Last January 20, 2017 (Friday), five senior Bachelor of Science in Electronics Engineering students from the Faculty of Engineering emerged as Champions in the National Computer Research and Engineering Symposium, organized by the Polytechnic University of the Philippines.

The thesis, entitled “Evaluation of Muscle Fatigue degree using Surface Electromyography and Accelerometer signals in fall detection systems,” investigated “whether the analysis of muscle fatigue degree may enhance the performance of existing fall detection systems that utilize both surface electromyography (SEMG) and accelerometer (ACC) sensors.”

The abstract of the paper read: “SEMG and ACC signals were measured and recorded from 20 healthy study volunteers. A series of pre-defined activities that mimic fall events were performed by the study volunteers. These activities were conducted in a controlled environment. Acquired SEMG signals were pre-processed to eliminate unwanted signals and distortion. Discriminative features were then extracted from the clean signals, and these extracted features were combined with the accelerometer data for classification using an Artificial Neural Network (ANN) classifier. Results showed that the combination of SEMG and ACC data have relatively increased the accuracy of fall detection systems.”

The members of the group were John Paul Franklyn E. Ocampo, John Angelo T. Dizon, Clarence Vincent I. Reyes, Jon Joseph C. Capitulo, and Juncarl Kevin T. Tapang. Their adviser was Mr. Siegfred V. Prado.