Drone-Driven Running:Exploring the Opportunities for Drones to Support Running Well-being through a Review of Running and Drone Interaction Technologies

There is an underexplored interaction space for drones that can be utilised as running interaction technology, distinct from human drone interaction that warrants foregrounding. This paper consolidates the current state of art in running interaction technology through a review of relevant studies and commercial technologies in a framework positioned using dimensions related to the form of interaction as identified in the sports ITECH framework. Our analysis highlights the unmet opportunities in running interaction technology and presents the potential of drones to further support runners. The potential of drones to support various forms of interaction are supported using exemplar research done in human-drone interaction. Through our findings, we hope to inform and expedite future research and practice in the field of running interaction technology and runner drone interaction by supporting researchers in defining and situating their contributions.</p

Understanding different types of recreational runners and how they use running-related technology

This study aims to help professionals in the field of running and running-related technology (i.e., sports watches and smartphone applications) to address the needs of runners. It investigates the various runner types—in terms of their attitudes, interests, and opinions (AIOs) with regard to running—and studies how they differ in the technology they use. Data used in this study were drawn from the standardized online Eindhoven Running Survey 2016 (ERS2016). In total, 3723 participants completed the questionnaire. Principal component analysis and cluster analysis were used to identify the different running types, and crosstabs obtained insights into the use of technology between different typologies. Based on the AIOs, four distinct runner types were identified: casual individual, social competitive, individual competitive, and devoted runners. Subsequently, we related the types to their use of sports watches and apps. Our results show a difference in the kinds of technology used by different runner types. Differentiation between types of runners can be useful for health professionals, policymakers involved in public health, engineers, and trainers or coaches to adapt their services to specific segments, in order to make use of the full potential of running-related systems to support runners to stay active and injury-free and contribute to a healthy lifestyle.</p

Understanding the Shared Experiences of Runners and Spectators in Long-Distance Running Events

attract not just runners but an exponentially increasing number of spectators. Due to the long duration and broad geographic spread of such events, interactions between them are limited to brief moments when runners (R) pass by their supporting spectators (S). Current technology is limited in its potential for supporting interactions and mainly measures and displays basic running information to spectators who passively consume it. In this paper, we conducted qualitative studies for an in-depth understanding of the R&S’ shared experience during LDRE and how technology can enrich this experience. We propose a two-layer DyPECS framework, highlighting the rich dynamics of the R&S multi-faceted running journey and of their micro-encounters. DyPECS is enriched by the findings from our in depth qualitative studies. We finally present design implications for the multifacet co-experience of R&S during LDRE

Técnicas de computación evolutiva aplicadas a la clasificación a partir de monitores de actividad física

Actualmente, varios factores están haciendo que el campo de reconocimiento de actividades humanas cobre una mayor importancia, como por ejemplo, la proliferación de dispositivos “wearables” que permiten monitorizar la actividad física o la tendencia de la población mundial hacia un estilo de vida cada vez más sedentario. Este estilo de vida sedentario propio de la sociedad actual se traduce en insuficiente actividad física y se considera uno de los mayores factores de riesgo para la salud, estando entre los primeros puestos de factores de riesgo de mortalidad a nivel mundial, según la OMS [11]. De esta manera, dentro del ámbito de la salud y el bienestar, gracias al avance en la miniaturización de sensores, que incluso permite su uso incorporados a la ropa de las personas, el reconocimiento automático de actividades se presenta como una solución a problemas de diversa índole, como por ejemplo, prevención de enfermedades, envejecimiento activo, monitorización remota de enfermos, además de un amplio espectro de aplicaciones en el ámbito deportivo. Es por esto que se convierten en dispositivos de monitorización sumamente útiles en otras áreas de investigación, introduciendo el reconocimiento de actividades humanas en la computación ubicua, el entretenimiento, el registro de actividades diarias personales o el seguimiento del rendimiento deportivo o profesional. Con la principal motivación de explorar nuevos frentes de investigación del reconocimiento de actividades, con un enfoque distinto a los planteados hasta ahora, en este trabajo se propone un sistema de reconocimiento automático de actividades que integra un algoritmo evolutivo, para la tarea de clasificación de actividades, y un enjambre de partículas, para la realización de un clustering que mejore el aprendizaje automático. El sistema ha sido evaluado mediante validación cruzada del tipo leave-one-subject-out, para comprobar su rendimiento en situaciones de reconocimiento independiente del sujeto, obteniendo un 52,37% de acierto. También, se ha evaluado el sistema con validación cruzada estándar de 10-folds en cada sujeto, para analizar la capacidad del sistema en casos de clasificación dependiente del sujeto, alcanzando un 98,07% de acierto. Un resultado significativamente más positivo que el primero, que muestra que el sistema puede tender a la personalización del reconocimiento de actividades. Además, se ha llevado a cabo la evaluación del sistema con validación cruzada estándar de 10-folds en el conjunto de todos los sujetos, con un 70,2267% de acierto, abundándose en la conclusión expuesta más arriba, de que el sistema presenta un mejor funcionamiento en situaciones de personalización del reconocimiento de actividades.In the current time, various factors are making the field of activity recognition become more important, such as the proliferation of wearable devices that allow to monitor physical activity or global population’s tendency towards a more sendentary lifestyle. This sedentary lifestyle is turning into insufficient physical activity and is considered one of the factors with a highest risk for health, being among the leading risk factors of mortality, regarding the WHO [11]. This way, within health and wellness field, thanks to the advance in sensor miniaturization, which even allows sensor usage incorporated to people clothes, activity automatic recognition is presented as a solution to very diverse problems, such as diseases prevention, active aging, patient remote monitoring, as well as a wide range of applications in sports. For that reason, wearable sensors happen to be extremely useful monitorizing devices in other research areas, introducing human activity recognition to ubiquitous computing, entertainment industry, daily life activities logging and sportive and professional perfomance monitoring, among others. With the main motivation of exploring new research horizons, through a different approach to the previous works, in this project, an activity automatic recognition system that integrates an evolutionary algorithm, for the activity classification task, and a particle swarm, for a clustering that improves the automatic learning, is proposed. The system has been evaluated with leave-one-subject-out (LOSO) cross validation, in order to assess its performance in situations where the recognition is subject independent, obtaining an accuracy rate of 52,37%. Also, the system has been evaluated with 10-fold standard cross validation within each subject, to analyze the system’s capacity in subject dependent classification cases, reaching an accuracy rate of 98,07%. A significantly more positive result than the first one, that shows the system might tend to personalization of activity recognition. In addition, the system evaluation has been carried with 10-fold standard cross validation within the whole set of all the subjects, getting an accuracy rate of 70,2267%, which supports the conclusion presented above that the system works better in situations of personalization of the activity recognition.Grado en Ingeniería Informátic

Can Smartphones Help with Running Technique?

AbstractRunning is one of the most popular sports for the masses. However, not every runner might run properly. Incorrect running technique decreases movement efficiency and increases the risk of injury. In this work, we present the development of a smartphone application to provide feedback on running technique on the example of arm carriage. Recognition algorithms were developed in a preliminary study with 10 participants. Investigating sensor positions and modalities, we found that a single IMU on the upper arm yielded an accuracy of 80.73% for the assessment of arm movement. We implemented our approach as a smartphone application and found that runners improved their arm movement using our application within a user study including 23 participants. Results from questionnaires revealed high user acceptance (average rating of 8 from 10 possible points)