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The dancing actions for entertainment robots are usually designed in advance and saved in a database according to the beats and rhythm of the given music. This research is devoted to developing a real-time algorithm that can detect the primary information of the music needed for the actions of entertainment robots. The computation of the proposed algorithm is very efficient and can satisfy the requirement of real-time processing by a digital signal controller. The digitized music signal is first normalized to make the algorithm robust for miscellaneous music volume. Next, the primary features of the beat for the music are extracted. Based on the extracted features, the algorithm will then identify the occurrence of beats in the music through the use of an optimized classifier. From the accumulated information of the timing for the beats, the algorithm can provide the predicted timing information of the next beat through regression analysis. The type and tempo of the given music can also be derived thereafter. The entertainment robot can thus move or dance according to the information derived by the algorithm. A graphical user interface (GUI) program in LabVIEW is also utilized to observe and verify the analysis results. In this study, the ratio for correct beat detection is greater than 90%. The prediction ratio for the correct timing of beats is over 80%, and it is 100% correct for both music type and music tempo.

Traditional electroencephalogram (EEG) measurements require conductive gel to ensure good conduction between the electrodes and skin (or scalp). But this gel may make patients uncomfortable because of possible skin allergies during long-term measurement. The measurement procedure is also tedious because pasting the electrodes to the patient’s scalp is time consuming. The required lead wires also constrain patient movements. This study uses conductive fabric as the capacitive electrode for EEG measurements. This paper provides the design considerations and the related theoretical analysis for this method. A long-term, null-input experiment was conducted to verify the stability of the proposed system. Experiments were conducted over the hair of patients to verify the feasibility of using the method during EEG monitoring. Various mental task experiments were also conducted. The changes in frequency domain analysis show that the proposed strategy discriminates between different mental tasks.