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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

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

Edition of a Research topic of Frontiers in Neurology with 12 reviewed and accepted original papers

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.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

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. Part of this project has been performed within the framework of the MASPARK project which is funded by La Fundació La Marató de TV3 20140431. This work also forms part of the framework of the FP7 project REMPARK ICT-287677, which is funded by the European Community. peer-reviewed

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests with motor and non-motor symptoms. PD treatment is symptomatic and tries to alleviate the associated symptoms through an adjustment of the medication. As the disease is evolving and this evolution is patient specific, it could be very difficult to properly manage the disease. The current available technology (electronics, communication, computing, etc.), correctly combined with wearables, can be of great use for obtaining and processing useful information for both clinicians and patients allowing them to become actively involved in their condition. Parkinson's Disease Management through ICT: The REMPARK Approach presents the work done, main results and conclusions of the REMPARK project (2011 - 2015) funded by the European Union under contract FP7-ICT-2011-7-287677. REMPARK system was proposed and developed as a real Personal Health Device for the Remote and Autonomous Management of Parkinson's Disease, composed of different levels of interaction with the patient, clinician and carers, and integrating a set of interconnected sub-systems: sensor, auditory cueing, Smartphone and server. The sensor subsystem, using embedded algorithmics, is able to detect the motor symptoms associated with PD in real time. This information, sent through the Smartphone to the REMPARK server, is used for an efficient management of the disease. Implementation of REMPARK will increase the independence and Quality of Life of patients; and improve their disease management, treatment and rehabilitation.

Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.

HighlightsThe necessary algorithms to evaluate the occurrence of dopaminergic-induced dyskinesias in the activities of daily life are developed.Sensor placement at the waist provides a good resolution for almost any choreic dyskinesias and provides a good usability and comfort to the patient.A new frequency based approach is proposed to evaluate the occurrence of dyskinesias.The algorithm presented has been evaluated on a database of signals of 92 PD patients and provides specificities and sensitivities above 90%. BackgroundAfter 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. ObjectiveTo 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 methodsData 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. ResultsResults 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. ConclusionThe 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.

The electrical interfacing with cultured networks of neurons by means of multielectrode arrays (MEAs) is an area of intensive research given the potential of this technique to aid in the extraction of the algorithms that support neurocomputation in brain tissue. To this end, we have previously described the polymer-on-multielectrode technology (PoM [E. Claverol-Tinture, M. Ghirardi, F. Fiumara, X. Rosell, J. Cabestany. Multielectrode arrays with elastomeric microstructures for neuronal patterning towards interfacing with uni-dimensional neuronal networks. J. Neural Eng. 2(2) (2005) L1-7] and demonstrated single-site recordings from patterned invertebrate cells. The realisation of the full potential of PoM is dependent on its successful application to vertebrate cells and to the recording of activity in ensembles of neurons. Here, we describe progress in these directions, specifically towards the development of vertebrate neuronal cultures devoid of glial layers compatible with PoM devices, the connection of pairs of invertebrate neurons threading microchannels and the recordings of synaptic and spike-like activity.

Otoliths have traditionally been used to estimate fish age. However, many factors influence changes in otolith shape, so manual classification remains a complicated task. Very recently, statistical learning techniques have been proposed for automating such a process. We propose performing automatic fish age classification using otolith images (in cases in which growth rings are not properly displayed or are unavailable), morphological and statistical feature-extraction methods and multi-class support vector machines. The results of our experiments, in which we classified cod ages from otolith images, demonstrate the effectiveness of the approach.

