Sensor-based artificial intelligence to support people with cognitive and physical disorders

A substantial portion of the world's population deals with disability. Many disabled people do not have equal access to healthcare, education, and employment opportunities, do not receive specific disability-related services, and experience exclusion from everyday life activities. One way to face these issues is through the use of healthcare technologies. Unfortunately, there is a large amount of diverse and heterogeneous disabilities, which require ad-hoc and personalized solutions. Moreover, the design and implementation of effective and efficient technologies is a complex and expensive process involving challenging issues, including usability and acceptability. The work presented in this thesis aims to improve the current state of technologies available to support people with disorders affecting the mind or the motor system by proposing the use of sensors coupled with signal processing methods and artificial intelligence algorithms. The first part of the thesis focused on mental state monitoring. We investigated the application of a low-cost portable electroencephalography sensor and supervised learning methods to evaluate a person's attention. Indeed, the analysis of attention has several purposes, including the diagnosis and rehabilitation of children with attention-deficit/hyperactivity disorder. A novel dataset was collected from volunteers during an image annotation task, and used for the experimental evaluation using different machine learning techniques. Then, in the second part of the thesis, we focused on addressing limitations related to motor disability. We introduced the use of graph neural networks to process high-density electromyography data for upper limbs amputees’ movement/grasping intention recognition for enabling the use of robotic prostheses. High-density electromyography sensors can simultaneously acquire electromyography signals from different parts of the muscle, providing a large amount of spatio-temporal information that needs to be properly exploited to improve recognition accuracy. The investigation of the approach was conducted using a recent real-world dataset consisting of electromyography signals collected from 20 volunteers while performing 65 different gestures. In the final part of the thesis, we developed a prototype of a versatile interactive system that can be useful to people with different types of disabilities. The system can maintain a food diary for frail people with nutrition problems, such as people with neurocognitive diseases or frail elderly people, which may have difficulties due to forgetfulness or physical issues. The novel architecture automatically recognizes the preparation of food at home, in a privacy-preserving and unobtrusive way, exploiting air quality data acquired from a commercial sensor, statistical features extraction, and a deep neural network. A robotic system prototype is used to simplify the interaction with the inhabitant. For this work, a large dataset of annotated sensor data acquired over a period of 8 months from different individuals in different homes was collected. Overall, the results achieved in the thesis are promising, and pave the way for several real-world implementations and future research directions

An Initial Investigation of Mental Well-being Monitoring through Personal Healthcare Devices

Smart sensory devices, such as smart watches, scales, and blood pressure gauges, are increasingly adopted by individuals aiming to improve their health and fitness. Those devices gather extensive data about cardiovascular parameters, physical activities, sleep quality, and behavior. Thanks to data analytics and artificial intelligence algorithms, they provide insights into the health status of individuals. Derived data is used to support self-care interventions and to provide practitioners with additional health information acquired on a continuous basis. However, most of the current solutions focus on the physical dimension of health, while the mental dimension is often neglected. In this paper, we present the initial investigation of a system to recognize a wide range of psychological parameters, including behavioral inhibition/activation, anxiety, and stress, leveraging data acquired from personal healthcare devices. We experimented with the application of different supervised learning algorithms on features extracted from heart, sleep, and inertial sensor data acquired from a cohort of 21 individuals over 24 hours each. Our preliminary findings suggest that our method may yield promising outcomes in recognizing different aspects of mental well-being. However, due to the limited size of the used dataset, a more comprehensive experimental evaluation, with a broader number of participants and carried out over an extended monitoring period, is imperative to substantiate the results

Explainable AI-powered graph neural networks for HD EMG-based gesture intention recognition

The ability to recognize fine-grained gestures enables several applications in different domains, including healthcare, robotics, remote control, and human-computer interaction. Traditional gesture recognition systems rely on data acquired from cameras, depth sensors, or smart gloves. More recently, techniques for recognizing gestures based on signals acquired by high-density (HD) EMG electrodes worn on the forearm have been proposed. An advantage of these techniques is that they do not rely on the use of external devices, and they are feasible also to people who underwent amputation. Unfortunately, the extraction of complex features from raw HD EMG signals may introduce delays that deter the real-time requirements of the system. To address this issue, in a preliminary investigation we proposed to use graph neural networks for gesture recognition from raw HD EMG data. In this paper, we extend our previous work by exploiting Explainable AI algorithms to automatically refine the graph topology based on the data in order to improve recognition rates and reduce the computational cost. We performed extensive experiments with a large dataset collected from 20 volunteers regarding the execution of 65 fine-grained gestures, comparing our technique with state-of-the-art methods based on handcrafted features and different machine learning algorithms. Experimental results show that our technique outperforms the state of the art in terms of recognition performance while incurring significantly lower computational cost at run-time

