In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ON (TM), a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ON (TM) since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ON (TM) are presented.

Introduction: Stroke is the second most common cause of adult death in Africa. This study reports the demographics, stroke types, stroke care and hospital outcomes for stroke in Freetown, Sierra Leone.Methods: A prospective observational register recorded all patients 18 years and over with stroke between May 2019 and April 2020. Stroke was defined according to the WHO criteria. Pearson's chi-squared test was used to examine associations between categorical variables and unpaired t-tests for continuous variables. Multivariable logistic regression, to explain in-hospital death, was reported as odds ratios (ORs) and 95% confidence intervals.Results: Three hundred eighty-five strokes were registered, and 315 (81.8%) were first-in-a-lifetime events. Mean age was 59.2 (SD 13.8), and 187 (48.6%) were male. Of the strokes, 327 (84.9%) were confirmed by CT scan. Two hundred thirty-one (60.0%) were ischaemic, 85 (22.1%) intracerebral haemorrhage, 11 (2.9%) subarachnoid haemorrhage and 58 (15.1%) undetermined stroke type. The median National Institutes of Health Stroke Scale on presentation was 17 [interquartile range (IQR) 9–25]. Haemorrhagic strokes compared with ischaemic strokes were more severe, 20 (IQR 12–26) vs. 13 (IQR 7–22) (p < 0.001), and occurred in a younger population, mean age 52.3 (SD 12.0) vs. 61.6 (SD 13.8) (p < 0.001), with a lower level of educational attainment of 28.2 vs. 40.7% (p = 0.04). The median time from stroke onset to arrival at the principal referral hospital was 25 hours (IQR 6–73). Half of the patients (50.4%) sought care at another health provider prior to arrival. One hundred fifty-one patients died in the hospital (39.5%). Forty-three deaths occurred within 48 hours of arriving at the hospital, with median time to death of 4 days (IQR 0–7 days). Of the patients, 49.6% had ≥1 complication, 98 (25.5%) pneumonia and 33 (8.6%) urinary tract infection. Male gender (OR 3.33, 1.65–6.75), pneumonia (OR 3.75, 1.82–7.76), subarachnoid haemorrhage (OR 43.1, 6.70–277.4) and undetermined stroke types (OR 6.35, 2.17–18.60) were associated with higher risk of in-hospital death.Discussion: We observed severe strokes occurring in a young population with high in-hospital mortality. Further work to deliver evidence-based stroke care is essential to reduce stroke mortality in Sierra Leone.

In this article, the Kalman filter, Mahony filter and Madgwick filter are implemented to estimate the orientation from inertial data, using an IMU called 9 × 3 of the MoMoPa3 project which contain various sensors including a gyroscope, an accelerometer and a magnetometer, each one of them, equipped with three perpendicular axes, in the magnetometer the measurement was modified to correct the distortions by hard metals, demonstrating improvements in the accuracy of the orientation estimates. In addition, the Kinovea video analyzer software is used as reference and gold standard to calculate the Root-Mean-Square Error (RMSE) with each filter. When comparing the angles estimated by the filters with those obtained from Kinovea, it was observed that one of the filters was better in performance. The information obtained in this article can be involved in several fields of science, one of the most important in the field of medicine, helping to control Parkinson’s disease since it allows to evaluate and recognize when a patient suffers a fall or presents Freezing of the gait (FOG).

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.

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.

Freezing of gait (FoG) is one of the most disturbing and incapacitating symptoms in Parkinson's disease. It is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown to be not reliable, since it is subjective due to its dependence on patients' and caregivers' judgment. Several authors have analyzed the usage of MEMS inertial systems to detect FoG with the aim of objectively evaluating it. So far, specific methods based on accelerometer's frequency response has been employed in many works; nonetheless, since they have been developed and tested in laboratory conditions, their performance is commonly poor when being used at patients' home. Therefore, this work proposes a new set of features that aims to detect FoG in real environments by using accelerometers. This set of features is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. In addition, a greedy subset selection process is performed to reduce the computational load of the method and to enable a real-time implementation. Results show that the proposed method detects FoG at patients' home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives. (c) 2017 Elsevier B.V. Allrightsreserved.

