Smartphone Apps for Food Purchase Choices: Scoping Review of Designs, Opportunities, and Challenges

Background: Smartphone apps can aid consumers in making healthier and more sustainable food purchases. However, there is still a limited understanding of the different app design approaches and their impact on food purchase choices. An overview of existing food purchase choice apps and an understanding of common challenges can help speed up effective future developments.Objective: We examined the academic literature on food purchase choice apps and provided an overview of the design characteristics, opportunities, and challenges for effective implementation. Thus, we contribute to an understanding of how technologies can effectively improve food purchase choice behavior and provide recommendations for future design efforts.Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we considered peer-reviewed literature on food purchase choice apps within IEEE Xplore, PubMed, Scopus, and ScienceDirect. We inductively coded and summarized design characteristics. Opportunities and challenges were addressed from both quantitative and qualitative perspectives. From the quantitative perspective, we coded and summarized outcomes of comparative evaluation trials. From the qualitative perspective, we performed a qualitative content analysis of commonly discussed opportunities and challenges.Results: We retrieved 55 articles, identified 46 unique apps, and grouped them into 5 distinct app types. Each app type supports a specific purchase choice stage and shares a common functional design. Most apps support the product selection stage (selection apps; 27/46, 59%), commonly by scanning the barcode and displaying a nutritional rating. In total, 73% (8/11) of the evaluation trials reported significant findings and indicated the potential of food purchase choice apps to support behavior change. However, relatively few evaluations covered the selection app type, and these studies showed mixed results. We found a common opportunity in apps contributing to learning (knowledge gain), whereas infrequent engagement presents a common challenge. The latter was associated with perceived burden of use, trust, and performance as well as with learning. In addition, there were technical challenges in establishing comprehensive product information databases or achieving performance accuracy with advanced identification methods such as image recognition.Conclusions: Our findings suggest that designs of food purchase choice apps do not encourage repeated use or long-term adoption, compromising the effectiveness of behavior change through nudging. However, we found that smartphone apps can enhance learning, which plays an important role in behavior change. Compared with nudging as a mechanism for behavior change, this mechanism is less dependent on continued use. We argue that designs that optimize for learning within each interaction have a better chance of achieving behavior change. This review concludes with design recommendations, suggesting that food purchase choice app designers anticipate the possibility of early abandonment as part of their design process and design apps that optimize the learning experience

Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design

Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data

Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming

Animal production (e.g., milk, meat, and eggs) provides valuable protein production for human beings and animals. However, animal production is facing several challenges worldwide such as environmental impacts and animal welfare/health concerns. In animal farming operations, accurate and efficient monitoring of animal information and behavior can help analyze the health and welfare status of animals and identify sick or abnormal individuals at an early stage to reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, sensors, big data, machine learning, and artificial intelligence are used to improve management efficiency, reduce production costs, and enhance animal welfare. Although these technologies still have challenges and limitations, the application and exploration of these technologies in animal farms will greatly promote the intelligent management of farms. Therefore, this Special Issue will collect original papers with novel contributions based on technologies such as sensors, big data, machine learning, and artificial intelligence to study animal behavior monitoring and recognition, environmental monitoring, health evaluation, etc., to promote intelligent and accurate animal farm management

Desarrollo y versatilidad del algoritmo de discretización Ameva.

Esta tesis presentada como un compendio de artículos, analiza el problema de reconocimiento de actividades y detección de caídas en dispositivos móviles donde el consumo de batería y la precisión del sistema son las principales áreas de investigación. Estos problemas se abordan mediante el establecimiento de un nuevo algoritmo de selección, discretización y clasificación basado en el núcleo del algoritmo Ameva. Gracias al proceso de discretización, se obtiene un sistema eficiente en términos de energía y precisión. El nuevo algoritmo de reconocimiento de actividad ha sido diseñado para ejecutarse en dispositivos móviles y smartphones, donde el consumo de energía es la característica más importante a tener en cuenta. Además, el algoritmo es eficiente en términos de precisión dando un resultado en tiempo real. Estas características se probaron tanto en una amplia gama de dispositivos móviles utilizando diferentes datasets del estado del arte en reconocimiento de actividades así como en escenarios reales como la competición EvAAL donde personas no relacionadas con el equipo de investigación llevaron un smartphone con el sistema desarrollado. En general, ha sido posible lograr un equilibrio entre la precisión y el consumo de energía. El algoritmo desarrollado se presentó en el track de reconocimiento de actividades de la competición EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), que tiene como objetivo principal la medición del rendimiento de hardware y software. El sistema fue capaz de detectar las actividades a través del conjunto establecido de puntos de referencia y métricas de evaluación. Se desarrolló para varias clases de actividades y obtiene una gran precisión cuando hay aproximadamente el dataset está balanceado en cuanto al número de ejemplos para cada clase durante la fase de entrenamiento. La solución logró el primer premio en la edición de 2012 y el tercer premio en la edición de 2013.This thesis, presented as a set of research papers, studies the problem of activity recognition and fall detection in mobile systems where the battery draining and the accuracy are the main areas of researching. These problems are tackled through the establishment of a new selection, discretization and classification algorithm based on the core of the algorithm Ameva. Thanks to the discretization process, it allows to get an efficient system in terms of energy and accuracy. The new activity recognition algorithm has been designed to be run in mobile systems, smartphones, where the energy consumption is the most important feature to take into account. Also, the algorithm had to be efficient in terms of accuracy giving an output in real time. These features were tested both in a wide range of mobile devices by applying usage data from recognized databases and in some real scenarios like the EvAAL competition where non-related people carried a smartphone with the developed system. In general, it had therefore been possible to achieve a trade-off between accuracy and energy consumption. The developed algorithm was presented in the Activity Recognition track of the competition EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), which has as main objective the measurement of hardware and software performance. The system was capable of detecting some activities through the established set of benchmarks and evaluation metrics. It has been developed for multi-class datasets and obtains a good accuracy when there is approximately the same number of examples for each class during the training phase. The solution achieved the first award in 2012 competition and the third award in 2013 edition

WICC 2016 : XVIII Workshop de Investigadores en Ciencias de la Computación

Actas del XVIII Workshop de Investigadores en Ciencias de la Computación (WICC 2016), realizado en la Universidad Nacional de Entre Ríos, el 14 y 15 de abril de 2016.Red de Universidades con Carreras en Informática (RedUNCI