Gait Analysis for Early Neurodegenerative Diseases Classification through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined

Ensemble consensus: An unsupervised algorithm for anomaly detection in network security data

Unsupervised network traffic monitoring is of paramount importance in cyber security. It allows to detect suspicious events that are defined as non-normal and report or block them. In this work the Anomaly Consensus algorithm for unsupervised network analysis is presented. The algorithm aim is to fuse the three most important anomaly detection techniques for unsupervised detection of suspicious events. Tests are performed against the KDD Cup'99 dataset, one of the most famous supervised datasets for automatic intrusion detection created by DARPA. Accuracies reveal that Anomaly Consensus performs on-par with respect to state-of-the-art supervised learning techniques, ensuring high generalization power also in borderline tests when small amount of data (5%) is used for training and the rest is for validation and testing

Factors affecting adherence to guidelines for antithrombotic therapy in elderly patients with atrial fibrillation admitted to internal medicine wards

Current guidelines for ischemic stroke prevention in atrial fibrillation or flutter (AFF) recommend Vitamin K antagonists (VKAs) for patients at high-intermediate risk and aspirin for those at intermediate-low risk. The cost-effectiveness of these treatments was demonstrated also in elderly patients. However, there are several reports that emphasize the underuse of pharmacological prophylaxis of cardio-embolism in patients with AFF in different health care settings. AIMS: To evaluate the adherence to current guidelines on cardio-embolic prophylaxis in elderly (> 65 years old) patients admitted with an established diagnosis of AFF to the Italian internal medicine wards participating in REPOSI registry, a project on polypathologies/polytherapies stemming from the collaboration between the Italian Society of Internal Medicine and the Mario Negri Institute of Pharmacological Research; to investigate whether or not hospitalization had an impact on guidelines adherence; to test the role of possible modifiers of VKAs prescription. METHODS: We retrospectively analyzed registry data collected from January to December 2008 and assessed the prevalence of patients with AFF at admission and the prevalence of risk factors for cardio-embolism. After stratifying the patients according to their CHADS(2) score the percentage of appropriateness of antithrombotic therapy prescription was evaluated both at admission and at discharge. Univariable and multivariable logistic regression models were employed to verify whether or not socio-demographic (age >80years, living alone) and clinical features (previous or recent bleeding, cranio-facial trauma, cancer, dementia) modified the frequency and modalities of antithrombotic drugs prescription at admission and discharge. RESULTS: Among the 1332 REPOSI patients, 247 were admitted with AFF. At admission, CHADS(2) score was ≥ 2 in 68.4% of patients, at discharge in 75.9%. Among patients with AFF 26.5% at admission and 32.8% at discharge were not on any antithrombotic therapy, and 43.7% at admission and 40.9% at discharge were not taking an appropriate therapy according to the CHADS(2) score. The higher the level of cardio-embolic risk the higher was the percentage of antiplatelet- but not of VKAs-treated patients. At admission or at discharge, both at univariable and at multivariable logistic regression, only an age >80 years and a diagnosis of cancer, previous or active, had a statistically significant negative effect on VKAs prescription. Moreover, only a positive history of bleeding events (past or present) was independently associated to no VKA prescription at discharge in patients who were on VKA therapy at admission. If heparin was considered as an appropriate therapy for patients with indication for VKAs, the percentage of patients admitted or discharged on appropriate therapy became respectively 43.7% and 53.4%. CONCLUSION: Among elderly patients admitted with a diagnosis of AFF to internal medicine wards, an appropriate antithrombotic prophylaxis was taken by less than 50%, with an underuse of VKAs prescription independently of the level of cardio-embolic risk. Hospitalization did not improve the adherence to guideline

Adherence to antibiotic treatment guidelines and outcomes in the hospitalized elderly with different types of pneumonia

Background: Few studies evaluated the clinical outcomes of Community Acquired Pneumonia (CAP), Hospital-Acquired Pneumonia (HAP) and Health Care-Associated Pneumonia (HCAP) in relation to the adherence of antibiotic treatment to the guidelines of the Infectious Diseases Society of America (IDSA) and the American Thoracic Society (ATS) in hospitalized elderly people (65 years or older). Methods: Data were obtained from REPOSI, a prospective registry held in 87 Italian internal medicine and geriatric wards. Patients with a diagnosis of pneumonia (ICD-9 480-487) or prescribed with an antibiotic for pneumonia as indication were selected. The empirical antibiotic regimen was defined to be adherent to guidelines if concordant with the treatment regimens recommended by IDSA/ATS for CAP, HAP, and HCAP. Outcomes were assessed by logistic regression models. Results: A diagnosis of pneumonia was made in 317 patients. Only 38.8% of them received an empirical antibiotic regimen that was adherent to guidelines. However, no significant association was found between adherence to guidelines and outcomes. Having HAP, older age, and higher CIRS severity index were the main factors associated with in-hospital mortality. Conclusions: The adherence to antibiotic treatment guidelines was poor, particularly for HAP and HCAP, suggesting the need for more adherence to the optimal management of antibiotics in the elderly with pneumonia

Next Activity Prediction and Elapsed Time Prediction on Process Dataset

Process mining is a field of research that has gained much attention in recent years because of its ability to analyze and improve processes. Indeed, one of the key aspects of process mining is its ability to predict the activities in the future and the time spent on these activities. In this work is proposed the use of Bidirectional LSTM and Multi-Speed Transformer on a recent dataset called BPIC-2020 related to reimbursement process of the University of Technology of Eidenhoven. Results shows that Multi-Speed Transformer is more capable to performs next activity prediction than the Bi-LSTM. Meanwhile, for the elapsed time prediction, the viceversa is true. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Fall detection by human pose estimation and kinematic theory

In a society with increasing age, the understanding of human falls it is of paramount importance. This paper presents a Decision Support System whose pipeline is designed to extract and compute physical domain's features achieving the state of the art accuracy on the Le2i and UR fall detection datasets. The paper uses the Kinematic Theory of Rapid Human Movement and its sigma-lognormal model together with classic physical features to achieve 98% and 99% of accuracy in automatic fall detection on respectively Le2i and URFD datasets. The effort made in the design of this work is toward recognition of falls by using physical models whose laws are clear and understandable

Mechanical Tests on Wood Specimens Connected by Metal Plates

Proceedings of the 8th World Conference on Timber Engineering, June 14-17, 2004, Lahti, Finland (ISSN: 0356-9403

Isolation and characterization of bioactive compounds from plant resources: the role of analysis in the ethnopharmacological approach

The phytochemical research based on ethnopharmacology is considered an effective approach in the discovery of novel chemicals entities with potential as drug leads. Plants/plant extracts/decoctions, used by folklore traditions for treating several diseases, represent a source of chemical entities but no information are available on their nature. Starting from this viewpoint, the aim of this review is to address natural-products chemists to the choice of the best methodologies, which include the combination of extraction/sample preparation tools and analytical techniques, for isolating and characterizing bioactive secondary metabolites from plants, as potential lead compounds in the drug discovery process. The work is distributed according to the different steps involved in the ethnopharmacological approach (extraction, sample preparation, biological screening etc.), discussing the analytical techniques employed for the isolation and identification of compound/s responsible for the biological activity claimed in the traditional use (separation, spectroscopic, hyphenated techniques, etc.). Particular emphasis will be on herbal medicines applications and developments achieved from 2010 up to date