Microsoft Power BI is used to classified consumers into various groups to achieve effective targeted advertising. Robot Operating System (ROS) is used as a framework to integrate data from different sensors to perform autonomous navigation. Several metrics were used to evaluate the performance of SMADBOT including age, emotion prediction errors and stopping accuracy. Emotion prediction achieved a mean accuracy of 94% and had a navigation accuracy of 5.51cm in error. SMADBOT was deployed in real environment to further validate and test proposed system, where 184 face counts were collected after a 3-hour autonomous navigation. Data collected were successfully classified into various customer segments for effective targeted advertising.}, number={3}, journal={ELEKTRIKA- Journal of Electrical Engineering}, author={Mohd Kamarolzaman, Nik Ahmad Faisal and Yeong, Che Fai and Lee Ming Su, Eileen and Fei Chik, Sheng and Duan, Feng and Chuan Tan, Jeffery Too and Hua Tan, Ping and Hua Chin, Patrick Jun}, year={2017}, month={Dec.}, pages={11–16} }