Data Processing
From Capsil Wiki
The use of technology to aid independent living has the side effect of generating potentially vast amounts of data. How that data is managed, processed, and applied is crucial to the successful deployment of this technology. In terms of data processing there are many aspects that need to be considered such as the best approach to process the data, i.e., on board data processing verses streaming to a centralised processing node. This includes identifying optimal solutions in terms of CPU overhead, battery life performance, processing time, etc. In addition, data mining techniques need to be developed to allow the identification of events that require monitoring or intervention. Recommendations will be developed which outlined data formats which should be adopted in the development of any BSN application. The following sections look at the different aspects of data processing such as the use of on-node processing, the fusion of data, the use of context for autonomic sensing, and data mining techniques and trend analysis.
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On-node Data Processing
This refers to algorithms that can be implemented directly on a sensor node, allowing the optimisation of resources in terms of energy and communications. These techniques are also essential if abnormal events were to be detected on the sensor node, and alerts were to be generated at that level.
Sensor Fusion
The use of multiple sensors with information fusion has the several main advantages compared to single sensor systems. These include improved signal to noise ratios, enhanced robustness and reliability in the event of sensor failure, integration of independent features and prior knowledge, reducing uncertainty and improved resolution, precision, confidence and hypothesis discrimination.
Context Aware and Autonomic Sensing
The contextual information is mainly focused on the user's activity, physiological status and the surrounding physical environment. Understanding the context in which the user performs his/her activities is essential in comprehending the activities themselves and their relationship to prior and future activities or events, as well as environmental changes. Context aware sensing and autonomic sensing are linked; the latter referring to networks that can autonomically configure, optimise, manage, heal, protect, adapt, scale and integrate.
Data Mining and Trend Analysis
With large amounts of data obtained, efficient data-mining is essential to allow important patterns to be recognised, errors in the data highlighted and trends to be noted. This section will cover approaches that have been successfully used to provide pattern recognition in Body Sensor Networks.
Falls Detection Algorithms
A variety of data analysis techniques incorporating value algorithms have been applied to the falls detection domain. Many of the approaches focus on improving the selectivity of falls. The issue of false positives is the most significant issues limiting the reliability of body worn falls detectors. Many efforts have focused on improving the classification base solely on the sensor by using a variety of mathematical techniques such as thresholding using support data with sensor data, typically accelerometer based, to achieve greater selectivity. Alternatively, supporting data sources have been utilized to improve selectivity such as the inclusion of sound or/and visual sensors to improve the accuracy of the falls detection.
