Advanced active pixel architectures in standard CMOS technology

This paper aims at exploring and validating the adoption of standard fabrication processes for the realization of CMOS active pixel sensors, for particle detection purposes. The goal is to implement a single-chip, complete radiation sensor system, including on a CMOS integrated circuit the sensitive devices, read-out and signal processing circuits. A prototype chip (RAPS01) based on these principles has been already fabricated, and a chip characterization has been carried out; in particular, the evaluation of the sensitivity of the sensor response on the actual operating conditions was estimated, as well as the response uniformity. Optimization and tailoring of the sensor structures for High Energy Physics applications are being evaluated in the design of the next generation chip (RAPS02). Basic features of the new chip includes digitally configurable readout and multi-mode access (i.e., either sparse of line-scan readout). © 2005 IEEE

Cloud-Based Behavioral Monitoring in Smart Homes

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques

Hardware-oriented adaptation of a Particle Swarm Optimization algorithm forobject detection

In 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

A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

High-Accuracy, Unsupervised Annotation of Seismocardiogram Traces for Heart Rate Monitoring

This 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

Fully Automated Annotation of Seismocardiogram for Noninvasive Vital Sign Measurements

This paper presents a fully automated procedure for acquiring and analyzing seismocardiographic (SCG) traces from an Inertial Measurement Unit (IMU) placed over a subject’s sternum. An automated calibration procedure allows for straightforward adaption to different subjects. Calibration is performed once per subject, exploiting ECG (electrocardiogram) markers; relevant patterns and parameters are automatically extracted and used for successive SCG processing, which does not require concurrent ECG information any longer. Annotation of SCG traces is performed in two steps: in the first one, a suitably engineered signal is derived from SCG and used as coarse heartbeat detector; then, annotation can be performed by comparing the prototype extracted at calibration time with segments of SCG data, near to the detected beats. The proposed methodology is validated by direct comparison with ECG, adopted as gold-standard. In particular, three main metrics are taken into account: sensitivity (i.e. percentage of correctly identified heartbeats, compared to ECG), precision (i.e. impact of false positives on truly detected beats) and R2 (i.e. linearity between beat-to-beat measurements as computed by ECG and SCG). Results show satisfactory performance, more than adequate to continuous, long-term monitoring: overall, approximately 90% of heartbeats are correctly detected, on average, with minimal false positives (≈1%). Linearity between ECG and SCG-computed beat-to-beat intervals is extremely high (R2 > 0.95, on average), indicating good agreement between the two measurement methods. These results suggest SCG can be used as a reliable, contactless measure of heart-related parameters

Using small checkerboards as size reference: A model-based approach

Monitoring diet is crucial for preventing or dealing with many chronic diseases. Therefore, plenty of different methods have been developed to serve this purpose. Among these, automatic diet monitoring based on mobile devices are of particular interest. An automatic system is supposed to be able to detect type and amount of food intake. This work suggests using a small checkerboard in food images as size reference as an aid for estimating food amount. Although checkerboard is a simple pattern, most of the off-the-shelf algorithms do not perform well in detecting small checkerboards. This paper extends a previous work presenting a new stochastic model-based algorithm to detect small checkerboards. The algorithm first locates the checkerboard in the food image and then applies a customized corner detection algorithm to the located region. Experimental results show notably better performance in comparison to basic methods and to the previous version of the method