This paper presents a conceptual framework of a deep learning and IoT integrated solution to address the persistent issue of missed or mistimed medication intake. Timely medication intake particularly for time sensitive medications such as antibiotics is critical for effective healthcare outcomes. Yet forgetfulness remains a widespread and serious concern for all age demographics. To address this challenge, we propose a hybrid solution integrating a mobile application with a custom developed wearable wristband and an open API to contribute to development in the digital heath sector. We have developed a deep learning model based on YOLOv11 to interpret handwritten and printed prescriptions, extracting medicine names, dosages, schedules, types, and course durations with an added feature that allows patients to self-annotate the exact time they prefer to take each medication. Input images are processed using grayscale normalization and Otsu thresholding. The interpreted data is structured then used to generate time bound reminders and provide auditory notification with medicine names via a mobile application. A companion wearable wristband and the mobile application, both currently under development, are designed to provide haptic remainders and real-time dose tracking, with offline synchronization support for low-connectivity environments. The system also includes an open API to facilitate integration into broader digital health ecosystems. While the machine learning model has been fully developed and tested locally, ongoing work focuses on the implementation of the mobile interface, wearable device, and cloud-based API. This research aims to deliver an accessible and scalable healthcare framework that ensures promotes timely medication intake and accurate prescription interpretation, with future efforts directed toward large-scale testing and compliance with ISO 14971 standards.

Model-Based Systems Engineering (MBSE) has emerged with great potential to fulfill the non-linearly rising demand in interdisciplinary engineering, e.g., product development. However, the variety and complexity of MBSE tools pose difficulties in particular industrial applications. This paper tries to serve as a guideline to find the ideal tool for a specific industrial application as well as to highlight the key criteria that an industry might consider. For this purpose, we propose a logical framework for MBSE tool selection, which is based on market research, the approaches of Quality Function Deployment (QFD), and decision matrix. As customers are at the center of any product, accordingly the needs of MBSE tool users are addressed within this research as the fundamental starting point. Market research and extensive discussions with MBSE tool vendors and academia show the current situation of MBSE tools. To compare the performance of the considered tools, a set of user needs is defined. QFD is performed to analyze the user needs with respect to evaluable technical properties. Subsequently each tool performance is assessed using a decision matrix. Through this process, a well-defined functional structure of MBSE tools is sketched, and in order to identify the properties of an ideal tool, all the attributes of different MBSE tools are mapped to a common platform. For the purpose of evaluation, we apply our proposed logical framework to select an exemplary MBSE tool for interdisciplinary application.