A mobile system for sedentary behaviors classification based on accelerometer and location data - Ceron Bravo, Jesus David; et al. (Computers in Industry, 2017)
Background: Sedentary behaviors are associated to the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Accelerometers and inclinometers have been used to estimate sedentary behaviors, however a major limitation is that these devices do not provide enough contextual information in order to recognize the specific sedentary behavior performed, e.g., sitting or lying watching TV, using the PC, sitting at work, driving, etc.Objective: Propose and evaluate the precision of a mobile system for objectively measuring six sedentary behaviors using accelerometer and location data.Results: The system is implemented as an Android Mobile App, which identifies individual's sedentary behaviors based on accelerometer data taken from the smartphone or a smartwatch, and symbolic location data obtained from Bluetooth Low Energy (BLE) beacons. The system infers sedentary behaviors by means of a supervised Machine Learning Classifier. The precision of the classification of five of the six studied sedentary behaviors exceeded 95% using accelerometer data from a smartwatch attached to the wrist and 98% using accelerometer data from a smartphone put into the pocket. Statistically significant improvement in the average precision of the classification due to the use of BLE beacons was found by comparing the precision of the classification using accelerometer data only, and BLE beacons localization technology.Conclusions: The proposed system provides contextual information of specific sedentary behaviors by inferring with very high precision the physical location where the sedentary event occurs. Moreover, it was found that, when accelerometers are put in the user's pocket, instead of the wrist and, when symbolic location is inferred using BLE beacons; the precision in the classification is improved. In practice, the proposed system has the potential to contribute to the understanding of the context and determinants of sedentary behaviors, necessary for the implementation and monitoring of personalized noncommunicable diseases prevention programs, for instance, sending sedentary behavior alerts, or providing personalized recommendations on physical activity. The system could be used at work to promote active breaks and healthy habits. (C) 2017 Elsevier B.V. All rights reserved.
Keywords: Sedentary behaviors classification; Data mining; Sensor mining; Accelerometer; BLE beacons

A Personal Health Record System for Diabetes Care Conforming to the ISO 16527 Interoperability Requirements. - Ceron Bravo, Jesus David; et al. (2014)
A Personal Health Record (PHR) is a health information repository controlled and managed directly by a patient or his/her custodian, or a person interested in his/her own health. PHR System's adoption and compliance with international standards is foremost important because it can help to meet international, national, regional or institutional interoperability and portability policies. In this paper, an interoperable PHR System for supporting the control of type 2 diabetes mellitus is proposed, which meets the mandatory interoperability requirements proposed in the Personal Health Record System Functional Model standard (ISO 16527). After performing a detailed analysis of different applications and platforms for the implementation of electronic Personal Health Records, the adaptation of the Indivo Health open source platform was completed. Interoperability functions were added to this platform by integrating the Mirth Connect platform. The assessment of the platform's interoperability capabilities was carried out by a group of experts, who verified the interoperability requirements proposed in the ISO 16527 standard.

A Two-Layer Method for Sedentary Behaviors Classification Using Smartphone and Bluetooth Beacons. - Ceron Bravo, Jesus David; et al. (2017)
Among the factors that outline the health of populations, person's lifestyle is the more important one. This work focuses on the caracterization and prevention of sedentary lifestyles. A sedentary behavior is defined as "any waking behavior characterized by an energy expenditure of 1.5 METs (Metabolic Equivalent) or less while in a sitting or reclining posture". To propose a method for sedentary behaviors classification using a smartphone and Bluetooth beacons considering different types of classification models: personal, hybrid or impersonal. Following the CRISP-DM methodology, a method based on a two-layer approach for the classification of sedentary behaviors is proposed. Using data collected from a smartphones' accelerometer, gyroscope and barometer; the first layer classifies between performing a sedentary behavior and not. The second layer of the method classifies the specific sedentary activity performed using only the smartphone's accelerometer and barometer data, but adding indoor location data, using Bluetooth Low Energy (BLE) beacons. To improve the precision of the classification, both layers implemented the Random Forest algorithm and the personal model. This study presents the first available method for the automatic classification of specific sedentary behaviors. The layered classification approach has the potential to improve processing, memory and energy consumption of mobile devices and wearables used. BACKGROUND OBJECTIVE RESULTS CONCLUSIONS

A vital signs telemonitoring system - interoperability supported by a personal health record systema and a cloud service. - Ceron Bravo, Jesus David; et al. (Studies in health technology and informatics, 2014)
This article presents the development process of an acquisition and data storage system managing clinical variables through a cloud storage service and a Personal Health Record (PHR) System. First, the paper explains how a Wireless Body Area Network (WBAN) that captures data from two sensors corresponding to arterial pressure and heart rate is designed. Second, this paper illustrates how data collected by the WBAN are transmitted to a cloud storage service. It is worth mentioning that this cloud service allows the data to be stored in a persistent way on an online database system. Finally, the paper describes, how the data stored in the cloud service are sent to the Indivo PHR System, where they are registered and charted for future revision by health professionals. The research demonstrated the feasibility of implementing WBAN networks for the acquisition of clinical data, and particularly for the use of Web technologies and standards to provide interoperability with PHR Systems at technical and syntactic levels.

Design and implementation of a personal health record system for diabetes mellitus type 2 monitoring. - Ceron Bravo, Jesus David; et al. (2014)
The objective of this paper is to describe the design and implementation of a Personal Health Record System (PHR-S) for supporting monitoring of blood glucose in diabetes mellitus type 2 patients. The paper describes a survey applied in order to elicit the specific ICT needs of Diabetes Mellitus Type 2 patients. Based on the requirements, a web application "GlucoseDataManager" was implanted and integrated into a local deployed IndivoHealth PHR-S platform.

