A Deep Learning-Based Approach for the Recognition of Sleep Disorders in Patients with Cognitive Diseases: A Case Study

Alzheimer's disease is the most common type of dementia. Patients suffer from of this kind of disease could show symptoms such as sleep disturbances, muscle rigidity or other typical Alzheimer's movement irregularities. In our work, we have focused on those types of disturbances related to sleep disorders. Due to their not well-known nature, it is difficult to develop software able to identify sleep disorders. In this work, we have addressed the problem of the automatic recognition of sleep disorders in patients with Alzheimer's disease by using deep learning algorithm

Gait Anomaly Detection of Subjects With Parkinson's Disease Using a Deep Time Series-Based Approach

Parkinson's disease (PD) is a cognitive degenerative disorder of the central nervous system that mainly affects the motor system. The earliest symptoms evidence a general deficit of coordination and an unsteady gait. Current approaches for the evaluation and assessment of gait disturbances in PD have proved to be expensive, inconvenient and ineffective in the detection of anomalous walking patterns. In this paper, we address these issues by defining a deep time series-based approach for the detection of anomalous walking patterns in the gait dynamics of elderly people by analyzing the acceleration values of their movements. The results show a training accuracy and testing accuracy of over 90% with an accuracy improvement of 4.28% in comparison with related works

Analysis of operator variability in standardized root canal preparation with Ni–Ti instruments

Summary Objectives The aim of this study is to assess the influence of the operator variability during the preparation of standard resin blocks and the learning process with Ni–Ti instruments, the null hypothesis being that there are no significant differences regarding dentin removal, variation of the angle and radius of curvature, centering of the preparation, and time required by operator with different clinical experience. Materials and methods 100 standard resin blocks were used for this study. The blocks were divided into 4 groups of 25, and each group was assigned to a different operator. Operators 1 and 2 were 4th year DDS undergraduate students that had never performed endodontic treatments and as such could be considered as inexperienced operators. Operators 3 and 4 were 2 clinicians with 10 years experience and that were familiar with endodontic treatments and instrumentation (experienced operators). Many parameters were measured and compared. Result Differences could be detected between the 2 different level of clinical experience. Conclusions Under the experimental conditions of this study, experience of the operators can be considered as a crucial factor when all the other parameters are kept standard. In conclusion the null-hypothesis has to be rejected. Statistically significant differences exist in dentin removal, in the variation of the degree of curvature, in the centering of the preparation and in the time needed for the preparation when operators with different clinical experience where tested

Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction

ALPHA: an eAsy inteLligent service Platform for Healthy Ageing

Dementia is one of the biggest global public health challenges facing our generation. Alzheimers disease (AD) is the most frequent cause of dementia in elderly people over 65 years of age. The typical characteristic of AD is impairment of memory. As the disease progresses, other cognitive domains such as language, praxis, visuo-spatial and executive functions become involved, eventually resulting in global cognitive decline. Behavioral Psychological Symptoms of Dementia (BPSD) problems are constant in AD and have highly negative impact on the quality of life of patients and their families. ALPHA project aims at developing an intelligent situation-aware system to collect and process information about Alzheimer Disease patients? life style. Starting from various data provided by caregivers and a set of non-invasive sensors and devices. ALPHA will provide clinicians with new quantitative and qualitative information about patients? abnormal behavior which, along with medical data, will enhance the accuracy and reliability of monitoring and assessing the patient?s health status. Clinicians will be supported by a suite of specifically designed tools and interfaces to analyze the metadata captured, improve management of personalized care plans and better interact with patients and caregivers. Studies of antique records of former psychiatric hospital will enable us towiden the knowledge of behavioral disorders thus allowing to compare the ancient ones and the curcurrent and to probabilistically determine relation between type of dementia and behavioral disorders

Optimal User Scheduling in Multi Antenna System Using Multi Agent Reinforcement Learning

