(1) Background: Parkinson’s disease (PD) is one of the most frequent causes of disability among older people. Recently, virtual reality and exergaming have been emerged as promising tools for gait and balance rehabilitation in PD patients. Our purpose is to evaluate an innovative treatment for older patients with PD, based on non-immersive virtual reality exergames, improving gait and balance and reducing falling risk. (2) Methods: Thirty PD patients were recruited and randomly divided into two groups, to receive a traditional rehabilitation (CG) or a technological rehabilitation (TG). (3) Results: A statistical improvement of balance at the end of treatments was observed in both groups (CG: 12.4 ± 0.7 vs. 13.5 ± 0.8, p = 0.017; TG: 13.8 ± 0.5 vs. 14.7 ± 0.4, p = 0.004), while the overall risk of falling was significantly reduced only in the TG (POMA Total: 24.6 ± 0.9 vs. 25.9 ± 0.7, p = 0.010). The results between groups shows that all POMA scores differ in a statistically significant manner in the TG, emphasizing improvement not only in balance but also in gait characteristics (9.7 ± 0.8 vs. 11.4 ± 0.2, p = 0.003). Moreover, TG also improves the psychological sphere, measured thorough MSC-(17.1 ± 0.4 vs. 16.5 ± 0.4, p = 0.034). Although an improvement in FES-I and Gait Speed can be observed, this increase does not turn out to be significant. (4) Conclusions: Results suggest how non-immersive virtual reality exergaming technology offers the opportunity to effectively train cognitive and physical domains at the same time.

Background: With the rapid expansion of artificial intelligence (AI) applications, researchers have begun focusing on the concept of human-centered artificial intelligence (HCAI). This field is dedicated to designing AI systems that augment and improve human abilities, rather than substituting them. Objective: The objective of the paper was to review the information on design principles, techniques, applications, methods, and outcomes adopted in the field of HCAI, in order to provide some insights on the discipline, in relation with the broader concepts of human-centered and user-centered design. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist guidelines, we conducted a scoping review in PubMed, ScienceDirect, and IEEE Xplore, including all study types, excluding narrative reviews and editorials. Results: Out of the 1035 studies retrieved, 14 studies conducted between 2018 and 2023 met the inclusion criteria. The main fields of application were the health sector and AI applications. Human-centered design methodologies were adopted in 3 studies, personas in 2 studies, while the remaining methodologies were adopted in individual studies. Conclusions: HCAI emphasizes designing AI systems that prioritize human needs, satisfaction, and trustworthiness, but current principles and guidelines are often vague and difficult to implement. The review highlights the importance of involving users early in the development process to enhance trust, especially in fields like health care, but notes that there is a lack of standardized HCAI methodologies and limited practical applications adhering to these principles.

The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s.

The aim of this paper is to describe the results collected with the Italian study conducted within eWare project, aimed at supporting the autonomy and health of the older people affected by dementia and their informal caregivers, through the use of an innovative system based on a social robot and a sensorized environmental infrastructure. Nine dyads of older participants with their caregivers were enrolled in testing the system for 6 months. The results show a positive impact of the system in supporting the achievement of personal goals of the participants, as well as in supporting the quality of life of the informal caregivers. Nevertheless, the impact of the system in reducing the caregivers’ burden needs to be deeply investigated. This research highlights the potential of the eWare system but modifications will have to be made, especially on the interactivity capabilities, in order to meet the peculiar needs and wishes of older people with dementia and to favor the long-term use of the system.

Objective: Despite hand gesture recognition is a widely investigated field, the design of myoelectric architectures for detecting finer motor task, like the handwriting, is less studied. However, writing tasks involving cognitive loads represent an important aspect toward the generalization of myoelectric-based human-machine interfaces (HMI), and also for many rehabilitative tasks. In this study, the handwriting recognition of the ten digits was faced under the myoelectric control perspective, considering the probes setup and the feature extraction step. Methods: Time and frequency domain features were extracted from surface electromyography (sEMG) signals of 11 subjects who wrote the ten digits following a standardized template and 8 sEMG probes were equally distributed between forearm and wrist. Feature class separability was investigated and an aggregated feature set was built to train pattern recognition architectures, i.e. linear discriminant analysis (LDA) and quadratic support vector machine (QSVM). Also, four reduced probes setups were investigated. Results: LDA and QSVM showed mean accuracy of about 97%, with all the forearm and wrist sEMG information. A significant reduction of performances was observed considering the wrist or the forearm only (≤92%) and when LDA and QSVM were trained with two electrodes information (≤90%). Conclusions: For the reliable classification performances in a motor task involving high cognitive demands, like the handwriting, it is required the use of probes fully covering forearm and wrist. Outcomes support the methodological transfer from myoelectric hand gesture to the handwriting recognition, which represents a key aspect in the development of new HMI for rehabilitation tasks.

(1) Background: The increasing older population and demographic shifts highlight the need to understand the digital profiles of older adults, a pivotal factor in developing innovative technologies like the e-VITA virtual coach. This personalized coach provides recommendations for sustainable well-being in a smart home environment. (2) Methods: This study focuses on analyzing the characteristics of older individuals categorized as Internet users (onliners) and non-users (offliners). European Social Survey data from 2021 were utilized for European analysis, determining Internet usage based on frequency. Offliners are defined as users who never use the Internet, and onliners as those who use it, albeit with different frequencies. In Japan, data from the 9th International Comparative Survey on the Lives and Attitudes of the Elderly were employed, based on the responses of 1367 subjects, which defined onliners as individuals using communication devices and offliners as those not utilizing fax machines, cell phones, or the Internet. (3) Results: This paper presents a primary analysis of older end-user context and perspectives, outlining effective strategies for the diffusion of an active and healthy aging coaching system in the market and society. (4) Conclusions: the study emphasizes the importance of analyzing digital behavior in any user-centered design approach to ensure the system’s acceptance after deployment.