Special Issue: "Intelligent Systems for Clinical Care and Remote Patient Monitoring".

The year 2020 was definitely like no other [...].

Benefits of Telerehabilitation for Patients With Severe Acquired Brain Injury: Promising Results From a Multicenter Randomized Controlled Trial Using Nonimmersive Virtual Reality.

BACKGROUND: In neurorehabilitation, the use of innovative technologies offers many opportunities to monitor and improve the health status of patients with severe acquired brain injury (SABI). Telerehabilitation allows for continuity of service through the entire rehabilitation cycle, including assessment, intervention, consultation, and education, affording early reintegration and positively enhancing the quality of life (QoL). OBJECTIVE: The main purpose of this multicenter randomized controlled trial was to test the effectiveness of advanced training provided using a nonimmersive virtual reality rehabilitation system (ie, the VRRS HomeKit device) in improving functional outcomes in patients with SABI. METHODS: In total, 40 patients with SABI and their 40 caregivers visiting 2 Italian rehabilitation centers were enrolled in the study protocol and randomized into 2 groups. Of the 40 patients, 20 (50%) underwent the experimental training using the VRRS HomeKit (teleneuro-VRRS group), whereas the other 20 (50%) were administered usual territorial rehabilitative treatments (UTRTs; control group). To investigate motor and neuropsychological functioning, patients with SABI were evaluated before (T0) and at the end of (T1) each training session by a multispecialist team through a complete clinical and psychometric battery: the Barthel Index (BI), the Tinetti Scale (TS), the Modified Ashworth Scale (MAS), the Montreal Cognitive Assessment (MoCa), the Frontal Assessment Battery (FAB), the Beck Depression Inventory II (BDI-II), the Short Form Health Survey 36 (SF-36), and the Psychological General Well-Being Index (PGWBI). In addition, the Caregiver Burden Inventory (CBI) was administered to each caregiver to investigate the emotional burden status. RESULTS: The teleneuro-VRRS group achieved a statistically significant improvement in both general and motor outcomes, as well as psychological well-being and QoL, compared to the control group. In particular, the BI (P<.001), FAB (P<.001), and BDI-II (P<.001) were the outcome scales with the best improvement. The burden of caregivers also significantly improved in the teleneuro-VRRS group (CBI; P<.004). Between-group analysis showed statistical differences in the anxiety (effect size [ES]=0.85, P<.02) and self-control (ES=0.40, P<.03) subtests of the PGWBI and in the social role functioning (ES=0.85, P<.02) subtest of the SF-36, confirmed by quite medium and large ESs. CONCLUSIONS: Our results suggest that the VRRS is a suitable alternative tool or complementary tool or both to improve motor (level of functional independence) and cognitive (frontal/executive abilities) outcomes, reducing behavioral alterations (anxiety and depression symptoms) in patients with SABI, with a beneficial impact also on the caregivers' burden distress management, mitigating distress and promoting positive aspects of caring. TRIAL REGISTRATION: ClinicalTrials.gov NCT03709875; https://classic.clinicaltrials.gov/ct2/show/NCT03709875.

Deep learning applications in telerehabilitation speech therapy scenarios.

Nowadays, many application scenarios benefit from automatic speech recognition (ASR) technology. Within the field of speech therapy, in some cases ASR is exploited in the treatment of dysarthria with the aim of supporting articulation output. However, in presence of atypical speech, standard ASR approaches do not provide any reliable result in terms of voice recognition due to main issues, including: (i) the extreme intra and inter-speakers variability of the speech in presence of speech impairments, such as dysarthria; (ii) the absence of dedicated corpora containing voice samples from users with a speech disability to train a state-of-the-art speech model, particularly in non-English languages. In this paper, we focus on isolated word recognition for native Italian speakers with dysarthria and we exploit an existing mobile app to collect audio data from users with speech disorders while they perform articulation exercises for speech therapy purposes. With this data availability, a convolutional neural network has been trained to spot a small number of keywords within atypical speech, according to a speaker dependent method. Finally, we discuss the benefits of the trained ASR system in tailored telerehabilitation contexts intended for patients with dysarthria who can follow treatment plans under the supervision of remote speech language pathologists.

Recent Considerations on Gaming Console Based Training for Multiple Sclerosis Rehabilitation.

