Motion symmetry evaluation using accelerometers and energy distribution

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use ofmicroelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities. © 2019 by the authors.Ministry of Health of the Czech Republic [FN HK 00179906]; Charles University in Prague, Czech Republic [PROGRES Q40]; project PERSONMED - European Regional Development Fund (ERDF) [CZ.02.1.010.00.017_0480007441]; governmental budget of the Czech Republi

Cycling segments multimodal analysis and classification using neural networks

This paper presents methodology for the processing of GPS and heart rate signals acquired during long-term physical activities. The data analysed include geo-positioning and heart rate multichannel signals recorded for 272.2 h of cycling across the Andes mountains over a 5694-km long expedition. The proposed computational methods include multimodal data de-noising, visualization, and analysis in order to determine specific biomedical features. The results include the correspondence between the heart rate and slope for downhill and uphill cycling and the mean heart rate evolution on flat segments: a regression coefficient of -0.014 bpm/h related to time and 6.3 bpm/km related to altitude. The classification accuracy of selected cycling features by neural networks, support vector machine, and k-nearest neighbours methods is between 91.3% and 98.6%. The proposed methods allow the analysis of data during physical activities, enabling an efficient human-machine interaction. © 2017 by the authors.Cancer Research U

Breathing analysis using thermal and depth imaging camera video records

The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values −0.16°C/min and −0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.Department of Neurology, University of Pittsburg

The effect of face masks on physiological data and the classification of rehabilitation walking

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k-nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.[LTAIN19007

Blood oxygen concentration and physiological data changes during motion while wearing face masks

The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions

Lymphocyte populations and their change during five-year glatiramer acetate treatment

Background The goal of this study was to determine the characteristics that are affected in patients treated with glatiramer acetate (GA). Methods A total of 113 patients were included in this study. Patients were treated with glatiramer acetate (subcutaneous injection, 20 mg, each day). Peripheral blood samples were obtained just prior to treatment as well as 5 years after GA treatment. All the calculations were performed with the statistical system R (r-project.org). Results After 5 years of treatment, a significant decrease was found in the absolute and relative CD3+/CD69+ counts, the absolute and relative CD69 counts, the relative CD8+/CD38+ count and the relative CD38 count. A significant increase was found in the absolute and relative CD5+/CD45RA+ counts and the absolute CD5+/CD45RO+ count after 5 years of treatment. Conclusion This study presents some parameters that were affected by long-term GA treatment

Linear and Nonlinear EEG Synchronization in Alzheimer’s Disease

As is known, Alzheimer’s disease (AD) is associated with cognitive deficits due to significant neuronal loss. Reduced connectivity might be manifested as changes in the synchronization of electrical activity of collaborating parts of the brain. We used wavelet coherence to estimate linear/nonlinear synchronization between EEG samples recorded from different leads. Mutual information was applied to the complex wavelet coefficients in wavelet scales to estimate nonlinear synchronization. Synchronization rates for a group of 110 patients with moderate AD (MMSE score 10 to 19) and a group of 110 healthy control subjects were compared. The most significant decrease in mutual information in AD patients was observed on the third scale in the fronto-temporal area and for wavelet coherence within the same areas as for mutual information; these areas are preferentially affected by atrophy in AD. The new method used utilizes mutual information in wavelet scales and demonstrates larger discriminatory values in AD compared to wavelet coherence.Як відомо, хвороба Альцгеймера (ХА) пов’язана з прогресуючим когнітивним дефіцитом у результаті істотної загибелі нейронів. Зменшення міжнейронних зв’язків може проявлятись як зміни ступеню синхронізації електричної активності взаємодіючих мозкових структур. Ми використовували методику оцінки вейвлет-когерентності для оцінки лінійної або нелінійної синхронізації зразків ЕЕГ, відведених від різних локусів кори. Визначення індексів взаємної інформації використовувалося для оцінки нелінійної синхронізації згідно з комплексними вейвлет-коефіцієнтами за вейвлет-шкалами. Було порівняно ступені синхронізації ЕЕГ-активності в групі пацієнтів, що страждали на ХА помірної тяжкості (оцінки за MMSE від 10 до 19 балів), та в групі із 110 контрольних здорових суб’єктів. Найістотніші зменшення індексів взаємної інформації у пацієнтів із ХА спостерігалися по третій шкалі для фронто-темпоральної зони; зменшення вейвлет-когерентності відзначались у тих самих зонах, що й зміни взаємної інформації. Саме ці зони зазнають переважної атрофії при ХА. Використаний новий метод базується на оцінках взаємної інформації за вейвлет-шкалами та демонструє більшу дискримінаційну здатність в умовах ХА, аніж визначення вейвлет-когерентності