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 learning 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. Posture transition identification is performed by means of a tri-axial accelerometer located in the waist.A hierarchical structure of classifiers allows to determine the human posture.SVM techniques have been used to set parameters of the algorithm.The algorithm allows different locations along waist's left side.The algorithm is focused on Parkinson's disease patients.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder. First appreciable symptoms in PD are those related to an altered movement control. Current PD treatments temporally revert the symptoms, but they do not prevent disease’s progression. At the beginning of the treatment, the antiparkinsonian effect of the medication is very evident and symptoms may completely disappear for hours; however, as disease progresses, motor fluctuations appear. Collecting precise information on the temporal course of fluctuations is essential for tailoring an optimal therapy in PD patients and is one of the main parameters in clinical trials. This paper presents an algorithm for wearable devices to automatically detect patient’s motor fluctuations based on inertial sensors. The algorithm has been evaluated in 7 PD patients at their homes without supervision and performing their usual activities. Results are a mean sensitivity of 99.9% and a mean specificity of 99.9%.

Parkinson's Disease (PD) is a neurodegenerative disease that predominantly alter patients' motor performance and compromises the speed, the automaticity and fluidity of natural movements. The patients fluctuate between periods in which they can move almost normally for some hours (ON state) and periods with motor disorders (OFF state). Gait properties are affected by the motor state of a patient: reduced stride length, reduced gait speed, increased stride width etc. The ability to assess the motor states (ON/OFF) on a continuous basis for long time without disturbing the patients' daily life activities is an important component of PD management. An accurate report of motor states could allow clinics to adjust the medication regimen to avoid OFF periods. The real-time monitoring will also allow an online treatment by combining, for instance, with automatic drug-administration pump doses. Many studies have attempted to extract gait properties through a belt-worn single tri-axial accelerometer. In this paper, a user friendly position is proposed to place the accelerometer and six step length estimators are compared considering the proposed sensor placement in a preliminary database of healthy volunteers. Adaptation methods to some of these estimators are also proposed and compared. The comparison shows that the adapted estimators improve the performance and reduce errors in respect of the original methods applied in the new sensor location.

Parkinson's Disease (PD) is a neurodegenerative disease that alters the patients' motor performance. Patients suffer many motor symptoms: bradykinesia, dyskinesia and freezing of gait, among others. Furthermore, patients alternate between periods in which they are able to move smoothly for some hours (ON state), and periods with motor complications (OFF state). An accurate report of PD motor states and symptoms will enable doctors to personalize medication intake and, therefore, improve response to treatment. Additionally, real-time reporting could allow an automatic management of PD by means of an automatic control of drug-administration pump doses. Such a system must be able to provide accurate information without disturbing the patients' daily life activities. This paper presents the results of the MoMoPa study classifying motor states and dyskinesia from 20 PD patients by using a belt-worn single tri-axial accelerometer. The algorithms obtained will be validated in a further study with 15 PD patients and will be enhanced in the REMPARK project.

Most recent therapeutic solutions to treat Parkinson's disease seek continuous administration of dopaminergic agonists, as for example rigotine patches or apomorphine infusion pumps. Such drug-delivery devices are aimed at preventing fluctuations in drug plasma levels, which could cause certain symptoms such as wearing-off periods or dyskinesia. However, we postulate that drug plasma levels should not keep constant, but rather adjust to the varying intensity of the different user's activities. The rationale behind this is that the drug amount appropriate to treat a patient at rest is lower than that required to treat the same patient when engaged in physical activity. We propose dynamic real-time dose adjustment, so that the doses increase as the patient starts performing physical activity, thus preventing off periods such as "freeze" phenomenon, and the doses reduce during the resting periods, thus preventing adverse effects. Small portable movement sensors are currently available, which detect the amount and type of activity in a continuous way. Combining such technology with infusion pumps to produce modified pumps capable of adjusting the infusion rate to the user's activity, seems to be feasible in the short-term.

