On Internet-of-Things Devices in Ambient Assisted Living Solutions

In this paper, we present the results of a Rapid Review (RR) of Internet-of-Things (IoT) devices that have been using in AAL solutions for elderly people. In that respect, our literature review is born from the need of delivering evidence to the stakeholders that are involved in the project in which this RR has been conducted. Nevertheless, the obtained results can be of interest to software engineers who want to know which IoT devices have been using in AAL solutions for elderly people to support decision-making in the development of these solutions. The findings of our RR emerge from 61 papers and can be summarized as follows: (i) a number of IoT devices are used in AAL solutions for elderly people; (ii) most IoT devices do not explicitly focus on specific diseases; and (iii) IoT devices support several needs

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings

A Human–AI interaction paradigm and its application to rhinocytology

This article explores Human-Centered Artificial Intelligence (HCAI) in medical cytology, with a focus on enhancing the interaction with AI. It presents a Human–AI interaction paradigm that emphasizes explainability and user control of AI systems. It is an iterative negotiation process based on three interaction strategies aimed to (i) elaborate the system outcomes through iterative steps (Iterative Exploration), (ii) explain the AI system’s behavior or decisions (Clarification), and (iii) allow non-expert users to trigger simple retraining of the AI model (Reconfiguration). This interaction paradigm is exploited in the redesign of an existing AI-based tool for microscopic analysis of the nasal mucosa. The resulting tool is tested with rhinocytologists. The article discusses the analysis of the results of the conducted evaluation and outlines lessons learned that are relevant for AI in medicine

A Tool for Improving Privacy in Software Development

Privacy is considered a necessary requirement for software development. It is necessary to understand how certain software vulnerabilities can create problems for organizations and individuals. In this context, privacy-oriented software development plays a primary role to reduce some problems that can arise simply from individuals’ interactions software applications, even when the data being processed is not directly linked to identifiable. The loss of confidentiality, integrity, or availability at some point in the data processing, such as data theft by external attackers or the unauthorized access or use of data by employees., represent some types of cybersecurity-related privacy events. Therefore, this research work discusses the formalization of 5 key privacy elements (Privacy by Design Principles, Privacy Design Strategies, Privacy Pattern, Vulnerabilities and Context) in software development and presents a privacy tool that supports developers’ decisions to integrate privacy and security requirements in all software development phases

Deep Learning for Processing Electromyographic Signals: a Taxonomy-based Survey

Deep Learning (DL) has been recently employed to build smart systems that perform incredibly well in a wide range of tasks, such as image recognition, machine translation, and self-driving cars. In several fields the considerable improvement in the computing hardware and the increasing need for big data analytics has boosted DL work. In recent years physiological signal processing has strongly benefited from deep learning. In general, there is an exponential increase in the number of studies concerning the processing of electromyographic (EMG) signals using DL methods. This phenomenon is mostly explained by the current limitation of myoelectric controlled prostheses as well as the recent release of large EMG recording datasets, e.g. Ninapro. Such a growing trend has inspired us to seek and review recent papers focusing on processing EMG signals using DL methods. Referring to the Scopus database, a systematic literature search of papers published between January 2014 and March 2019 was carried out, and sixty-five papers were chosen for review after a full text analysis. The bibliometric research revealed that the reviewed papers can be grouped in four main categories according to the final application of the EMG signal analysis: Hand Gesture Classification, Speech and Emotion Classification, Sleep Stage Classification and Other Applications. The review process also confirmed the increasing trend in terms of published papers, the number of papers published in 2018 is indeed four times the amount of papers published the year before. As expected, most of the analyzed papers (≈60 %) concern the identification of hand gestures, thus supporting our hypothesis. Finally, it is worth reporting that the convolutional neural network (CNN) is the most used topology among the several involved DL architectures, in fact, the sixty percent approximately of the reviewed articles consider a CNN

Effects of short-term feed restriction on milk yield and composition, and hormone and metabolite profiles in mid-lactation Sarda dairy sheep with different body condition score

Ten Sarda dairy ewes (5 with high Body Condition Score: H-BCS, BCS>2.5; BW 48.8±5.4 kg; 5 with low BCS: L-BCS, BCS<2.5; BW 36.2±4.7 kg) were subjected, after 7-day preliminary (Prel) period, to short-term feed restriction (FR, 50% of nutrient requirements) for three days followed by refeeding (Re-Fed, 100% requirements) for three days. Milk yield and composition (protein, fat, lactose, MUN, SCC, fatty acids), and blood parameters (glucose, NEFA, BUN, insulin, GH, IGF-I, leptin) were monitored. Milk yield decreased during FR in both BCS groups: at day 3 it was 38% and 35% of Prel values in HBCS and L-BCS ewes, respectively, reaching Prel levels at Re-Fed in both groups. Milk fat concentration was influenced by BCS¥sampling, increasing in H-BCS ewes during FR, but not varying in L-BCS ewes throughout the trial. During FR, milk protein increased as milk yield decreased. There was no change in milk urea nitrogen concentration during FR, but this decreased in both BCS groups during Re-Fed. FR modified the FA profile of milk fat in both BCS groups, increasing LCFA at the expense of SCFA and MCFA. Some blood parameters (NEFA, GH and IGF-I) were influenced by BCS, whereas almost all parameters were influenced by sampling. There was a rapid return to initial levels in all parameters except milk urea, blood urea and insulin at Re-Fed

Renal involvement in mitochondrial cytopathies

Mitochondrial cytopathies constitute a group of rare diseases that are characterized by their frequent multisystemic involvement, extreme variability of phenotype and complex genetics. In children, renal involvement is frequent and probably underestimated. The most frequent renal symptom is a tubular defect that, in most severe forms, corresponds to a complete De Toni-Debré-Fanconi syndrome. Incomplete proximal tubular defects and other tubular diseases have also been reported. In rare cases, patients present with chronic tubulo-interstitial nephritis or cystic renal diseases. Finally, a group of patients develop primarily a glomerular disease. These patients correspond to sporadic case reports or can be classified into two major defects, namely 3243 A>G tRNALEU mutations and coenzyme Q10 biosynthesis defects. The latter group is particularly important because it represents the only treatable renal mitochondrial defect. In this Educational Review, the principal characteristics of these diseases and the main diagnostic approaches are summarized