Human activity recognition supported on indoor localization: A systematic review - Ceron Bravo, Jesus David; et al. (15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018, 2018)
Keywords: Simultaneous Human Activity Recognition and Indoor Localization; Indoor Localization; Human Activity Recognition

Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach - Ceron, Jesus David; López, Diego M.; Eskofier, Björn (Proceedings, 2018)

Human-centered design of a personal health record system for metabolic syndrome management based on the ISO 9241-210:2010 standard. - Ceron Bravo, Jesus David; et al. (Journal of Multidisciplinary Healthcare, 2018)
Previous studies have demonstrated the effectiveness of information and communication technologies to support healthy lifestyle interventions. In particular, personal health record systems (PHR-Ss) empower self-care, essential to support lifestyle changes. Approaches such as the user-centered design (UCD), which is already a standard within the software industry (ISO 9241-210:2010), provide specifications and guidelines to guarantee user acceptance and quality of eHealth systems. However, no single PHR-S for metabolic syndrome (MS) developed following the recommendations of the ISO 9241-210:2010 specification has been found in the literature. The aim of this study was to describe the development of a PHR-S for the management of MS according to the principles and recommendations of the ISO 9241-210 standard. The proposed PHR-S was developed using a formal software development process which, in addition to the traditional activities of any software process, included the principles and recommendations of the ISO 9241-210 standard. To gather user information, a survey sample of 1,187 individuals, eight interviews, and a focus group with seven people were performed. Throughout five iterations, three prototypes were built. Potential users of each system evaluated each prototype. The quality attributes of efficiency, effectiveness, and user satisfaction were assessed using metrics defined in the ISO/IEC 25022 standard. The following results were obtained: 1) a technology profile from 1,187 individuals at risk for MS from the city of Popayan, Colombia, identifying that 75.2% of the people use the Internet and 51% had a smartphone; 2) a PHR-S to manage MS developed (the PHR-S has the following five main functionalities: record the five MS risk factors, share these measures with health care professionals, and three educational modules on nutrition, stress management, and a physical activity); and 3) usability tests on each prototype obtaining the following results: 100% effectiveness, 100% efficiency, and 84.2 points in the system usability scale. The software development methodology used was based on the ISO 9241-210 standard, which allowed the development team to maintain a focus on user's needs and requirements throughout the project, which resulted in an increased satisfaction and acceptance of the system. Additionally, the establishment of a multidisciplinary team allowed the application of considerations not only from the disciplines of software engineering and health sciences but also from other disciplines such as graphical design and media communication. Finally, usability testing allowed the observation of flaws in the designs, which helped to improve the solution. Background Objective Methods Results Conclusion

Human-Centered Development of an Online Social Network for Metabolic Syndrome Management. - Ceron Bravo, Jesus David; et al. (2016)
According to the International Diabetes Federation (IDF), a quarter of the world's population has Metabolic Syndrome (MS). To develop (and assess the users' degree of satisfaction of) an online social network for patients who suffer from Metabolic Syndrome, based on the recommendations and requirements of the Human-Centered Design. Following the recommendations of the ISO 9241-210 for Human-Centered Design (HCD), an online social network was designed to promote physical activity and healthy nutrition. In order to guarantee the active participation of the users during the development of the social network, a survey, an in-depth interview, a focal group, and usability tests were carried out with people suffering from MS. The study demonstrated how the different activities, recommendations, and requirements of the ISO 9241-210 are integrated into a traditional software development process. Early usability tests demonstrated that the user's acceptance and the effectiveness and efficiency of the social network are satisfactory. PROBLEM OBJECTIVE RESULTS CONCLUSIONS

Indoor Trajectory Reconstruction of Walking, Jogging, and Running Activities Based on a Foot-Mounted Inertial Pedestrian Dead-Reckoning System - Ceron, Jesus David; Martindale, Christine; López, Diego M.; Kluge, Felix; Eskofier, Björn (Sensors, 2020)
The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standin

Information and communication technology-based strategies for the reduction of cardiovascular risk factors in the active working population Estrategias basadas en tecnologías de la información y la comunicación para la reducción de factores de riesgo cardiovascular en personas laboralmente activas - Ceron Bravo, Jesus David; et al. (Revista Colombiana de Cardiología, 2018)
Keywords: Diabetes mellitus; Cardiovascular risk factors; Metabolic syndrome

Open Dataset for the Automatic Recognition of Sedentary Behaviors. - Ceron Bravo, Jesus David; et al. (2017)
Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification. BACKGROUND OBJECTIVE RESULTS CONCLUSIONS

Towards a Personal Health Record System for the Assesment and Monitoring of Sedentary Behavior in Indoor Locations. - Ceron Bravo, Jesus David; et al. (2016)
Sedentary behavior has been associated to the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Accelerometers and inclinometers have been used to estimate sedentary behaviors, however a major limitation is that these devices do not provide contextual information such as the activity performed, e.g., TV viewing, sitting at work, driving, etc. The main objective of the thesis is to propose and evaluate a Personal Health Record System to support the assessment and monitoring of sedentary behaviors. Until now, we have implemented a system, which identifies individual's sedentary behaviors and location based on accelerometer data obtained from a smartwatch, and symbolic location data obtained from Bluetooth beacons. The system infers sedentary behaviors by means of a supervised Machine Learning Classifier. The precision in the classification of the six studied sedentary behaviors exceeded 90%, being the Random Forest algorithm the most precise. The proposed system allows the recognition of specific sedentary behaviors and their location with very high precision. BACKGROUND OBJECTIVE RESULTS CONCLUSION