Posture transitions are one of the most mechanically demanding tasks and are useful to evaluate the motor status of patients with motor impairments, frail individuals or the elderly, among others. So far, wearable inertial systems have been one of the most employed tools in the study of these movements due to their suitable size and weight, being non-invasive systems. These devices are mainly composed of accelerometers and, to a lesser extent, gyroscopes, magnetometers or barometers. Although accelerometers provide the most reliable measurement, detecting activities where a change of altitude is observed, such as some posture transitions, may require additional sensors to reliably detect these activities. In this work, we present an algorithm that combines the information of a barometer and an accelerometer to detect posture transitions and falls. In contrast to other works, we test different activities (where altitude is involved) in order to achieve a reliable classifier against false positives. Furthermore, by means of feature selection methods, we obtain optimal subsets of features for the accelerometer and barometer sensors to contextualise these activities. The selected features are tested through several machine learning classifiers, which are assessed with an evaluation data set. Results show that the inclusion of barometer features in addition to those obtained for an accelerometer clearly enhances the detection accuracy up to a 11%, in terms of geometric mean between sensitivity and specificity, compared to algorithms where only the accelerometer is used. Finally, we have also analysed the computer burden; in this sense, the usage of barometers, in addition to increase the accuracy, also reduces the computational resources required to classify a new pattern, as shown by a reduction in the number of support vectors.

A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods).The goal of this study was to analyze the accuracy of this algorithm under real conditions of use.This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard.The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%.The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting.

[This corrects the article on p. 431 in vol. 8, PMID: 28919877.].

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.

In this paper we use the idea of conceptual space introduced by Boden and redefine some properties such appropriateness and relevance that facilitate the computational implementation of the transformational creativity mechanism. While appropriateness can only be evaluated by an expert, relevance can be objectively measured for any spectator. Computational creativity is based on the relationship between appropriateness and relevance of a concept, and therefore a computational system can be used to support this task. The paper analyses this relationship in the field of music in order to obtain a computer tool to support the musical composition task.

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.

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 3days 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.

In the previous chapters, the REMPARK system has been presented and discussed, along with its usefulness. According to the initial project specifications and some important conclusions obtained from the work carried out during the project, the main characteristics and requirements of the integrating parts and REMPARK sub-systems were initially defined and refined afterwards. The present chapter will first describe the final system and, then, its overall final assessment. This way, first, a description of the communication flows established in the REMPARK platform (type of information, dataflow and security issues) is given. Secondly, an overview of the final necessary platform, ready to be used in the piloting part of the project is provided. The assessment process was done through a pilot in which the patients tested the system in ambulatory conditions and during their daily living activities. More than 40 PD patients participated from four countries (Spain, Italy, Israel and Ireland).

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.

This chapter describes the methodology developed and used to obtain the elements of the REMPARK system devoted to monitor the related PD motor symptoms, according to the specifications discussed in the previous chapter. The monitoring part basically consists in the waist-worn device and its embedded algorithms for the analysis of the PD patients’ movement and gait. The development of such a device to assess motor symptoms has been divided into several steps that are summarised below: • First, different questionnaires were administered to professionals and cargiveers to identify some system requirements and the most important symptoms to be monitored. This step has been already partially presented and used for the purpose of Chapter 3. The present chapter describes the details of these questionnaires and the obtained results. • A methodology was developed to detect these symptoms, which is based on inertial sensors and machine learning techniques. This methodology is presented in Section 4.3. • A database of signals was collected in order to develop automatic detection methods based on the mentioned methodology. The design of the experiment to collect such data and a summary of the data obtained are also detailed in Section 4.3.

Posture transitions (PT) are important movements among the activities performed in daily life of older adults. Their analysis provides information related to the amount of activity performed by a patient over a day and, furthermore, they are useful for assessing symptoms in some movement disorders such as Parkinson's disease. Many research works have attempted to automatically identify PT relying on the use of machine learning algorithms and light and small accelerometers, since they might be embedded into wearable systems, being unobtrusive for the users. However, distinguishing PTs through a single sensor results in complex classifiers requiring high computational resources, since some PT (such as Stand-to-Sit and Sit-to-Stand PT) may provide very similar acceleration signals. In this paper, we propose a barometer sensor with the aim of complementing the information provided by accelerometers. In addition, a hierarchical algorithm is presented, which is based on Support Vector Machines to detect PT including falls and Lying-to-Stand PT through a single sensor device. Results in 14 users show that the use of a barometer sensor enables the hierarchical algorithm to distinguish Sit-to-Stand from Stand-to-Sit transitions, and Falls from Lying-to-Stand with accuracies over 99%.

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.

Freezing of gait is one of the most disturbing and incapacitating symptoms in Parkinson's Disease. This is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown not to be reliable since it is subjective due to its dependence on patients' and caregivers' judgement. Several authors have analysed the usage of MEMS inertial systems to detect FoG with the aim of objective evaluate this symptom. So far, a threshold-based method based on accelerometer's frequency response has been employed in many works; nonetheless, since it has been developed and tested in laboratory conditions, it provides a much lower accuracy at patients' home. This work proposes a new set of features to detect FoG by using accelerometers, which is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. Results show that the proposed method detects FoG at patients' home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.