Search Results (4)

The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor alignment with daily life. This review surveys the current landscape of at-home healthcare technologies, including wearable vital sign monitors, digital diagnostics and body composition assessment tools. We synthesize insights from the existing literature for this narrative review, highlighting strengths and limitations in sensing accuracy, user experience and integration into daily health routines. Special attention is given to the role of AI in enabling real-time insights, adaptive feedback and predictive monitoring across these devices. To examine persistent adoption challenges from a user-centered perspective, we reflect on the Pi-CON methodology, a conceptual framework previously introduced to stimulate discussion around passive, non-contact, and continuous data acquisition. While Pi-CON is highlighted as a representative methodology, recent external studies in multimodal sensing, RFID-based monitoring, and wearable–ambient integration confirm the broader feasibility of unobtrusive, passive, and continuous health monitoring in real-world environments. We conclude with strategic recommendations to guide the development of more accessible, intelligent and user-aligned smart healthcare solutions. Full article

Augmenting the author’s prior research on lyrical code-switching, as presented in Picone, “Artistic Codemixing”, published in 2002, various conceptual frameworks are made explicit, namely the enlistment of multimodal and intertextual approaches for their methodological usefulness in analyzing and interpreting message-making that incorporates lyrical code-switching as one of its components. Conceived as a bipolarity, the rooted (or local) and the transcendent (or global), each having advantages in the negotiation of identity, is also applied to the analysis. New departures include the introduction of the notion of “curated lyrical code-switching” for the purpose of analyzing songs in which multiple performers are assigned lyrics in different languages, as a function of their respective proficiencies, as curated by the person or persons having authorial agency and taking stock of the social semiotics relevant to the anticipated audience. Moving beyond the negotiation of the identity of the code-switching composer or performer, in another new departure, attention is paid to the musical identity of the listener. As a reflection of the breadth of lyrical code-switching, a rich assortment of examples draws from the musical art of Beyoncé, Jon Batiste, Stromae, Shakira, BTS, NewJeans, Indigenous songsmiths, Cajun songsmiths, Latin Pop and Hip-Hop artists, songs composed for international sports events, and other sources. Full article

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time. Full article

Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology. Full article