Abstract
Falling is a very serious problem for our society, as it affects one out of three older adults. Currently, this is a well-known problem and therefore multiple ICT-based solutions for falls management exist. In addition, a small part of them are said to help preventing falls, but most of the reviewed solutions doesn't seem to have their focus in reducing specific fall risk factors like loss of muscle mass or a poor balance. The proposed ICT-based fall prevention solution is based on an existing fall prevention exercise programme specifically designed for the Portuguese population. It takes advantage of the smartphone processing capabilities as well as its built-in inertial sensors to evaluate the movements performed during the execution of specific exercises. Using only a simple smartphone it is possible to provide a friendly and inexpensive solution capable of increasing seniors' adherence to fall prevention exercises as well as raise their motivation to properly execute the exercises in their home environment. © 2013 IEEE.

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This paper presents an extension to a hardware/software system architecture in which repetitive instruction traces, called Megablocks, are accelerated by a Reconfigurable Processing Unit (RPU). This scheme is supported by a custom toolchain able to automatically generate a RPU tailored for the execution of one or more Megablocks detected offline. Switching between hardware and software execution is done transparently, without modifications to source code or executable binaries. Our approach has been evaluated using an architecture with a MicroBlaze General Purpose Processor (GPP) softcore. By using a memory sharing mechanism, the RPU can access the GPP's data memory, allowing the acceleration of Megablocks with load/store operations. For a set of 21 embedded benchmarks, an average speedup of 1.43x is achieved, and a potential speedup of 2.09x is predicted for an implementation using a low overhead interface for communication between GPP and RPU.

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