Medicine intake, as prescribed by physicians and health care providers, is important not only for minimizing the risk of relapse but also to treating conditions and improving one’s overall well-being. However, adherence to a medication routine seems to be a problem for some people which is usually affected by a variety of factors such as hectic day-to-day activity schedules, poor prescription instruction, concurrent intake of multiple medications, and forgetfulness. Medication adherence has been then considered as one of the major medical problems globally. In such cases, a medical device that could alert and remind patients in taking their medicines on time will come in handy. Consequently, this study aimed to design and develop a pocket-sized electronic pillbox device using TFT LCD display, Arduino microcontroller, Piezo Buzzer (for sound notification), Eccentric Rotating Mass (for vibration notification), Lithium Ion battery as power source, and plastic organizer as the main body. The said pillbox device will act as a countermeasure for medication non-adherence particularly by patients under the case of polypharmacy. Thus, this study focused on the design and development of the prototype, hardware testing and system qualification only. Furthermore, this paper is part of a future study where the assessment and measure of device behavior and adherence will be conducted to compare whether the utilization of pillbox device has an impact to the people who are using it.

An ideal library makes every last patron feels what it is like to have their own library where one can traverse a world of knowledge, and then build their personal learning environment. Parallel to this conception is the intensification of fine-tuning the conventional librarianship to transform it into a center for new digital learning. As a contribution to this digital revolution, this paper presents an innovative way of renovating the house of dusty books into the center of creativity, research, and partnership through the fusion of traditional librarianship, self-service solutions, and human-computer interaction. The self-service system employs technologies and modalities such as touch screen-assistive technology for the kiosk terminal with the inclusion of a built-in camera, speaker, microphone, and lights, Automatic Speech Recognition, Radio-Frequency Identification and Content-Based Image Retrieval for holdings circulation and monitoring. In conformance with the ISO 9241-210 (Human-centred design for interactive systems), a series of user-centered evaluations were accomplished to obtain early feedback, and to validate that the user requirements have been satisfied at a later stage of the project cycle. To integrate a humanistic approach, the HCSDLC, or Human-Centered Systems Development Life Cycle Methodology, was utilized to complement the four main user-centered design activities specified in the ISO 9241-210 standard. The prototype designs and final self-service library system were assessed in terms of efficiency, effectiveness, and user satisfaction using metrics defined in the ISO/IEC 25022.

The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.