Massimo Canonico

The digitization of tourism has made numerous platforms available, but there remains a significant shortage of tools capable of promoting local events and activities. This study hypothesizes that a decentralized digital interface can mitigate over-tourism. We conducted an experiment by deploying a digital platform to assess the synergy between local providers and visitors through the Tourism Open-ended Experimentation Platform (TOEP), a multi-interface solution designed to directly connect tourism activity providers with residents and visitors. The platform integrates a web portal for providers and a mobile app for users, supported by a recommendation system based on individual profiles and preferences. TOEP stands out for its focus on local and sustainable tourism, facilitating the promotion of smaller events and helping to reduce the concentration of tourist flows in already saturated destinations. Initial validation, conducted with a panel of industry experts, highlighted the ease of use and good organization of the interfaces, with scores above average. Preliminary results confirm the relevance of TOEP as a tool for the sustainable and digitized promotion of local tourism, opening prospects for development towards a smart, participatory tourism ecosystem that can be replicated in different territorial contexts.

The explosive growth of smart devices and mobile users has led to an unprecedented demand for efficient data storage and processing. Edge computing has emerged as a promising solution to address these challenges by bringing computation and storage resources closer to data sources. Edge Storage Systems (ESSs) are a specific type of edge computing system designed to store and serve data efficiently at the network edge. While replication-based ESSs are a simple and effective approach, erasure-coded ESSs offer superior resilience, performance, and storage efficiency. However, effective data placement in erasure-coded ESSs is a complex problem, as it involves balancing factors such as data popularity, network latency, and resource utilization. In this paper, we propose a novel data placement approach for erasure-coded ESSs that addresses these challenges. Our approach consists of (1) a data placement strategy that leverages a virtual-space-based placement technique and incorporates data popularity into the placement decision, and (2) a dynamic data retrieval strategy that retrieves data from nodes with less load (in terms of data requests to be served). We evaluate our approach through extensive simulations, considering various network topologies, user mobility patterns, and data access patterns. Our results demonstrate that our proposed approach suitably addresses the data placement challenges arising from using erasure-coded ESSs, while allowing to exploit their advantages with respect to replication-based ESSs in terms of both space efficiency and data availability.

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app’s results and the physicians’ assessments for all tests. These findings demonstrate the STEP system’s potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations.

Telemedicine has emerged as a vital component of contemporary healthcare, revolutionizing the way medical services are delivered and accessed (e.g., it enables patients living in underserved or rural areas to receive medical consultation and treatment remotely). Moreover, telemedicine plays a pivotal role in improving healthcare efficiency by reducing wait times, minimizing unnecessary hospital visits, and optimizing resource allocation. In this paper, we present HealthTracker, a monitoring infrastructure for patients comprising two Internet of Things (IoT) devices (one of which was designed and created by us) and a mobile app that sends data collected by the IoT devices to a cloud service. All these components work together to provide an innovative system able to monitor patient health condition, provide alerts in emergency cases, and elaborate upon data to improve the quality of medical care. Preliminary tests show that the system works well, and real experimentation will start soon in collaboration with the local health authority.

In this paper, we describe Neonatal Resuscitation Training Simulator (NRTS), an Android mobile app designed to support medical experts to input, transmit and record data during a High-Fidelity Simulation course for neonatal resuscitation. This mobile app allows one to automatically send all the recorded data from the Neonatal Intensive Care Unit (NICU) of Casale Monferrato Children’s Hospital, (Italy) to a server in the cloud managed by the University of Piemonte Orientale (Italy). The medical instructor can then view statistics on simulation exercises, that may be used during the debriefing phase for the evaluation of multidisciplinary teams involved in the simulation scenarios.

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform.

The forensic study of mobile apps that use application-level encryption requires the decryption of the data they generate. Such a decryption requires the knowledge of the encryption algorithm and key. Determining them requires, however, a quite complex analysis that is time-consuming, error prone, and often beyond the reach of many forensic examiners. In this paper, we tackle this problem by devising a framework able to automate the decryption of these data when third-party encryption libraries or platforms are used. Our framework is based on the use of dynamic instrumentation of app’s binary code by means of hooking, which enables it to export the plaintext of data after they have been decrypted by the app, as well as the corresponding encryption key and parameters. This framework has been conceived to be used only with test devices used for forensic study purposes, and not with devices that need to be forensically analyzed. We describe the architecture of the framework as well as the implementation of its components and of the hooks supporting three prominent and popular encryption libraries, namely SQLCipher, Realm and Jetpack Security. Also, we validate our framework by comparing its decryption results against those published in the literature for Wickr Me, Signal, Threema, and Element.

Background: Parkinson's disease (PD) is a chronic, progressive neurodegenerative condition that gradually worsens motor function and leads to postural instability and, eventually, falls. Several factors may influence the frequency of future falls, such as slowness, freezing of gait, loss of balance, and mobility problems, cognitive impairments, and the number of previous falls. The TED bracelet is an advanced technological wearable device able to predict falls. Aims: This principal aim is to investigate the feasibility of a full-scale research project that uses the TED bracelet to identify whether individuals with PD are at risk of falling. Methods: This study will involve a pilot prospective observational study design; the subjects will include 26 patients suffering from mild PD and 26 others with no PD and no gait problems. Data will be collected from the TED bracelet and then compared to a paper-based fall diary. The enrolled participants will have a scheduled outpatient evaluation to collect both clinical and instrumental data as well as biological samples. Discussion: This pilot study could then be implemented in a larger form to further evaluate the effectiveness of the TED device. Finally, it will help further develop gait monitoring systems for people with Parkinson's disease and other neurodegenerative diseases that can affect physical function and mobility, such as dementia and Alzheimer's. Conclusions: Preventing falls and their complications could lead to major advancements in the quality of home care for patients with PD, which would significantly impact the quality of life of both these patients and their caregivers.

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.