Non-neural phenotype of spinal and bulbar muscular atrophy: Results from a large cohort of Italian patients

Objective: To carry out a deep characterisation of the main androgen-responsive tissues involved in spinal and bulbar muscular atrophy (SBMA). Methods: 73 consecutive Italian patients underwent a full clinical protocol including biochemical and hormonal analyses, genitourinary examination, bone metabolism and densitometry, cardiological evaluation and muscle pathology. Results: Creatine kinase levels were slightly to markedly elevated in almost all cases (68 of the 73; 94%). 30 (41%) patients had fasting glucose above the reference limit, and many patients had total cholesterol (40; 54.7%), low-density lipoproteins cholesterol (29; 39.7%) and triglyceride (35; 48%) levels above the recommended values. Although testosterone, luteinising hormone and follicle-stimulating hormone values were generally normal, in one-third of cases we calculated an increased Androgen Sensitivity Index reflecting the presence of androgen resistance in these patients. According to the International Prostate Symptom Score (IPSS), 7/70 (10%) patients reported severe lower urinal tract symptoms (IPSS score >19), and 21/73 (30%) patients were moderately symptomatic (IPSS score from 8 to 19). In addition, 3 patients were carriers of an indwelling bladder catheter. Videourodynamic evaluation indicated that 4 of the 7 patients reporting severe urinary symptoms had an overt prostate-unrelated bladder outlet obstruction. Dual-energy X-ray absorptiometry scan data were consistent with low bone mass in 25/61 (41%) patients. Low bone mass was more frequent at the femoral than at the lumbar level. Skeletal muscle biopsy was carried out in 20 patients and myogenic changes in addition to the neurogenic atrophy were mostly observed. Conclusions: Our study provides evidence of a wide non-neural clinical phenotype in SBMA, suggesting the need for comprehensive multidisciplinary protocols for these patients. \ua9 2016 Published by the BMJ Publishing Group Limited

The leukemia inhibitory factor regulates fibroblast growth factor receptor 4 transcription in gastric cancer

Purpose: The gastric adenocarcinoma (GC) represents the third cause of cancer-related mortality worldwide, and available therapeutic options remain sub-optimal. The Fibroblast growth factor receptors (FGFRs) are oncogenic transmembrane tyrosine kinase receptors. FGFR inhibitors have been approved for the treatment of various cancers and a STAT3-dependent regulation of FGFR4 has been documented in the H.pylori infected intestinal GC. Therefore, the modulation of FGFR4 might be useful for the treatment of GC.MethodsTo investigate wich factors could modulate FGFR4 signalling in GC, we employed RNA-seq analysis on GC patients biopsies, human patients derived organoids (PDOs) and cancer cell lines.ResultsWe report that FGFR4 expression/function is regulated by the leukemia inhibitory factor (LIF) an IL-6 related oncogenic cytokine, in JAK1/STAT3 dependent manner. The transcriptomic analysis revealed a direct correlation between the expression of LIFR and FGFR4 in the tissue of an exploratory cohort of 31 GC and confirmed these findings by two external validation cohorts of GC. A LIFR inhibitor (LIR-201) abrogates STAT3 phosphorylation induced by LIF as well as recruitment of pSTAT3 to the promoter of FGFR4. Furthermore, inhibition of FGFR4 by roblitinib or siRNA abrogates STAT3 phosphorylation and oncogentic effects of LIF in GC cells, indicating that FGFR4 is a downstream target of LIF/LIFR complex. Treating cells with LIR-201 abrogates oncogenic potential of FGF19, the physiological ligand of FGFR4.ConclusionsTogether these data unreveal a previously unregnized regulatory mechanism of FGFR4 by LIF/LIFR and demonstrate that LIF and FGF19 converge on the regulation of oncogenic STAT3 in GC cells

Case Report: Post-COVID-19 Vaccine Recurrence of Guillain–Barré Syndrome Following an Antecedent Parainfectious COVID-19–Related GBS