Multiple Sclerosis (MS) is a well-known, chronic demyelinating disease of the Central Nervous System (CNS) and one of the most common causes of disability in young adults. In this context, one of the major challenges in patients' rehabilitation is to maintain the gained motor abilities in terms of functional independence. This could be partially obtained by applying new emerging and cutting-edge virtual/augmented reality and serious game technologies for a playful, noninvasive treatment that was demonstrated to be quite efficient and effective in enhancing the clinical status of patients and their (re)integration into society. Recently, Cloud computing and Internet of Things (IoT) emerged as technologies that can potentially revolutionize patients' care. To achieve such a goal, a system that on one hand gathers patients' clinical parameters through a network of medical IoT devices equipped with sensors and that, on the other hand, sends the collected data to a hospital Cloud for processing and analytics is required. In this paper, we assess the effectiveness of a Nintendo Wii Fit® Plus Balance Board (WFBB) used as an IoT medical device adopted in a rehabilitation training program aimed at improving the physical abilities of MS patients (pwMS). In particular, the main scientific contribution of this paper is twofold: (i) to present a preliminary new pilot study investigating whether exercises based on the Nintendo Wii Fit® balance board included in a rehabilitation training program could improve physical abilities and Quality of Life (QoL) of patients compared to that of a conventional four-week rehabilitation training program; (ii) to discuss how such a rehabilitation training program could be adopted in the perspective of near future networks of medical IoT-based rehabilitation devices, interconnected with a hospital Cloud system for big data processing to improve patients' therapies and support the scientific research about motor rehabilitation. Results demonstrate the advantages of our approach from both health and technological points of view.

Infrared Monitoring of Oxygenation Process Generated by Robotic Verticalization in Bedridden People.

Bedridden people, especially at home, suffer from diverse pathologies beyond the main one that brings them to a specific position. Long-term cares are suitable at home to avoid congestions within hospital facilities. There are different technologies available to improve such people's conditions in their daily life. The standing posture is the key solution to enhance people's wellness amid the psychological burden due to the almost impossibility to be completely healed. The paper proposes the use of a polyfunctional and robotic bed capable of displaying many positions namely vertical, tilting, anti-trendelenburg with necessary graduation. A three-year monitoring of a patient, using a polyfunctional and robotic bed, suffering from amyotrophic lateral sclerosis (ALS), has been investigated. Different physiological parameters have been measured and, particularly, the variation of temperature has been measured in presence of body position connected to the robotic bed rotation that provokes biomechanical effort. It is demonstrated that certain body positions correspond to major and minor physical effort, hence major and minor oxygenation. An infrared camera has been used. As a positive result, the variation of posture has been delaying the increase of the pathological signs, because of better conditions.

Blockchain-Based Healthcare Workflow for Tele-Medical Laboratory in Federated Hospital IoT Clouds.

In a pandemic situation such as that we are living at the time of writing of this paper due to the Covid-19 virus, the need of tele-healthcare service becomes dramatically fundamental to reduce the movement of patients, thence reducing the risk of infection. Leveraging the recent Cloud computing and Internet of Things (IoT) technologies, this paper aims at proposing a tele-medical laboratory service where clinical exams are performed on patients directly in a hospital by technicians through IoT medical devices and results are automatically sent via the hospital Cloud to doctors of federated hospitals for validation and/or consultation. In particular, we discuss a distributed scenario where nurses, technicians and medical doctors belonging to different hospitals cooperate through their federated hospital Clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion leveraging the intrinsic security features of the Blockchain technology. In particular, both public and hybrid Blockchain scenarios are discussed and assessed using the Ethereum platform.

Telerehabilitation in individuals with severe acquired brain injury: Rationale, study design, and methodology.

BACKGROUND: Severe acquired brain injury (SABI) rehabilitation should be as intensive and long as to allow the patients get the best independence and quality of life (QoL), but facing with the rehabilitation socioeconomic burden. Telerehabilitation (TR) could supply frail subjects requiring long-term rehabilitation. METHODS: A multicenter, prospective, parallel design, single-blind trial will be conducted at the IRCCS Neurolesi Bonino Pulejo (Messina, Italy) and IRCCS Hospital San Camillo (Venice, Italy) involving patients suffering from SABI and requiring home motor and cognitive rehabilitation. We will investigate the use of TR, based on advanced Information and Communication Technology (ICT) solutions, taking into account that the supervision of rehabilitation at home will be enriched with the counseling and vital parameters monitoring. The enrolled patients will be balanced for pathology, and randomized in 2 groups, performing TR (G1) or standard rehabilitation training (G2), respectively, according to a pc-generated random assignment. TR will be delivered by means of an advanced video-conferencing system, whereas the patient will be provided with low-cost monitoring devices, able to collect data about his/her health status and QoL. In both the groups each treatment (either cognitive or motor, or both as per patient functional status) will last about 1 hour a day, 5 days/week, for 12 weeks. Two structured telephone interviews will be administered to the patients (when possible) and/or their caregivers, and to all the healthcare professionals involved in the patient management, 1 week after the beginning and at the end of the TR. All the patients will undergo a complete neurological and cognitive examination performed by skilled physicians and psychologists, blindly. Clinical evaluations will be administered blindly, before and after the treatments. RESULTS: the data of this study should demonstrate that TR is at least non-inferior in comparison with the same amount of usual territorial rehabilitative physical treatments, taking into account patients' functional recovery, psychological well-being, caregiver burden, and healthcare costs. CONCLUSION: data coming from this study could demonstrate the usefulness of TR in facing the rehabilitation socioeconomic burden of managing patients with SABI, so to allow the patients get the best independence and quality of life (QoL).

