1,721,037 research outputs found
An Embedded Video Sensor for a Smart Traffic Light
No full textIn this article, a motion sensor, based on a video embedded system, is presented. This sensor can detect if a person is on a crosswalk near a traffic light: if this event occurs while pedestrians have the stop sign, then a warning alarm to incoming drivers can be generated. The system innovation consists of the fact that no PC for video image processing is needed, but the motion sensor consists of a smart camera with embedded processing. This is obtained using an embedded system based on a high density FPGA programmable logic, which contains a soft-microprocessor-IP core and specific circuits dedicated to the execution of a Particle Swarm Optimization (PSO) algorithm. The proposed framework is such that the phases of design, simulation, implementation, prototyping and debugging are closely related in a tested design flow that uses the most modern methodologies and efficient prototyping tools
High-Accuracy, Unsupervised Annotation of Seismocardiogram Traces for Heart Rate Monitoring
Full text linkThis article presents an unsupervised, automated procedure for the analysis of SeismoCardioGram (SCG) signals. SCG is a measure of chest vibrations, induced by the mechanical activity of the heart, that allows to extract relevant parameters, including Heart Rate (HR) and HR Variability (HRV). An initial self-calibration is performed, solely based on SCG traces, yielding a suitable heartbeat template (personalized for each subject). Then, beat detection and timing annotation are performed in two steps: at first, candidate beats are identified and validated, by means of suitably defined detection signals; then, precise timing annotation is achieved by best aligning such candidate beats to the previously extracted template. The algorithm has been validated on two separate datasets, featuring different acquisition setups: the first one is the publicly available CEBS database, reporting SCG signals from subjects lying in supine position, whereas the second one was acquired using a custom setup, involving sitting subjects. Results show good sensitivity and precision scores (98.5%, 98.6% for the CEBS database, and 99.1%, 97.9% for the Custom one, respectively). Also, comparison with ECG gold-standard is given, showing good agreement between beat-to-beat intervals computed from SCG and the ECG gold-standard: on average, R2 scores of 99.3% and 98.4% are achieved on CEBS and Custom datasets, respectively. Furthermore, a low RMS Error is achieved on the CEBS and Custom dataset, amounting to 4.6 ms and 6.2 ms, respectively (i.e. 2.3 Ts and 3.1 Ts, where Ts is the sampling period): such results well compare to related literature. Validation on two different datasets indicates the robustness of the proposed methodology
Accurate Heartbeat Detection on Ballistocardiogram Accelerometric Traces
Full text linkThis paper presents an automated procedure for acquisition and analysis of BallistoCardioGraphy (BCG) traces. A tri-axial accelerometer and a microcontroller unit are used to record heart-induced recoil forces generated from a lying subject. The problem of BCG J-peak annotation is split into two sub-tasks: candidates extraction, based on a detection signal, and actual annotation, guided by subject-specific search windows. Such procedure is derived from an automatic calibration, which is carried out with no need of concurrent ElectroCardioGram (ECG) or user intervention. The algorithm also implements post-annotation checks for refinement of annotation, which effectively reduces the number of missed J-peaks. The impact of each algorithm phase is analyzed, assessing statistical significance of each step; finally, performance is optimized in a data-driven fashion. Results show that the proposed methodology is able to achieve high sensitivity and precision (the median score is 98.9% and 98.1%, respectively) in J-peak detection. The quality of J-peaks timing annotation is further demonstrated by a very low discrepancy between BCG and ECG HR estimates. Over all population, the standard deviation of such error was found to be approximately 6.56 ms, whereas the Mean Absolute Error just 4.7 ms (i.e. ≈1.18;Ts, where Ts = 4 ms is the sampling period). Such scores, indeed, improve over recent related literature
An accurate and stable bed-based ballistocardiogram measurement and analysis system
No full textAn experimental BallistoCardioGram (BCG) measurement and analysis system is presented, featuring accelerometers placed between the mattress and bed slats. The system can sample BCG waveforms at 500 Hz and can perform unsupervised BCG heartbeat detection and heartbeat interval measurements. The approach is validated on an experimental dataset consisting in 14 subjects recorded while lying in three different positions: supine, left side and right side. The overall performance is good, comparing to other works in literature. In fact, the average sensitivity and precision across subjects and positions is 98.2% and 98.0%, respectively; similarly, an R2 score of 98.2% was achieved between BCG and reference ECG measurements, while Mean Absolute Error and Root Mean Squared Error are as low as 3.9 ms and 5.6 ms. The presented methodology is shown to be resilient to different sleeping positions, as confirmed by Kruskal-Wallis statistical tests (p≈0.91 for sensitivity, p≈0.73 for precision, p≈0.81 for R2, p≈0.26 for MAE, p≈0.20 for RMSE). Moreover, results are in line and comparable to those already achieved on a different measurement scenario featuring a different bed structure. This further proves the stability of the presented BCG measurement and analysis system
Hardware-oriented adaptation of a Particle Swarm Optimization algorithm forobject detection
No full textIn this paper we propose a simplified, hardware-oriented
algorithm for object detection, based on Particle Swarm
Optimization. Starting from an algorithm coded in a highlevel
language which has shown to perform well, both in
terms of accuracy and of computation efficiency, the simplified
version can be implemented on an FPGA. After describing
the original algorithm, we describe how it has been
simplified for hardware implementation. We show how the
intrinsic modularity of the algorithm permits to define a
general core, independent of the specific application, which
implements object search, along with a simple applicationspecific
module, which implements a problem-dependent fitness
function. This makes the system easily reconfigurable
when switching between different object detection applications.
Finally, we show some examples of application of our
algorithm and discuss about possible future developments
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