Min-Max Predictive Control of a Five-Phase Induction Machine

In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de Economía y Competitividad DPI 2016-76493-C3-1-R y 2014/425Unión Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42

Enhancing FoG detection by means of postural context using a waist accelerometer

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Home detection of freezing of gait using Support Vector Machines through a single waist-worn triaxial accelerometer

Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.Peer ReviewedPostprint (published version

A double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.Postprint (published version

A kinematic sensor and algorithm to detect motor fluctuations in Parkinson disease: Validation study under real conditions of use

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A heterogeneous database for movement knowledge extraction in Parkinson's disease

This paper presents the design and methodology used to create a heterogeneous database for knowledge movement extraction in Parkinson's Disease. This database is being constructed as part of REM- PARK project and is composed of movement measurements acquired from inertial sensors, standard medical scales as Uni ed Parkinson's Disease Rating Scale, and other information obtained from 90 Parkinson's Disease patients. The signals obtained will be used to create movement disorder detection algorithms using supervised learning techniques. The different sources of information and the need of labelled data pose many challenges which the methodology described in this paper addresses. Some preliminary data obtained are presented.Postprint (published version

Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer

Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems. Posture transitions (PT) are one of the most mechanically demanding activities in daily life and,thus, they can lead to falls in patients with mobility problems. This paper deals with PT recognition in Parkinson’s Disease (PD) patients by means of a triaxial accelerometer situated between the anterior and the left lateral part of the waist. Since sensor’s orientation is susceptible to change during long monitoring periods, a hierarchical structure of classifiers is proposed in order to identify PT while allowing such orientation changes. Results are presented based on signals obtained from 20 PD patients and 67 healthy people who wore an inertial sensor on different positions among the anterior and the left lateral part of the waist. The algorithm has been compared to a previous approach in which only the anterior-lateral location was analyzed improving the sensitivity while preserving specificity. Moreover, different supervised machine l earning techniques have been evaluated in distinguishing PT. Results show that the location of the sensor slightly affects method’s performance and, furthermore, PD motor state does not alter its accuracy.Peer ReviewedPostprint (author’s final draft

Effectiveness of Percutaneous Electrolysis in Supraspinatus Tendinopathy: A Single-Blinded Randomized Controlled Trial

Supraspinatus tendinopathy is one of the most common causes of shoulder pain. Many studies support conservative treatments such as exercise, trigger point dry needling or corticosteroid injections. Otherwise, a minimally invasive approach with percutaneous electrolysis (PE) has also been used successfully in shoulder pain, although evidence about its long-term effects is scarce. The aim of this trial was to determine the effects of PE on supraspinatus tendinopathy compared with trigger point dry needling (TDN). Thirty-six patients with supraspinatus tendinopathy were randomly assigned to either a PE group (n=18) or a TDN group (n=18). Both groups also performed eccentric exercises. The main outcome to be measured was the Numerical Pain Rating Scale (NPRS), but the shoulder range of motion (ROM) and trigger point pressure pain threshold (PPT) were also considered. A one-year follow-up was conducted. Significant differences favoring the PE group were found regarding pain at one-year follow-up (p=0.002). The improvement achieved in the PE group was greater in the NPRS (p<0.001), proximal PPT, middle PPT, distal PPT (all p<0.001) and ranges of movement. PE seems to be more effective than TDN in relieving pain and improving ROM and PPT supraspinatus values in patients with supraspinatus tendinopathy, both right after treatment and at one-year follow-up

Percutaneous Electrolysis in the Treatment of Lateral Epicondylalgia: A Single-Blind Randomized Controlled Trial

Few studies have considered the effects of percutaneous electrolysis (PE) in the treatment of lateral epicondylalgia (LE). For this reason, the objective of this study was to compare the effects of PE with an evidence-based approach-trigger point dry needling (TDN)-in patients with LE. A randomized controlled trial was conducted in which 32 participants with LE were randomly assigned to two treatment groups, the PE group (n= 16) and the TDN group (n= 16). Both groups received four therapy sessions and an eccentric exercise program to be performed daily. The numerical pain rating scale (NPRS), pressure pain thresholds (PPT), quality of life, and range of motion were measured before treatment, at the end of treatment, and at one- and three-month follow-ups. Significant between-group mean differences were found after treatment for NPRS (p< 0.001) and flexion movement (p= 0.006). At one-month follow-up, significant mean differences between groups were found for NPRS (p< 0.001), PPT (p= 0.021), and flexion (p= 0.036). At three-months follow-up, significant mean differences between groups were found for NPRS (p< 0.001), PPT (p= 0.004), and flexion (p= 0.003). This study provides evidence that PE could be more effective than TDN for short- and medium-term improvement of pain and PPTs in LE when added to an eccentric exercise program

A “HOLTER” for Parkinson's disease: validation of the ability to detect on-off states using the REMPARK system

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. Objective To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. Methods Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson's Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3 days and completed a diary of their motor state once every hour. Results The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). Conclusion The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.Peer ReviewedPostprint (published version