This section presents all the developed applications and tools supported by Mining Minds, an innovative digital cloud platform for personalized health and wellness care.
This video shows the complete operation flow of the Mining Minds platform and its constituent layers.
This demo presents the physical activity promotion services and tools supported by the new version of Mining Minds. First, it presents an end-user app devoted to changing sedentary conducts, promoting healthy habits and educating on risky behaviors. Second, it describes an advanced expert inspection tool that provides diverse sort of analytics and information devised to facilitate the assessment of users' progresses. Finally, it shows IKAT, an Intelligent Knowledge Authoring Tool that allows experts to create and maintain the knowledge used by Mining Minds for the generation of both healthy recommendations and educational facts.
This application is particularly intended to support personalized active lifestyle recommendations, as well as track of behavioral patterns and analysis of user weight trends. This videodemonstration does not only describes the end-user app but also introduces some tools designed for experts and administrators, as well as the supporting cloud environment on which the platform runs.
A description of the Mining Minds platform can be seen in:
Banos, O., Bilal Amin, M., Ali Khan, W., Afzel, M., Hussain, M., Kang, B. H., Lee, S. The Mining Minds Digital Health and Wellness Framework. BioMedical Engineering OnLine, vol. 15, no. 1, pp. 165-186 (2016). [PDF BiBTeX LINK]
This section presents a set of video demos describing some technologies developed for the automatic recognition of human behaviour from multimodal sensors.
This video presents a demonstration of the Low-Level Context Awareness (LLCA), which transforms a wide-spectrum of data obtained from the user interaction with the real and cyber world into abstract concepts or categories, namely physical activities, emotional states and locations.
This video shows a demonstration of the High-Level Context Awareness (HLCA), which intelligently combines and processes the low-level context categories generated via the LLCA in order to determine and track more meaningful semantic representations of the user context.
A complete description of the LLCA and HLCA is available here:
Villalonga, C., Razzaq, M.-A., Ali Khan, W., Pomares, H., Rojas, I., Lee, S., Banos, O. Ontology-based High-Level Context Inference for Human Behavior Identification. Sensors, vol. 16, no. 10, pp. 1-26 (2016). [PDF BiBTeX LINK]
Banos, O., Villalonga, C., Bang, J. H., Hur, T. H., Kang, D., Park., S.-B., Hyunh-The, T., Vui, L. B., Amin, M.-B., Razzaq, M.-A., Ali Khan, W., Hong, C.-S., Lee, S. Human Behavior Analysis by means of Multimodal Context Mining. Sensors, vol. 16, no. 8, pp. 1-19 (2016). [PDF BiBTeX LINK]
The mHealthApp is an app that showcases the potential of mHealhDroid. The application implements mechanisms for the processing and visualization of health data, persistent remote storage and personalized health and wellbeing guidelines. The health data is collected through external wearable sensors (concretely, Shimmer© sensors), which are simply interfaced to the regular mobile device. The application also provides a means to detect and track human behavior on-the-fly, a functionality that builds in our prior work and experience in the activity recognition field.
A full description of the app characteristics and use can be seen in:
Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado, J. A., Lee, S., Pomares, H., Rojas, I. Design, implementation and validation of a novel open framework for agile development of mobile health applications. BioMedical Engineering OnLine, vol. 14, no. S2:S6, pp. 1-20 (2015). [PDF BiBTeX LINK]
The PhysioDroid app is devised to combine wearable health sensors and mobile devices for a pervasive, continuous and personal monitoring. PhysioDroid is conceived to empower users in their daily living as well as to make them conscious and participatory of their healthcare and well-being through the access to a simplified description of their health status. The PhysioDroid also provides track and alerts on conditions, as well as mechanisms to trigger emergency procedures at the point of need. Besides, the PhysioDroid system not only applies to individual users but is devised to leverage multiple users' health and behavioral data, which is seen to be a key accelerator of medical and social knowledge. All these contributions may potentially lead to more efficient medical diagnoses, treatments and proactive policies.
A full description of the app characteristics and use can be seen in:
Banos, O., Villalonga C., Damas, M., Glösekötter, P., Pomares, H., Rojas, I. PhysioDroid: combining wearable health sensors and smartphones for a ubiquitous, continuous and personal monitoring. The Scientific World Journal, vol. 2014, no. 490824, pp. 1-11 (2014). [PDF BiBTeX LINK]