To enumerate Staphylococcus aureus in food, Baird-Parker Agar (BPA) is usually used in the conventional method, However it requires time and space for the preparation and plating, and incubation. Thus, use of the 3MTM PetrifilmTM Staph Express Count Plate (STX Petrifilm) might be appropriate to solve these challenging problems. The purpose of this study was to compare the efficiency of STX Petrifilm with BPA for enumeration of S. aureus in various foods. A mixture of S. aureus strains ATCC29213, ATCC25923, and ATCC13565 was inoculated on marinated pork chop, beef (chuck tender), dried filefish, semi-dried squid, rice cake, and Japchae (stir-fried glass noodles) at 2, 3, 5, and 7 Log CFU/g. S. aureus cell counts were enumerated by spread-plating on STX Petrifilm and BPA after 0 and 24 hours at 4oC (marinated pork chop, beef, semi-dried squid, and stir-fried glass noodles) and 25oC (dried filefish and rice cake). Recovery of STX Petrifilm for S. aureus from various food samples was compared with BPA, and the results showed that there were no significant differences between two selective media in all cases. The results indicated that STX Petrifilm had enough efficiency to recover S. aureus from various foods as well as saving time and space.

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform ‘tying up shoe’ using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user’s intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.