Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources

Abstract

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer's daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.

Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

“…Wearables are major means of personalized health monitoring for proactive healthcare [ 90 , 91 , 92 ]. The state of the art is concentrated on WBAN consisting several wearables interacting and transmitting data through the mesh/star topology to a gateway and eventually to a cloud/server for data fusion, analysis, and decision-making [ 93 , 94 ].…”

Hurdles to Developing and Scaling Remote Patients’ Health Management Tools & Systems: A Scoping Review

“…Targeted diseases ranged from PTSD, mental health, Parkinson disease, COPD to IBD and malaria. The remaining 8 articles [22], [15], [3], [38], [43], [44], [45] and [60] tended to cover a combination of diseases but only three [38], [44] and [60] were speci c about the combination of diseases or parameters they sought to monitor or measure (HIV/AID and TB, and parameters related to CVD or COPD).…”

“…Eight articles did not provide details related to accessibility. As depicted in Figure 8, the majority of articles [22], [58], [3], [36], [38], [39], [41], [42], [44], [46], [55], [56], [57], [60], [61], [62], [66], [67] and [68] gave various reasons why accessibility of discussed interventions was limited including scalability [3], [36] health apps and smart phones' credibility for continuous data ow, feasibility, portability, and power consumption [58], [39], [44] and [66], limited or lack of training [42], limited connectivity and internet requirement of systems [41], and failure to take into account natural variations in patient physiology or behaviour [62]. Other mentioned factors were similar to those covered by systematic reviews.…”

“…From our scoping review of the landscape of RPMTS's design, development and deployment, it appears that the majority of RPMTS interventions deployed in the pre-clinical setting for preventive purposes and known as 'self-diagnosing apps or symptom checkers' have not been found to be integrated into traditional clinical work ows. These interventions [18], [30], [34], [37], [42], [43], [44] and [45] are mainly deployed to monitor patients' physical activities (PA) to address lifestyle related diseases such as cardiovascular diseases (CVD), high blood pressure and diabetes in the context of the new 'quanti ed self' movement.…”

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

“…The application of wearable technology is determined by the target design. Some families of wearables have been designed for monitoring, prediction, and prevention of the user [13]. Some target the management of users and diseases [14], and others facilitates and influences decision-making [15].…”