Utilising emotion information through human-computer interactions is associated with practical applications that are beginning to pervade our every-day life. However, the challenges systems which aim to classify emotive states pose are many. Starting from the fuzziness of emotional boundaries to the computational demand of widely used algorithms and classifier functions. In this project an embedded platform was used for an affect recognition task which included computing features and functionals, database preparation, fitting data to a classifier, an evaluation of accuracy and timing the embedded board implemented algorithms. Since computation time is a critical factor in embedded platforms’ performance evaluation, the importance of timing the algorithms was emphasised. The tasks of this project were facilitated by making use of the openSMILE toolkit and Python functions for computing the features and implementing the classifier respectively. Following the assessment of different classifiers under diverse parametrisation, the prepared training data set was fitted to the SVM classifier and prediction evaluation was performed in terms of accuracy and execution time, both of which were compared with embedded platform’s results. Accuracy obtained is comparable to other works. The running time on the Raspberry Pi shows improvement as we decrease the sampling rate, but it is above the acceptable margin for real-time applications. The high accuracy is partially indebted to the meticulous database preparation, which ensured that the two data sets were evenly filled with emotive content from different octants.