The rapid advancement of intelligent technologies, including sensing devices, artificial intelligence, and the Internet of Things, has significantly accelerated the progress in industrial technology, particularly within the medical enterprise sector. Wearable innovations for health management have introduced novel approaches to physiological monitoring and early disease detection, contributing to an improved quality of life. In the context of sustainable development, wearable devices demonstrate considerable potential for supporting long-term healthcare solutions, particularly in the post-pandemic era, where the demand for smart health solutions continues to rise. This study aims to identify critical product design indicators for wearable devices that align with sustainable health management goals. Utilizing expert questionnaires and employing a combination of the Fuzzy Delphi Method and the DEMATEL-based Analytic Network Process (ANP), this research systematically evaluates the key factors influencing wearable device design. The findings highlight three primary aspects, six criteria, and 16 design indicators, with pivotal factors including “Compatibility”, “Foresight”, “Integration”, “Comfort”, “Appearance”, “Customization”, and “Intelligence”. These indicators provide a comprehensive framework for developing wearable devices that address diverse user needs while promoting individual well-being and sustainable health management. This study offers valuable insights into the design and development of wearable devices that support sustainable healthcare practices, advance social responsibility, and strengthen preventive care initiatives.

In modern, fast-paced lifestyles, food containers have become a popular solution for quick meals; however, they have significantly contributed to the increase in plastic packaging waste, which poses a substantial environmental risk. The prevalent use of non-biodegradable plastic containers exacerbates plastic pollution, contradicting government regulations designed to reduce plastic usage and promote sustainability. To address these issues, this study integrates TRIZ, ergonomics, inherent safety, and universal design methodologies, with an emphasis on sustainability. First, the technical contradiction matrix from TRIZ is applied to identify parameters for improvement without inducing negative side effects. Ergonomics principles ensure the development of user-friendly designs, while inherent safety and universal design approaches aim for accessibility and full biodegradability. Through a systematic innovation design process, this research proposes a biodegradable food container capable of decomposing entirely into particles and water within one month, blending naturally with the soil without compromising functionality. Moreover, the container incorporates a quick-alignment mechanism and an intuitive sealing design that ensures airtightness and spill prevention. As a result, the product not only meets user demands but also addresses pressing environmental concerns, aligning with global sustainability goals.

This study implemented an innovative system that trains a speech recognition model based on the DeepSpeech2 architecture using Python for voice control of a robot on the LabVIEW platform. First, a speech recognition model based on the DeepSpeech2 architecture was trained using a large speech dataset, enabling it to accurately transcribe voice commands. Then, this model was integrated with the LabVIEW graphical user interface and the myRIO controller. By leveraging LabVIEW’s graphical programming environment, the system processed voice commands, translated them into control signals, and directed the robot’s movements accordingly. Experimental results demonstrate that the system not only accurately recognizes various voice commands, but also controls the robot’s behavior in real time, showing high practicality and reliability. This study addresses the limitations inherent in conventional voice control methods, demonstrates the potential of integrating deep learning technology with industrial control platforms, and presents a novel approach for robotic voice control.