Guillain–Barré syndrome (GBS) is an autoimmune neurological disorder often preceded by viral illnesses or, more rarely, vaccinations. We report on a unique combination of postcoronavirus disease 2019 (COVID-19) vaccine GBS that occurred months after a parainfectious COVID-19–related GBS. Shortly after manifesting COVID-19 symptoms, a 57-year-old man developed diplopia, right-side facial weakness, and gait instability that, together with electrophysiology and cerebrospinal fluid examinations, led to a diagnosis of post-COVID-19 GBS. The involvement of cranial nerves and IgM seropositivity for ganglioside GD1b were noteworthy. COVID-19 pneumonia, flaccid tetraparesis, and autonomic dysfunction prompted his admission to ICU. He recovered after therapy with intravenous immunoglobulins (IVIg). Six months later, GBS recurred shortly after the first dose of the Pfizer/BioNTech vaccine. Again, the GBS diagnosis was confirmed by cerebrospinal fluid and electrophysiology studies. IgM seropositivity extended to multiple gangliosides, namely for GM3/4, GD1a/b, and GT1b IgM. An IVIg course prompted complete recovery. This case adds to other previously reported observations suggesting a possible causal link between SARS-CoV-2 and GBS. Molecular mimicry and anti-idiotype antibodies might be the underlying mechanisms. Future COVID-19 vaccinations/revaccinations in patients with previous para-/post-COVID-19 GBS deserve a reappraisal, especially if they are seropositive for ganglioside antibodies

Clinical-Genetic Features Influencing Disability in Spastic Paraplegia Type 4: A Cross-sectional Study by the Italian DAISY Network

Background and objectives: Hereditary spastic paraplegias (HSPs) are a group of inherited rare neurologic disorders characterized by length-dependent degeneration of the corticospinal tracts and dorsal columns, whose prominent clinical feature is represented by spastic gait. Spastic paraplegia type 4 (SPG4, SPAST-HSP) is the most common form. We present both clinical and molecular findings of a large cohort of patients, with the aim of (1) defining the clinical spectrum of SPAST-HSP in Italy; (2) describing their molecular features; and (3) assessing genotype-phenotype correlations to identify features associated with worse disability. Methods: A cross-sectional retrospective study with molecular and clinical data collected in an anonymized database was performed. Results: A total of 723 Italian patients with SPAST-HSP (58% men) from 316 families, with a median age at onset of 35 years, were included. Penetrance was 97.8%, with men showing higher Spastic Paraplegia Rating Scale (SPRS) scores (19.67 ± 12.58 vs 16.15 ± 12.61, p = 0.009). In 26.6% of patients with SPAST-HSP, we observed a complicated phenotype, mainly including intellectual disability (8%), polyneuropathy (6.7%), and cognitive decline (6.5%). Late-onset cases seemed to progress more rapidly, and patients with a longer disease course displayed a more severe neurologic disability, with higher SPATAX (3.61 ± 1.46 vs 2.71 ± 1.20, p < 0.001) and SPRS scores (22.63 ± 11.81 vs 12.40 ± 8.83, p < 0.001). Overall, 186 different variants in the SPAST gene were recorded, of which 48 were novel. Patients with SPAST-HSP harboring missense variants displayed intellectual disability (14.5% vs 4.4%, p < 0.001) more frequently, whereas patients with truncating variants presented more commonly cognitive decline (9.7% vs 2.6%, p = 0.001), cerebral atrophy (11.2% vs 3.4%, p = 0.003), lower limb spasticity (61.5% vs 44.5%), urinary symptoms (50.0% vs 31.3%, p < 0.001), and sensorimotor polyneuropathy (11.1% vs 1.1%, p < 0.001). Increasing disease duration (DD) and abnormal motor evoked potentials (MEPs) were also associated with increased likelihood of worse disability (SPATAX score>3). Discussion: The SPAST-HSP phenotypic spectrum in Italian patients confirms a predominantly pure form of HSP with mild-to-moderate disability in 75% of cases, and slight prevalence of men, who appeared more severely affected. Early-onset cases with intellectual disability were more frequent among patients carrying missense SPAST variants, whereas patients with truncating variants showed a more complicated disease. Both longer DD and altered MEPs are associated with worse disability

Predicting needlestick and sharps injuries in nursing students: Development of the SNNIP scale

© 2020 The Authors. Nursing Open published by John Wiley & Sons Ltd. Aim: To develop an instrument to investigate knowledge and predictive factors of needlestick and sharps injuries (NSIs) in nursing students during clinical placements. Design: Instrument development and cross-sectional study for psychometric testing. Methods: A self-administered instrument including demographic data, injury epidemiology and predictive factors of NSIs was developed between October 2018–January 2019. Content validity was assessed by a panel of experts. The instrument's factor structure and discriminant validity were explored using principal components analysis. The STROBE guidelines were followed. Results: Evidence of content validity was found (S-CVI 0.75; I-CVI 0.50–1.00). A three-factor structure was shown by exploratory factor analysis. Of the 238 participants, 39% had been injured at least once, of which 67.3% in the second year. Higher perceptions of “personal exposure” (4.06, SD 3.78) were reported by third-year students. Higher scores for “perceived benefits” of preventive behaviours (13.6, SD 1.46) were reported by second-year students