Applying clustering based on rules on WHO-DAS II for knowledge discovery on functional disabilities

The senior citizens represent a fast growing proportion of the population in Europe and other developed areas. This increases the proportion of persons with disability and reducing quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed a clinical test WHO Disability Assessment Schedule, (WHO-DASII) that is understood to include physical, mental, and social well-being, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge on the performance of the test WHO-DASII on the basis of a sample of neurological patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, a technique stablished on 1994 by Gibert which combines some Inductive Learning (from AI) methods with Statistics to extract knowledge on ill-structured domains (that is complex domains where consensus is not achieved, like is the case). So, in this paper, the results of applying this technique to the WHO-DASII results is presented.Postprint (published version

Reducing fall risk with combined motor and cognitive training in elderly fallers

Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.Peer ReviewedPostprint (published version

Advantages of combining AI and statistic for knowledge discovery on functional disability: multivariate analysis of assessment scales using clustering based on rules

In Europe, and other developed areas, senior citizens are a fast growing part of population. This increases proportion of disabled persons and proportion of persons with reduced quality of life. The concept of disability itself is not always precise and quantifiable. To improve agreement on the concept of disability, the World Health Organization (WHO) developed the clinical test WHO Disability Assessment Schedule, (WHO-DASII) that includes physical, mental, and social wellbeing, as a generic measure of functioning. From the medical point of view, the purpose of this work is to extract knowledge about the different kinds of disabilities from the responses to the WHO-DAS II of a sample of patients from an Italian hospital. This Knowledge Discovery problem has been faced by using clustering based on rules, an hybrid AI and Statistics technique introduced by Gibert (1994), which combines some Inductive Learning (from AI) with clustering (from Statistics) to extract knowledge from certain complex domains in form of typical profiles. In this paper, the results of applying this technique to the WHODAS II results is presented together with a comparison of other more classical analysis approaches. Four profiles of increasing degree of disability are identified together with the main characteristics associated to them.Peer ReviewedPostprint (author's final draft

Overground walking training with the i-Walker, a robotic servo-assistive device, enhances balance in patients with subacute stroke: a randomized controlled trial

Background: Patients affected by mild stroke benefit more from physiological overground walking training than walking-like training performed in place using specific devices. The aim of the study was to evaluate the effects of overground robotic walking training performed with the servo-assistive robotic rollator (i-Walker) on walking, balance, gait stability and falls in a community setting in patients with mild subacute stroke. Methods: Forty-four patients were randomly assigned to two different groups that received the same therapy in two daily 40-min sessions 5 days a week for 4 weeks. Twenty sessions of standard therapy were performed by both groups. In the other 20 sessions the subjects enrolled in the i-Walker-Group (iWG) performed with the i-Walker and the Control-Group patients (CG) performed the same amount of conventional walking oriented therapy. Clinical and instrumented gait assessments were made pre- and post-treatment. The follow-up observation consisted of recording the number of fallers in the community setting after 6 months. Results: Treatment effectiveness was higher in the iWG group in terms of balance improvement (Tinetti: 68.4 ± 27.6 % vs. 48.1 ± 33.9 %, p= 0.033) and 10-m and 6-min timed walking tests (significant interaction between group and time: F(1,40) = 14.252, p = 0.001; and F (1,40) = 7.883, p = 0.008, respectively). When measured, latero-lateral upper body accelerations were reduced in iWG (F= 4.727, p= 0.036), suggesting increased gait stability, which was supported by a reduced number of falls at home. Conclusions: A robotic servo-assisted i-Walker improved walking performance and balance in patients affected by mild/moderate stroke, leading to increased gait stability and reduced falls in the community.Peer ReviewedPostprint (published version

An adaptive scheme for wheelchair navigation collaborative control

In this paper we propose a system where machine and human cooperate at every situation via a reactive emergent behavior, so that the person is always in charge of his/her own motion. Our approach relies on locally evaluating the performance of the human and the wheelchair for each given situation. Then, both their motion commands are weighted according to those efficiencies and combined in a reactive way. This approach benefits from the advantages of typical reactive behaviors to combine different sources of information in a simple, seamless way into an emergent trajectory.Peer ReviewedPostprint (author’s final draft

e-Tools: an agent coordination layer to support the mobility of persons with disabilities

This paper outlines the development and integration of an agent coordination layer with a robotic platform to support senior citizens or persons with disabilities. This platform is situated in a given context (such as a Hospital) and it is intended to enhance user's mobility and autonomy. This objective is performed in a safe and sound fashion that meets the sets of laws, norms or protocols which rule the selected context.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 2Red de Universidades con Carreras en Informática (RedUNCI

A pilot study on brain plasticity of functional connectivity modulated by cognitive training in mild Alzheimer's disease and mild cognitive impairment

Alzheimer's disease (AD) alters the functional connectivity of the default mode network (DMN) but also the topological properties of the functional connectome. Cognitive training (CT) is a tool to slow down AD progression and is likely to impact on functional connectivity. In this pilot study, we aimed at investigating brain functional changes after a period of CT and active control (AC) in a group of 26 subjects with mild AD (mAD), 26 with amnestic mild cognitive impairment (aMCI), and a control group of 29 healthy elderly (HE) people. They all underwent a CT and AC in a counterbalanced order following a crossover design. Resting-state functional MRI and neuropsychological testing were acquired before and after each period. We tested post-CT and post-AC changes of cognitive abilities, of the functional connectivity of the DMN, and of topological network properties derived from graph theory and network-based statistics. Only CT produced functional changes, increasing the functional connectivity of the posterior DMN in all three groups. mAD also showed functional changes in the medial temporal lobe and topological changes in the anterior cingulum, whereas aMCI showed more widespread topological changes involving the frontal lobes, the cerebellum and the thalamus. Our results suggest specific functional connectivity changes after CT for aMCI and mAD

A new collaborative shared control strategy for continuous elder/robot assisted navigation

In nowadays aging society, many people require mobility assistance. Autonomous wheelchairs may provide some help, but they are not supposed to overtake all control on human mobility, as this is reported to lead to loss of residual capabilities and frustration. Instead, persons and wheelchairs are expected to cooperate. Traditionally, shared control hands control from human to robot depending on a triggering event. In this paper, though, we propose a method to allow constant cooperation between humans and robots, so that both have some weight in the emergent navigating behavior. We have tested the proposed method on a robotized Meyra wheelchair at Santa Lucia Hospedale in Rome with several volunteering in-patients presenting different disabilities. Results in indoor environments have been satisfactory both from a quantitative and qualitative point of view.Peer ReviewedPostprint (author’s final draft

Estimating dyskinesia severity in Parkinson's disease by using a waist-worn sensor: concurrent validity study

Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson’s patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30¿minutes, while performing normal daily life activities. Each patient’s activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician’s assessment and the sensor output was analyzed with the Spearman’s correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33–0.88; p¿=¿0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76–0.97: p¿<¿0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.Postprint (published version

Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer

Background After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. Objective To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. Materials and methods Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. Results Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. Conclusion The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.Postprint (published version