Multiple Input Multiple Output (MIMO) systems have been gaining significant attention from the research community due to their potential to improve data rates. However, a suitable scheduling mechanism is required to efficiently distribute available spectrum resources and enhance system capacity. This paper investigates the user selection problem in Multi-User MIMO (MU-MIMO) environment using the multi-agent Reinforcement learning (RL) methodology. Adopting multiple antennas’ spatial degrees of freedom, devices can serve to transmit simultaneously in every time slot. We aim to develop an optimal scheduling policy by optimally selecting a group of users to be scheduled for transmission, given the channel condition and resource blocks at the beginning of each time slot. We first formulate the MU-MIMO scheduling problem as a single-state Markov Decision Process (MDP). We achieve the optimal policy by solving the formulated MDP problem using RL. We use aggregated sum-rate of the group of users selected for transmission, and a 20% higher sum-rate performance over the conventional methods is reported

An AI-empowered infrastructure for risk prevention during medical examination

A medical examination at Nuclear Medicine Department (NMD) carries out at multiple stages. Patients are accompanied and guided by nurses during their movements within the NMD to avoid them entering into any hazardous situation. However, even accompanying nurses could be exposed to harmful radiation, which puts their safety at risk. Artificial Intelligence (AI) technologies can address these issues by supporting these processes avoiding risky situations, and preventing patients’ and clinicians’ safe. This article presents an artificial intelligence-based architecture for risk management during the nuclear medical examination to automatically guide the patients during the medical examination and support injury prevention. The architecture comprises two main components; the first component integrates Deep Learning (DL) techniques and WiFi tools to monitor and verify the patient’s position continuously; the second integrates Reinforcement Learning (RL) techniques to guide the patient during his/her examination. Experimental results show the suitability of the proposed architecture. Therefore the proposed risk management system can support the prevention of risks and injuries during medical examination and reduce operational costs

Bone Metabolism Effects of Medical Therapy in Advanced Renal Cell Carcinoma

The medical therapy of advanced renal cell carcinoma (RCC) is based on the use of targeted therapies, such as tyrosine kinase inhibitors (TKI) and immune-checkpoint inhibitors (ICI). These therapies are characterized by multiple endocrine adverse events, but the effect on the bone is still less known. Relatively few case reports or small case series have been specifically focused on TKI and ICI effects on bone metabolism. However, the importance to consider these possible side effects is easily intuitable because the bone is one of the most frequent metastatic sites of RCC. Among TKI used in RCC, sunitinib and sorafenib can cause hypophosphatemia with increased PTH levels and low-normal serum calcium levels. Considering ICI, nivolumab and ipilimumab, which can be used in association in a combination strategy, are associated with an increased risk of hypocalcemia, mediated by an autoimmune mechanism targeted on the calcium-sensing receptor. A fearsome complication, reported for TKI and rarely for ICI, is osteonecrosis of the jaw. Awareness of these possible side effects makes a clinical evaluation of RCC patients on anticancer therapy mandatory, especially if associated with antiresorptive therapy such as bisphosphonates and denosumab, which can further increase the risk of these complications

Immune Checkpoint Inhibitors as a Threat to the Hypothalamus–Pituitary Axis: A Completed Puzzle

Immune checkpoint inhibitors (ICI) prolong the survival in an increasing number of patients affected by several malignancies, but at the cost of new toxicities related to their mechanisms of action, autoimmunity. Endocrine toxicity frequently occurs in patients on ICI, but endocrine dysfunctions differ based on the ICI-subclass, as follows: agents targeting the CTLA4-receptor often induce hypophysitis and rarely thyroid dysfunction, which is the opposite for agents targeting the PD-1/PD-L1 axis. Recently, few cases of central diabetes insipidus have been reported as an adverse event induced by both ICI-subclasses, either in the context of anterior hypophysitis or as selective damage to the posterior pituitary or in the context of hypothalamitis. These new occurrences demonstrate, for the first time, that ICI-induced autoimmunity may involve any tract of the hypothalamic–pituitary axis. However, the related pathogenic mechanisms remain to be fully elucidated. Similarly, the data explaining the endocrine system susceptibility to primary and ICI-induced autoimmunity are still scarce. Since ICI clinical indications are expected to expand in the near future, ICI-induced autoimmunity to the hypothalamic–pituitary axis presents as a unique in vivo model that could help to clarify the pathogenic mechanisms underlying both the dysfunction induced by ICI to the hypothalamus–pituitary axis and primary autoimmune diseases affecting the same axis