Peripheral arterial disease (PAD) is an occlusive vascular disease of the lower extremities with increasing incidence and prevalence numbers and therefore rising health care costs. Mobile applications have great potential to improve health system efficiency and can assist in overcoming the increasing health care costs. In this review all available mobile applications developed for PAD are summarized and a new innovative application is introduced.Available applications for PAD are scarce and currently aim at prevention, teleconsultation and telemonitoring via questionnaires and activity tracking. Integration in the daily workflow of the physician is an overlooked aspect of these applications. JBZetje is an innovative mobile application that provides remote care, information services and self-service tools, and enables connection with the electronic health record (EHR).Connection of the application with the EHR generates a complete overview of a patient's current health status. This will reinforce the tendency toward personalized medicine and will probably lead to reduced health care costs and increase the quality of care. Future feasibility studies should answer the question whether the use of mobile applications is feasible in the PAD population.
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http://dx.doi.org/10.1080/17434440.2021.1935876 | DOI Listing |
Sensors (Basel)
December 2024
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
In this paper, a sub-1dB Low Noise Amplifier (LNA) with several gain modes, including amplification and attenuation modes required for the fifth and fourth generations (5G/4G) of mobile network applications, is proposed. Its current consumption is adaptive for every gain mode and varies to lower currents for lower amplifications due to the importance of current consumption for mobile network applications. The proposed LNA features an innovative architecture with a three-core input structure supporting multi-gain modes, achieving high gain and ultra-low noise performance.
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December 2024
Department of Mining and Geological Engineering, University of Arizona, Tucson, AZ 8572, USA.
Mining is a critical industry that provides essential minerals and resources for modern society. Despite its benefits, the industry is also recognized as one of the most dangerous occupations, with geotechnical hazards being a primary concern. This study introduces the hazard recognition in underground mines application (HUMApp), a mobile application developed to enhance safety within underground mines by efficiently identifying geotechnical hazards, specifically focusing on roof falls.
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December 2024
Department of Mobile Systems Engineering, Dankook University, Yongin 16890, Republic of Korea.
As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes' various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent.
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December 2024
Department of Psychiatry, Penn Center for Mental Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents.
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December 2024
Postgraduate Program in Biotechnology, Parnaíba Delta Federal University, Parnaíba 64202-020, Brazil.
Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person's pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM.
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