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.

An OAIS-Based Hospital Information System on the Cloud: Analysis of a NoSQL Column-Oriented Approach.

The Open Archive Information System (OAIS) is a reference model for organizing people and resources in a system, and it is already adopted in care centers and medical systems to efficiently manage clinical data, medical personnel, and patients. Archival storage systems are typically implemented using traditional relational database systems, but the relation-oriented technology strongly limits the efficiency in the management of huge amount of patients' clinical data, especially in emerging cloud-based, that are distributed. In this paper, we present an OAIS healthcare architecture useful to manage a huge amount of HL7 clinical documents in a scalable way. Specifically, it is based on a NoSQL column-oriented Data Base Management System deployed in the cloud, thus to benefit from a big tables and wide rows available over a virtual distributed infrastructure. We developed a prototype of the proposed architecture at the IRCCS, and we evaluated its efficiency in a real case of study.

Are Next-Generation Sequencing Tools Ready for the Cloud?

Cloud-based next-generation sequencing (NGS) tools are currently at an early stage. In this Forum article, we provide a clear picture of the current cloud-based NGS solutions and highlight what is still missing, along with future challenges for the achievement of an ecosystem of biotechnology clouds.

Enabling Secure XMPP Communications in Federated IoT Clouds Through XEP 0027 and SAML/SASL SSO.

Nowadays, in the panorama of Internet of Things (IoT), finding a right compromise between interactivity and security is not trivial at all. Currently, most of pervasive communication technologies are designed to work locally. As a consequence, the development of large-scale Internet services and applications is not so easy for IoT Cloud providers. The main issue is that both IoT architectures and services have started as simple but they are becoming more and more complex. Consequently, the web service technology is often inappropriate. Recently, many operators in both academia and industry fields are considering the possibility to adopt the eXtensible Messaging and Presence Protocol (XMPP) for the implementation of IoT Cloud communication systems. In fact, XMPP offers many advantages in term of real-time capabilities, efficient data distribution, service discovery and inter-domain communication compared to other technologies. Nevertheless, the protocol lacks of native security, data confidentiality and trustworthy federation features. In this paper, considering an XMPP-based IoT Cloud architectural model, we discuss how can be possible to enforce message signing/encryption and Single-Sign On (SSO) authentication respectively for secure inter-module and inter-domain communications in a federated environment. Experiments prove that security mechanisms introduce an acceptable overhead, considering the obvious advantages achieved in terms of data trustiness and privacy.

Embedded systems for supporting computer accessibility.

Nowadays, customized AT software solutions allow their users to interact with various kinds of computer systems. Such tools are generally available on personal devices (e.g., smartphones, laptops and so on) commonly used by a person with a disability. In this paper, we investigate a way of using the aforementioned AT equipments in order to access many different devices without assistive preferences. The solution takes advantage of open source hardware and its core component consists of an affordable Linux embedded system: it grabs data coming from the assistive software, which runs on the user's personal device, then, after processing, it generates native keyboard and mouse HID commands for the target computing device controlled by the end user. This process supports any operating system available on the target machine and it requires no specialized software installation; therefore the user with a disability can rely on a single assistive tool to control a wide range of computing platforms, including conventional computers and many kinds of mobile devices, which receive input commands through the USB HID protocol.

Providing Assistive Technology Applications as a Service Through Cloud Computing.

Users with disabilities interact with Personal Computers (PCs) using Assistive Technology (AT) software solutions. Such applications run on a PC that a person with a disability commonly uses. However the configuration of AT applications is not trivial at all, especially whenever the user needs to work on a PC that does not allow him/her to rely on his / her AT tools (e.g., at work, at university, in an Internet point). In this paper, we discuss how cloud computing provides a valid technological solution to enhance such a scenario.With the emergence of cloud computing, many applications are executed on top of virtual machines (VMs). Virtualization allows us to achieve a software implementation of a real computer able to execute a standard operating system and any kind of application. In this paper we propose to build personalized VMs running AT programs and settings. By using the remote desktop technology, our solution enables users to control their customized virtual desktop environment by means of an HTML5-based web interface running on any computer equipped with a browser, whenever they are.