Background Information and communications technologies (ICT) are being widely used in healthcare system. However, the real challenge is demonstrate their utility. Falls in the elderly and their consequences are increasing. We made an intervention to facilitate understanding of the user9s experience in the context of a European research (The Fall Detector for the Elderly [FATE]) project to validate fall-detecting technology. FATE is a device that automatically detects falls in the elderly. Objectives To extract information on patients9 experience of the FATE system, a fall-detecting device that includes a mobile telephone and a fall-warning system, focusing on utility, and users9 satisfaction. Methods Type of intervention: Guided group dynamics. Theoretical framework: systematic use of active learning, design thinking, role-playing and thematic analysis. Scenarios: The experience was developed using 3 simulated scenarios that included patients, relatives and investigators. Sample: Twenty-five patients currently using the device were included. Mean age 76 (SD 7) years, BMI 30 (DE 6). All patients had ≥3 chronic condition of which the most frequent were osteoarthritis (100%) hypertension (55%), osteoporosis (46%), depression (45.5%), urinary incontinency (36%), high cholesterol (32%), medications mean 6 (DE 2.3). Timed Up and Go (TUG) test 20.4 (DE 5) seconds (80% ≥13.5 seconds). Group dynamics: Five participants were selected from the 25 patients. Five unrelated relatives were chosen. Three investigators actively participated only in the third and final stage of the dynamics1,2. Results Areas for improvement. These included reducing the distance between the mobile phone and the warning-alarm device, improving the device9s sensitivity, improving teaching of handling of the mobile and 17 other recommendations which can be summed up as: Coverage: Faster, easier access to device functions; measurement of other health-related data; concentration/expansion of access points. Sensitivity: Adjustments in sensitivity of the response; increased autonomy and energy balance; adaptation of the alarm system (sound + lighting + message). Safety: Incorporation of customer service; dynamic customization; more-precise attachment and antitheft system. Control: Reinforcing training and tutorials; increased social transparency and dissemination of knowledge; implementation of study on the psycho-sociological impact. Integration: unification of the device in a single appliance; application of trust-building systems; involvement of all stakeholders in research. Conclusions Users and relatives, in a proactive process using simulated scenarios, identified problems, solutions and areas for improvement. This collaborative work produced valuable solutions better adapted to the real needs of users. References http://pracmatic.net/patient-empowerment/ http://youtu.be/dLYw984FiYQ Acknowledgements The FATE project has been funded by CIPICT-PSP-2011-5 297178 Disclosure of Interest None declared

10 pages, 7 figures, 4 tables, The present study reviewed some of the critical pre-processing steps required for otolith shape characterisation for automatic classification with heterogeneous distributed data. A common procedure for optimising automatic classification is to apply data pre-processing in order to reduce the dimension of vector inputs. One of the key aspects of these pre-processing methods is the type of codification method used for describing the otolith contour. Two types of codification methods (Cartesian and Polar) were evaluated, and the limitations (loss of information) and the benefits (invariance to affine transformations) associated with each method were pointed out. The comparative study was developed using four types of shape descriptors (morphological, statistical, spectral and multiscale), and focused on data codification techniques and their effects on extracting shape features for automatic classification. A new method derived from the Karhunen–Loève transformation was proposed as the main procedure for standardising the codification of the otolith contours., The present study was funded by the IBACS project (European Union Project QLRT-2001-01610), by the Spanish project AVG-ION (McyT-TIC2000-0376-p4-04) from the Spanish Ministry of Science and Technology and by the IBACS European project.

