Abstract
This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.
Highlights
Recent survey shows that non-medical grade wearable sensors provide heart rate measurement accuracy close to medical grade devices while being considerably cheaper [2]
Web services are used both by mobile and web client to communicate with server to process sensor data of patient
System implementation required the development of a dedicated domain system model that includes medical profile data, patient scheduling, geolocation and both inertial and biomedical sensor data sets
Summary
Utilisation of biomedical sensor data for diagnosis and remote care requires overcoming many challenges regarding data acquisition, analysis and visualisation [1]. Such data is gathered with usage of certified medial sensors. Wearable sensors design and it’s utilization for monitoring has become a very popular research direction [3]. Some of the system designs include using mobile for data acquisition and its further processing on server [4]. There are significantly less systems where data could be collected from heterogeneous sources and processed especially where an enterprise ready architecture would be in scope of research
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