Evaluation of accelerometric and cycling cadence data for motion monitoring

Motion pattern analysis uses methods for the recognition of physical activities recorded by wearable sensors, video-cameras, and global navigation satellite systems. This paper presents the motion analysis during cycling, using data from a heart rate monitor, accelerometric signals recorded by a navigation system, and the sensors of a mobile phone. The set of real cycling experiments was recorded in a hilly area with each route about 12 km long. The associated signals were analyzed with appropriate computational tools to find the relationships between geographical and physiological data including the heart rate recovery delay studied as an indicator of physical and nervous condition. The proposed algorithms utilized methods of signal analysis and extraction of body motion features, which were used to study the correspondence of heart rate, route profile, cycling speed, and cycling cadence, both in the time and frequency domains. Data processing included the use of Kohonen networks and supervised two-layer softmax computational models for the classification of motion patterns. The results obtained point to a mean time of 22.7 s for a 50 % decrease of the heart rate after a heavy load detected by a cadence sensor. Further results point to a close correspondence between the signals recorded by the body worn accelerometers and the speed evaluated from the GNSSs data. The accuracy of the classification of downhill and uphill cycling based upon accelerometric data achieved 93.9 % and 95.0 % for the training and testing sets, respectively. The proposed methodology suggests that wearable sensors and artificial intelligence methods form efficient tools for motion monitoring in the assessment of the physiological condition during different sports activities including cycling, running, or skiing. The use of wearable sensors and the proposed methodology finds a wide range of applications in rehabilitation and the diagnostics of neurological disorders as well. AuthorResearch through the Development of Advanced Computational Algorithms for Evaluating Post-Surgery Rehabilitation [LTAIN19007]; National Sustainability Programme of the Ministry of Education, Youth and Sports of the Czech Republic [LO1303 (MSMT-7778/2014)]; Ethics commission, Neurocentre Caregroup, Center for Neurological Care in Rychnov nad Kneznou, Czech RepublicMinisterstvo Školství, Mládeže a Tělovýchovy, MŠMT: LO1303, MSMT-7778/201

Mechanical recanalization in ischemic anterior circulation stroke within an 8-hour time window: a real-world experience

PURPOSE:We aimed to assess the safety and effectiveness of mechanical recanalization in patients with ischemic stroke in the anterior circulation within 8 h since symptoms onset and with unknown onset time. We compared time intervals <6 h vs. 6–8 h/unknown onset time, as only limited data are available for a time window beyond 6 h.METHODS:Our cohort included 110 consecutive patients (44 males; mean age, 73.0±11.5 years) with ischemic stroke in the anterior circulation due to the acute occlusion of a large intracranial artery who underwent mechanical recanalization within an 8-hour time window or with unknown onset time. All patients underwent unenhanced computed tomography (CT) of the brain, CT angiography of the cervical and intracranial arteries and digital subtraction angiography. Perfusion CT was performed in patients beyond a 6-hour time window/with unknown onset time. We collected the following data: baseline characteristics, presence of risk factors, neurologic deficit at the time of treatment, time to therapy, recanalization rate, and 3-month clinical outcome. Successful recanalization was defined as Thrombolysis in Cerebral Infarction score of 2b/3 and good clinical outcome as modified Rankin scale value of 0–2 points.RESULTS:Successful recanalization was achieved in 82 patients (74.5%): in 61 patients treated within 6 h (73.5%), 7 patients treated within 6–8 h (63.6%), and 13 patients with unknown onset time (81.3%). Good 3-month clinical outcome was achieved in 61 patients (55.5%): in 46 patients treated within 6 h (55.4%), 5 patients treated within 6–8 h (45.5%), and 10 patients with unknown onset time (62.5%). Recanalization success or clinical outcome were not significantly different between patients treated at different time windows.CONCLUSION:Our data confirms the safety and effectiveness of mechanical recanalization performed in carefully selected patients with ischemic stroke in the anterior circulation within 8 h of stroke onset or with unknown onset time in everyday practice

Data S1: Data

We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device