Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Design of analog front-ends for the RD53 demonstrator chip

The RD53 collaboration is developing a large scale pixel front-end chip, which will be a tool to evaluate the performance of 65 nm CMOS technology in view of its application to the readout of the innermost detector layers of ATLAS and CMS at the HL-LHC. Experimental results of the characterization of small prototypes will be discussed in the frame of the design work that is currently leading to the development of the large scale demonstrator chip RD53A to be submitted in early 2017. The paper is focused on the analog processors developed in the framework of the RD53 collaboration, including three time over threshold front-ends, designed by INFN Torino and Pavia, University of Bergamo and LBNL and a zero dead time front-end based on flash ADC designed by a joint collaboration between the Fermilab and INFN. The paper will also discuss the radiation tolerance features of the front-end channels, which were exposed to up to 800 Mrad of total ionizing dose to reproduce the system operation in the actual experiment

Classification of Two Volatiles Using an eNose Composed by an Array of 16 Single-Type Miniature Micro-Machined Metal-Oxide Gas Sensors

The artificial replication of an olfactory system is currently an open problem. The development of a portable and low-cost artificial olfactory system, also called electronic nose or eNose, is usually based on the use of an array of different gas sensors types, sensitive to different target gases. Low-cost Metal-Oxide semiconductor (MOX) gas sensors are widely used in such arrays. MOX sensors are based on a thin layer of silicon oxide with embedded heaters that can operate at different temperature set points, which usually have the disadvantages of different volatile sensitivity in each individual sensor unit and also different crossed sensitivity to different volatiles (unspecificity). This paper presents and eNose composed by an array of 16 low-cost BME680 digital miniature sensors embedding a miniature MOX gas sensor proposed to unspecifically evaluate air quality. In this paper, the inherent variability and unspecificity that must be expected from the 16 embedded MOX gas sensors, combined with signal processing, are exploited to classify two target volatiles: ethanol and acetone. The proposed eNose reads the resistance of the sensing layer of the 16 embedded MOX gas sensors, applies PCA for dimensional reduction and k-NN for classification. The validation results have shown an instantaneous classification success higher than 94% two days after the calibration and higher than 70% two weeks after, so the majority classification of a sequence of measures has been always successful in laboratory conditions. These first validation results and the low-power consumption of the eNose (0.9 W) enables its future improvement and its use in portable and battery-operated applications

A priority-based energy efficient multi-hop routing protocol with congestion control for wireless body area network

Wireless Body Area Networks (WBANs) are advanced and integrated monitoring networks for healthcare applications. In these networks, different types of Biomedical Sensor Nodes (BSNs) are used to monitor physiological parameters of the human body. The BSNs have limited resources such as energy, memory and computation power. These limited resources make the network challenging especially in terms of energy consumption. Efficient routing schemes are required to save the energy during communication processes. Additionally, the BSNs generate sensitive and non-sensitive data packets, which need to be routed according to their priority. In order to address these problems, a priority-based Energy Efficient Multihop Routing protocol with congestion control (3EMR) for wireless body area network was developed that comprises of three different schemes. First, an Optimal Next-hop Selection (ONS) scheme was developed based on the cost function of routing parameters to dynamically select best next-hop for forwarding data packets. Second, a Priority Based Routing (PBR) scheme was developed to forward data packets according to data priority, which is based on sensitivity of the data with regards to patience’s life. Third, a Congestion Avoidance and Mitigation (CAM) scheme was developed to save energy consumption and packet loss due to congestion by considering packet flow adjustment and congestion zone avoidance based strategy. It improvement is benchmarked against related solutions, and they are Healthcare-aware Optimized Congestion Avoidance (HOCA), Differentiated Rate control for Congestion (DRC), Priority based Cross Layer Routing (PCLR), Even Energy-consumption and Backside Routing (EEBR), and Energy Efficient Routing (EER) scheme. The simulation results demonstrated that the 3EMR scheme achieved significant improvement in terms of increased network lifetime by 31.4%, increased throughput by 33.2%, reduced packet loss 30.9%, increased packet delivery ratio by 21.1% and reduced energy consumption 26.8%. Thus, the proposed routing scheme has proven to be an energy efficient solution for data communication in wireless body area networks

Computational Commensality: from theories to computational models for social food preparation and consumption in HCI

Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective

Energy Neutral Design of Embedded Systems for Resource Constrained Monitoring Applications

Automatic monitoring of environments, resouces and human processes are crucial and foundamental tasks to improve people's quality of life and to safeguard the natural environment. Today, new technologies give us the possibility to shape a greener and safer future. The more specialized is the kind of monitoring we want to achieve, more tight are the constraints in terms of reliability, low energy and maintenance-free autonomy. The challenge in case of tight energy constraints is to find new techniques to save as much power as possible or to retrieve it from the very same environment where the system operates, towards the realization of energy neutral embedded monitoring systems. Energy efficiency and battery autonomy of such devices are still the major problem impacting reliability and penetration of such systems in risk-related activities of our daily life. Energy management must not be optimized to the detriment of the quality of monitoring and sensors can not be operated without supply. In this thesis, I present different embedded system designs to bridge this gap, both from the hardware and software sides, considering specific resource constrained scenarios as case studies that have been used to develop solutions with much broader validity. Results achieved demonstrate that energy neutrality in monitoring under resource constrained conditions can be obtained without compromising efficiency and reliability of the outcomes

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs