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This paper presents a low cost, noninvasive, clinical-grade Pulse Wave Velocity evaluation device. The proposed system relies on a simultaneous acquisition of femoral and carotid pulse waves to improve estimation accuracy and correctness. The sensors used are two high precision MEMS force sensors, encapsulated in two ergonomic probes, and connected to the main unit. Data are then wirelessly transmitted to a standard laptop, where a dedicated graphical user interface (GUI) runs for analysis and recording. Besides the interface, the Athos system provides a Matlab algorithm to process the signals quickly and achieve a reliable PWV assessment. To better compare the results at the end of each analysis, a detailed report is generated, including all the relevant examination information (subject data, mean PTT, and obtained PWV). A pre-clinical study was conducted to validate the system by realizing several Pulse Wave Velocity measurements on ten heterogeneous healthy subjects of different ages. The collected results were then compared with those measured by a well-established and largely more expensive clinical device (SphygmoCor).

Classification of human activity is an increasingly popular topic, as it is employed in various fields from fitness to remote health monitoring. Current automated approaches based on wearable sensors typically use supervised learning methodologies, where a classifier is trained with experimental data. This paper proposes the use of body motion and sensor simulation for building, or extending, the training databases and improve the classifier accuracy, without requiring further experimental campaigns.