The ONParkinson mHealth platform aims to empower an integrated assistance to support end-users of the triad “people with Parkinson’s Disease, their carers and health professionals”, promoting the self-management in Parkinson’s disease. Therefore, ONParkinson is expected to optimize the communication between the triad users, helping them find relevant knowledge to support their clinical issues, as well as allowing the monitoring of patients` daily routine and the recommendation for daily exercises. This mHealth solution was created and materialized after an initial study of the end-users’ needs. This paper presents the usability study of the first version of the ONParkinson prototype. According to the usability tests’ findings, ONParkinson was perceived by the triad users as easy to use, with functionalities well integrated, useful and attractive. Some recommendations were suggested to enhance its usability, users’ satisfaction and continuance intentions.

“Com a crescente pervasividade (Zahid Farid 2013) dos Sistemas Computacionais, a importância do Context-awareness nas Ciências da Computação tem crescido nas últimas décadas.” (Lovett e O'Neill, Mobile Context Awareness 2012) Context-awareness, que se pode definir como a ideia de que os sistemas computacionais podem sentir e reagir a diferentes situações, tem sido um caso de estudo entre a comunidade. Um dos mais ubíquos dispositivos nesta evolução têm sido os dispositivos móveis, principalmente, os smartphones. A sua popularidade e a forma como o seu uso generalizado no dia a dia das pessoas tem aumentado, e o seu hardware cada vez mais sofisticado - sensores, processamento, redes sem fios - aumentaram substancialmente o potencial do Context-Awareness no mundo. Nos últimos anos têm surgido aplicações nas mais diversas áreas científicas de de negócio, que fazem uso das capacidades de determinação do contexto no sentido de permitir usufruir de serviços que, devido ao seu conhecimento do contexto do utilizador, fazem com que a experiência de utilização seja, embora num ecrã normalmente de menores dimensões, mais agradável do que o uso de outros dispositivos. Este é um dos principais segredos do sucesso da computação móvel. Neste estudo, para além da análise bibliográfica e da determinação do estado da arte, são apresentados três casos práticos de sistemas computacionais baseados da determinação do contexto do utilizador.

Chemistry students have difficulty understanding molecular structures and their functions. To aide their comprehension, molecular visualization software has been developed to run on smart phones, but in order to positively influence learning it must have a high degree of usability (usability measures how software is used in terms of efficiency, efficacy and satisfaction). This paper describes a usability study of molecular visualization software running on a smart phone, where chemistry students analyzed molecular models. Results showed very good usability and 95% of students wanted to use it in further classes.

Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmen tal conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recogni tion of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Ex perimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with mo tor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions.