Moodyjensen8870

Peripheral vascular flow in response to induced reactive hyperemia of the radial artery is used as a benchmark for non-invasive assessment of the endothelial function. As an alternative to standard modalities, this study investigates the suitability of impedance plethysmography to estimate peripheral vascular flow variations associated with the reactive hyperemia process. Results indicate a consistent variation of bio-impedance during the reactive hyperemia process at higher measurement frequencies and these variations are compatible with a standard tissue impedance model. Further, calculated features of bioimpedance has shown the capability of differentiating healthy and diabetic groups which is useful in estimating the endothelial dysfunction.We have traditionally defined `loss of consciousness' (LOC) and `regain of consciousness' (ROC) during general anesthesia in terms of behavioral correlates. We are starting to understand the dynamics in brain activity that may help define those events; however, we have not yet explored the possible autonomic correlates of LOC and ROC. In this study, we investigated the autonomic dynamics immediately surrounding loss and regain of consciousness in nine healthy volunteers under controlled propofol sedation. find more We used multimodal autonomic indices generated from physiologically accurate models and found that just before and after LOC and ROC could be differentiated with an AUC of 0.80. In addition, we saw that some of the autonomic changes accompanying LOC and ROC verify known information about the mechanism of action of propofol, while others indicate new avenues for exploration of propofol's effect on the autonomic nervous system. Overall, our work suggests that the autonomic dynamics surrounding the events of loss and regain of consciousness are worthy of further investigation.Clinical Relevance-This introduces the possibility of autonomic biomarkers for loss and regain of consciousness during general anesthesia that are more precise than behavioral tracking alone.The progression of neurodegenerative conditions can be effectively monitored and improved by using objective assessments. The conditions such as Friedreich Ataxia (FA) are clinically assessed by means of subjective measures commonly practised in clinics. Here, we propose a device capable of measuring ataxia, in the form of a `cup' capable of sensing certain kinematic parameters of interest while engaging in an activity that is closely related to daily living. In this study, the functional task of 'drinking' was utilised to diagnose participants with FA and capture features in terms of diagnosis (separation) and correlation with the clinical scales. Frequency domain analysis was incorporated enabling the classification of control subjects and FA patients to an accuracy of 88% with a correlation of 90% with the clinical scores.Human observer-based assessments of Cerebellar Ataxia (CA) are subjective and are often inadequate to track mild motor symptoms. This study examines the potential use of a comprehensive sensor-based approach for objective evaluation of CA in five domains (speech, upper limb, lower limb, gait and balance) through the instrumented versions of nine bedside neurological tests. A total of twenty-three participants diagnosed with CA to varying degrees and eleven healthy controls were recruited. Data was collected using wearable inertial sensors and Kinect camera. In our study, an optimal feature subset based on feature importance in the Random Forest classifier model demonstrated an impressive performance accuracy of 97% (F1 score = 95.2%) for CA-control discrimination. Our experimental findings also indicate that the Romberg test contributed most, followed by the peripheral tests, while the Gait test contributed least to the classification. Sensor-based approaches, therefore, have the potential to complement existing clinical assessment techniques, offering advantages in terms of consistency, objectivity and informed clinical decision-making.The incidence of fall-related injuries in older adults is high. Given the significant and adverse outcomes that arise from injurious falls in older adults, it is of the utmost importance to identify older adults at greater risk for falls as early as possible. Given that balance dysfunction provides a significant risk factor for falls, an automated and objective identification of balance dysfunction in community dwelling older adults using wearable sensor data when walking may be beneficial. In this study, we examine the feasibility of using wearable sensors, when walking, to identify older adults who have trouble with balance at an early stage using state-of-the-art machine learning techniques. link2 We recruited 21 community dwelling older women. The experimental paradigm consisted of two tasks Normal walking with a self-selected comfortable speed on an instrumented treadmill and a test of reflexive postural response, using the motor control test (MCT). Based on the MCT, identification of older women with low or high balance function was performed. Using short duration accelerometer data from sensors placed on the knee and hip while walking, supervised machine learning was carried out to classify subjects with low and high balance function. Using a Gradient Boosting Machine (GBM) algorithm, we classified balance function in older adults using 60 seconds of accelerometer data with an average cross validation accuracy of 91.5% and area under the receiver operating characteristic curve (AUC) of 0.97. Early diagnosis of balance dysfunction in community dwelling older adults through the use of user friendly and inexpensive wearable sensors may help in reducing future fall risk in older adults through earlier interventions and treatments, and thereby significantly reduce associated healthcare costs.Frailty and falls are the main causes of morbidity and disability in elderly people. The Timed Up-and-Go (TUG) test has been proposed as an appropriate method for evaluating elderly individuals' risk of falling. To analyze the TUG's potential for falls prediction, we conducted a clinical study with participants aged ≥ 65 years, living in nursing homes. We harvested 138 TUG recordings with the information, if patients used a walking aid or not and developed a method to predict the use of walking aids using a Random Forest Classifier for ultrasonic based TUG test recordings. We achieved a high accuracy with an Area Under the Curve (AUC) of 96,9% using a 20% leave out evaluation strategy. Automated collection of structured data from TUG recordings - like the use of a walking aid - may help to improve fall risk tools in future.Motion rehabilitation is increasingly required owing to an aging population and suffering of stroke, which means human motion analysis must be valued. Based on the concept mentioned above, a deep-learning-based system is proposed to track human motion based on three-dimensional (3D) images in this work; meanwhile, the features of traditional red green blue (RGB) images, known as two-dimensional (2D) images, were used as a comparison. The results indicate that 3D images have an advantage over 2D images due to the information of spatial relationships, which implies that the proposed system can be a potential technology for human motion analysis applications.

Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases.

In this paper, we compared two techniques wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals.

Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained bymay use it interchangeably to process signals from the gait of Parkinson's disease patients to support diagnosis and follow up made by a clinical expert.

Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.Clinical Relevance- this establishes that peaks and wavelet techniques are comparable and may use it interchangeably to process signals from the gait of Parkinson's disease patients to support diagnosis and follow up made by a clinical expert.At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson's disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson's would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson's disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson's disease dataset comprised of healthy-elderly, healthy-young and Parkinson's disease patients. Our code is available at https//github.com/itsmeafra/Sublevel-Set-TDA.The analysis of gait data is one approach to support clinicians with the diagnosis and therapy of diseases, for example Parkinson's disease (PD). link3 Traditionally, gait data of standardized tests in the clinic is analyzed, ensuring a predefined setting. In recent years, long-term home-based gait analysis has been used to acquire a more representative picture of the patient's disease status. Data is recorded in a less artificial setting and therefore allows a more realistic perception of the disease progression. However, fully unsupervised gait data without additional context information impedes interpretation. As an intermediate solution, performance of gait tests at home was introduced. Integration of instrumented gait test requires annotations of those tests for their identification and further processing. To overcome these limitations, we developed an algorithm for automatic detection of standardized gait tests from continuous sensor data with the goal of making manual annotations obsolete. The method is based on dynamic time warping, which compares an input signal with a predefined template and quantifies similarity between both.