36,280 research outputs found

    Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

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    © 2015 Massé et al.Background: Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Methods: Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). Results: The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. Conclusion: The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier

    Fuzzy rule-based system applied to risk estimation of cardiovascular patients

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    Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

    Classification of sporting activities using smartphone accelerometers

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    In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach

    Recognition of elementary arm movements using orientation of a tri-axial accelerometer located near the wrist

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    In this paper we present a method for recognising three fundamental movements of the human arm (reach and retrieve, lift cup to mouth, rotation of the arm) by determining the orientation of a tri-axial accelerometer located near the wrist. Our objective is to detect the occurrence of such movements performed with the impaired arm of a stroke patient during normal daily activities as a means to assess their rehabilitation. The method relies on accurately mapping transitions of predefined, standard orientations of the accelerometer to corresponding elementary arm movements. To evaluate the technique, kinematic data was collected from four healthy subjects and four stroke patients as they performed a number of activities involved in a representative activity of daily living, 'making-a-cup-of-tea'. Our experimental results show that the proposed method can independently recognise all three of the elementary upper limb movements investigated with accuracies in the range 91–99% for healthy subjects and 70–85% for stroke patients

    Developing Predictive Models for Upper Extremity Post–Stroke Motion Quality Estimation Using Decision Trees and Bagging Forest

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    Stroke is one of the leading causes of long–term disability. Approximately twothirds of stroke survivors require long-term rehabilitation, which suggests the importance of understanding the post-stroke recovery process during his activities of daily living. This problem is formulated as quantifying and estimating the poststroke movement quality in real world settings. To address this need, we have developed an approach that quantifies physical activities and can evaluate the performance quality. Wearable accelerometer and gyroscope are used to measure the upper extremity motions and to develop a mathematical framework to objectively relates sensors’ data to clinical performance indices. In this article we employ two machine learning classification methods, Bootstrap Aggregating (Bagging) Forest and Decision Tree (DT), to relate the post-stroke kinematic data to quality of the corresponding motion. We then compare the accuracy of the resulted two prediction models using cross-validation approaches. Our findings indicate that Bagging forest approach is superior to the computationally simpler DTs for unstable data sets including those derived from stroke survivors in this project

    Predicting later categories of upper limb activity from earlier clinical assessments following stroke: An exploratory analysis

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    BACKGROUND: Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone\u27s subsequent UL performance category. PURPOSE: To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories. METHODS: This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance. RESULTS: A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification). CONCLUSIONS: UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA

    How Labor-Management Partnerships Improve Patient Care, Cost Control, and Labor Relations: Case Studies of Fletcher Allen Health Care, Kaiser Permanente, and Montefiore Medical Center’s Care Management Corporation

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    [Excerpt] This paper explores the ways in which healthcare unions and their members are strategically engaging with management through partnership to control costs and improve the patient experience, clinical outcomes, workplace environment, and labor relations. These initiatives depend on making use of the knowledge of front-line healthcare workers, improving communication between all staff members, and increasing transparency. In turn, these initiatives can also lead to more robust and dynamic local unions. Through participating in joint work activities, many union members note feeling more respected in their workplace and more connected to their union. Unions can benefit from these activities by offering their members the ability to inform decisions about how work gets done

    TOWARDS MONITORING WHEELCHAIR PROPULSION IN NATURAL ENVIRONMENT USING WEARABLE SENSORS

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    Due to lower limb paralysis, individuals with spinal cord injury (SCI) rely on their upper limbs for activities of daily living (ADLs) and wheelchair propulsion (WP). Previous research has found that specific biomechanical parameters of WP are associated with risk of UE pain and injury. However, the repetitiveness and quality of upper limb movements during WP are unclear. Recently, wearable sensors have been used to collect mobility characteristics of wheelchair users, but little research has looked into using them to monitor the quality of UE movements for WP in the natural environment. The purpose of this thesis was to develop and evaluate a WP monitoring device that can monitor wheelchair users’ activities, and propulsion parameters in the natural environment. This thesis is organized into three studies. The first study aims to develop activity classifiers that can distinguish WP episodes from a range of ADLs. Two classifying models were built using a Machine Learning (ML) technique. The model that yielded the highest accuracy showed an overall accuracy of 88.0%. Time spent on each activity was estimated based on the classifiers, and compared with the video observation. Percentage of difference between the criterion and estimated time ranged from 2.2% to 11.6%. The second study aims to estimate temporal parameters of WP, including the stroke number (SN) and push frequency (PF), using wearable sensors. The estimated SN and PF were compared with the criterion measures using the mean absolute errors (MAE) and mean absolute percentage of error (MAPE). Intraclass Correlation Coefficients were calculated to assess the agreement. The accelerometer placed on the upper arm yielded the highest accuracy with the MAPE of 8.0% for SN and 12.9% for PF. The third study aims to estimate wheelchair propulsion forces. Propulsion forces were estimated from the accelerometer placed on the upper arm using a bagging regression technique. The estimated forces were compared with the criterion. Mean absolute errors (MAE), mean absolute percentage of error (MAPE), were calculated. The results showed an overall MAPE of 17.9%. Intraclass Correlation Coefficients and Bland-Altman plots were used to assess the agreement between the criterion and the estimated force

    Are the dimensions of private information more multiple than expected? Information asymmetries in the market of supplementary private health insurance in England

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    Our study reexamines standard econometric approaches for the detection of information asymmetries on insurance markets. We claim that evidence based on a standard framework with 2 equations, which uses potential sources of information asymmetries, should stress the importance of heterogeneity in the parameters. We argue that conclusions derived from this methodology can be misleading if the estimated coefficients in such an `unused characteristics' framework are driven by different parts of the population. We show formally that an individual's expected risk from the perspective of insurance, conditioned on certain characteristics (which are not used for calculating the risk premium), can equal the population's expectation in risk { although such characteristics are both related to risk and insurance probability, which is usually interpreted as an indicator of information asymmetries. We provide empirical evidence on the existence of information asymmetries in the market for supplementary private health insurance in the UK. Overall, we found evidence for advantageous selection into the private risk pool; ie people with lower health risk tend to insure more. The main drivers of this phenomenon seem to be characteristics such as income and wealth. Nevertheless, we also found parameter heterogeneity to be relevant, leading to possible misinterpretation if the standard `unused characteristics' approach is applied

    Fall detection with wearable sensors - SAFE (SmArt Fall dEtection)

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