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.

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.

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.

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.

Teaching Cloud Computing is becoming crucial since this recent computing paradigm is used in many fields and it is changing the way we use the applications and the technology. As a matter of the fact, most of the applications that we use everyday through the web are based on cloud services. Unfortunately, the difficulty to set up a real testbed for students and, at the same time, the lack of an easy, open and collaborative educational material freely available make teaching Cloud Computing a hard task. In this paper we discuss the state of the art concerning teaching Cloud Computing and we propose education materials and tools that make Cloud Computing easy to use even for students/educators without any computer science skills.