The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.

Fall detection and prevention require logged physiological activity data of a patient for a long period of time. This work develops a data acquisition system to collect motion data from multiple patients and store in a data base. A wireless sensor network is built using high precision inertia sensors and low power Zigbee wireless transceivers. Testing results prove the system function properly. Researchers and physicians can now retrieve and analyze the accurate data of the patient movement with ease.

As digital microfluidics-based biochips find more applications, their complexity is expected to increase significantly owing to the trend of multiple and concurrent assays on the chip. There is a pressing need to deliver a top-down design methodology that the biochip designer can leverage the same level of computer-aided design support as the semi-conductor industry now does. Moreover, as microelectronics fabrication technology is scaling up and integrated device performance is improving, it is expected that these microfluidic biochips will be integrated with microelectronic components in next-generation system-on-chip designs. This study presents the analysis and experiments of digital microfluidic operations on a novel electrowetting-on-dielectric-based 'micro-electrode dot array architecture' that fosters a development path for hierarchical top-down design approach for digital microfluidics. The proposed architecture allows dynamic configurations and activations of identical basic microfluidic unit called 'micro-electrode cells' to design microfluidic components, layouts, routing, microfluidic operations and applications of the biochip hierarchically. Fundamental microfluidic operations have been successfully performed by the architecture. In addition, this novel architecture demonstrates a number of advantages and flexibilities over the conventional digital microfluidics in performing advanced microfluidic operations.

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