Background Assessment of pain and physical function is complex in patients with knee osteoarthritis (OA), as standard criteria are lacking.A previous study examining correlations between functional capacity and pain (WOMAC) and anthropometric characteristics and periarticular knee structure (quantified by ultrasound imaging) in females with knee OA found increased quadriceps muscle density was associated with higher functional disability and pain scores, suggesting that not only joint wear and symptom severity are involved and more objective measures are necessary. Objectives To determine and compare the periarticular knee structure in obese patients with knee OA and a healthy control group. Methods Analytical case-control study. Study group. Patients diagnosed with knee OA. Control group . Adults with no history of knee involvement, able to walk normally, with no pain or functional difficulties on examination and no history of surgery in other lower limb joints. Controls were matched for age, sex and body mass index (BMI). Sociodemographic, clinical, functional (Timed Up and Go test [TUG]) and anthropometric (weight, height, BMI, waist circumference, and lower limb [suprapatellar and infrapatellar indices]) data were collected. Periarticular knee structure was assessed by ultrasound (thickness of subcutaneous fat [distance from skin to fascia, in mm] and quadriceps/rectus femoris [distance between fascia and femur, in mm]) and appearance [density on digital image analysis according to Maurits et al]). Statistical Analysis . Groups were compared using the t test for continuous variables and χ 2 test for categorical variables. Results 66 lower limbs from 14 patients (mean age 62.7 [SD 8.6]) years, BMI 30.4 (SD 5.9) and 19 matched controls (mean age 62.6 [SD 8.1] years, BMI 30.1 [SD 4.7]) were evaluated. Comparison between groups: no significant differences in anthropometric measures were found. TUG took a mean 13.7s (6.7) and 9.9s (2.4) in patients and controls, respectively, p=0.002. Mean subcutaneous fat was 18.7 (SD 9.8) mm and 15.2 (4.41) in patients and controls, respectively, p=0.028. Mean quadriceps muscle density was 61.1 (25.9) and 41.7 (13.7), respectively, p=0.001. Conclusions Between-group differences were found in the periarticular knee structure. Patients with knee OA had increased subcutaneous fat thickness and quadriceps muscle density was observed compared with controls. These findings suggest that the assessment of periarticular structures in these patients analyzed by digital image derived from ultrasound could add a variable to determine more objectively uniform methods in the classification of patients and evaluation of results. References Maurits NM et al. Muscle ultrasound analysis: normal values and differentiation between myopathies and neuropathies. Ultrasound Med Biol 2003;29:215-25. Disclosure of Interest None declared DOI 10.1136/annrheumdis-2014-eular.3136

Otoliths are calcified structures in the inner ear of fish. The otolith shape changes during a fish's lifetime are particular to individual species. Then, otolith shape can be used to differentiate between species and between fish of the same species. Fishery research has used the growth patterns (i.e. rings) found in these calcified structures to estimate the age of individual fish. However, many factors, such as seasonal variations, temperature, habitat and food, may influence otolith growth. Then, the manual classification of otoliths remains a difficult task, and even experienced examiners can give inaccurate age estimation. We propose to use statistical learning techniques (artificial neural networks) to improve and automate the process. ANN classification methods are evaluated and used with some real otolith databases, giving significant results

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-59147-6_30 Freezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.

Bradykinesia is a cardinal symptom of Parkinson's disease (PD) and describes the slowness of movement revealed in patients. Current PD therapies are based on dopamine replacement, and given that bradykinesia is the symptom that best correlates with the dopaminergic deficiency, the knowledge of its fluctuations may be useful in the diagnosis, treatment and better understanding of the disease progression. This paper evaluates a machine learning method that analyses the signals provided by a triaxial accelerometer placed on the waist of PD patients in order to automatically assess bradykinetic gait unobtrusively. This method employs Support Vector Machines to determine those parts of the signals corresponding to gait. The frequency content of strides is then used to determine bradykinetic walking bouts and to estimate bradykinesia severity based on an epsilon-Support Vector Regression model. The method is validated in 12 PD patients, which leads to two main conclusions. Firstly, the frequency content of the strides allows for the dichotomic detection of bradykinesia with an accuracy higher than 90%. This process requires the use of a patient-dependant threshold that is estimated based on a leave-one-patient-out regression model. Secondly, bradykinesia severity measured through UPDRS scores is approximated by means of a regression model with errors below 10%. Although the method has to be further validated in more patients, results obtained suggest that the presented approach can be successfully used to rate bradykinesia in the daily life of PD patients unobtrusively. A method is proposed to detect and rate bradykinetic gait in an unobtrusive way.A waist-worn triaxial accelerometer, SVM and strides' frequency content are employed.Detection accuracy based on a leave-one-patient-out (LOPO) evaluation is 92%.Severity estimation error through LOPO is ~10%.Results suggest the method can unobtrusively rate bradykinesia in patients' daily life.

Altered movement control is typically the first noticeable symptom manifested by Parkinson’s disease (PD) patients. Once under treatment, the effect of the medication is very patent and patients often recover correct movement control over several hours. Nonetheless, as the disease advances, patients present motor complications. Obtaining precise information on the long-term evolution of these motor complications and their short-term fluctuations is crucial to provide optimal therapy to PD patients and to properly measure the outcome of clinical trials. This paper presents an algorithm based on the accelerometer signals provided by a waist sensor that has been validated in the automatic assessment of patient’s motor fluctuations (ON and OFF motor states) during their activities of daily living. A total of 15 patients have participated in the experiments in ambulatory conditions during 1 to 3 days. The state recognised by the algorithm and the motor state annotated by patients in standard diaries are contrasted. Results show that the average specificity and sensitivity are higher than 90%, while their values are higher than 80% of all patients, thereby showing that PD motor status is able to be monitored through a single sensor during daily life of patients in a precise and objective way.

In order to extract learning algorithms from living neural aggregates it would be advantageous to achieve one-to-one neuron-electrode interfacing with in vitro networks. Towards this goal, we have developed a hybrid glass-elastomer technology, which allows topology specification in small networks (of the order of 10 neurons) and recording of extracellular potentials from individual neurites grown through microfluidic channels. Here we report on progress towards adhesion-free placement of cells within microwells, promotion of neurite growth and recording of intra-channel extracellular spikes.

Background: Our group earlier developed a small monitoring device, which uses accelerometer measurements to accurately detect motor fluctuations in patients with Parkinson’s (On and Off state) based on an algorithm that characterizes gait through the frequency content of strides. To further validate the algorithm, we studied the correlation of its outputs with the motor section of the Unified Parkinson’s Disease Rating Scale part-III (UPDRS-III). Method: Seventy-five patients suffering from Parkinson’s disease were asked to walk both in the Off and the On state while wearing the inertial sensor on the waist. Additionally, all patients were administered the motor section of the UPDRS in both motor phases. Tests were conducted at the patient’s home. Convergence between the algorithm and the scale was evaluated by using the Spearman’s correlation coefficient. results: Correlation with the UPDRS-III was moderate (rho -0.56; p < 0.001). Correlation between the algorithm outputs and the gait item in the UPDRS-III was good (rho -0.73; p < 0.001). The factorial analysis of the UPDRS-III has repeatedly shown that several of its items can be clustered under the so-called Factor 1: “axial function, balance, and gait.” The correlation between the algorithm outputs and this factor of the UPDRS-III was -0.67 (p < 0.01). conclusion: The correlation achieved by the algorithm with the UPDRS-III scale suggests that this algorithm might be a useful tool for monitoring patients with Parkinson’s disease and motor fluctuations.

A nonlinear compartmental model is developed for the HIV detection system in Cuba with different types of detections, some random and others non-random. We analyze the dynamics of this system, compute the reproduction numbers, and use the data from the Cuban HIV/AIDS epidemic between 1986-2008 to fit the model. We obtain estimates for the detection-related parameters during two separate time periods to reflect the timeline of the implementation of various types of searches. The reproduction numbers for each time period are also computed from the sets of values of the parameters. We found that random screening is most important as a mean of surveillance. Moreover, local asymptotic stability for the Disease Free Equilibrium can be achieved if (i) random screening is sufficiently effective and (ii) infection by detected HIV-positive individuals is minimal. Our results highlight the importance of education for the known infectious for the purpose of preventing further infection. Fitting the 1986-2008 HIV data to obtain the model parameter estimates indicates that the HIV epidemic in Cuba is currently approaching an endemic equilibrium. A Genetic Algorithm is used.

This work presents the Autorregresive switching-Markov Model (AR-HMM) as a technique that allows modelling time series which are controlled by some unobserved process and finite time lags. Our objetive is to bring to light the potential of this method to give valuable information about how an efficient control strategy can be performed. As a case of study, we apply the method to the dengue fever epidemics (DF) in 2001 in Havana. For this time series, a first experiment with real data is performed in order to obtain the characterization of differents stages of the epidemics.