2,863 research outputs found

    The Estimation of Caloric Expenditure Using Three Triaxial Accelerometers

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    Accelerometer-based activity monitors are commonly used to measure physical activity energy expenditure (PAEE). Newly designed wrist and hip-worn triaxial accelerometers claim to accurately predict PAEE across a range of activities. Purpose: To determine if the Nike FuelBand (NFB), Fitbit (FB) and ActiGraph GT3X+ (AG) estimate PAEE in various activities. Methods: 21 healthy, college-aged adults wore a NFB on the right wrist, a FB on the left hip, and AG on the right hip, while performing 17 activities. AG data were analyzed using Freedson’s kcal regression equation. PAEE was measured using the Cosmed K4b2 (K4). Repeated measures ANOVAs were used to compare mean differences in PAEE (kcal/min). Paired sample t-tests with Bonferroni adjustments were used to locate significant differences. Results: For each device, the mean difference in PAEE was significantly different from the K4 (NFB, -0.45 + 2.8, FB, 0.48 + 2.27, AG, 0.64 + 2.59 kcal/min, p = 0.01). The NFB significantly overestimated most walking activities (e.g., regular walking; K4, 3.1 + 0.2 vs. NFB, 4.6 + 0.2 kcal/min) and activities with arm movements (e.g., sweeping; K4, 3.0 + 0.8 vs. NFB, 4.7 + 0.4 kcal/min, p \u3c 0.05). The NFB trended towards overestimating sport activities (basketball; K4, 10.8 + 0.8 vs. NFB, 12.2 + 0.5 kcal/min) (racquetball; K4, 9.6 + 0.8 vs. NFB 11.1 + 0.5 kcal/min). The FB and the AG significantly overestimated walking (K4, 3.1 + 0.2; FB, 5.4 + 0.3, AG, 5.8 + 0.4 kcal/min, p = 0.01) and underestimated PAEE of most activities with arm movements (e.g., Air Dyne, K4 5.6 + 0.2; Fitbit, 0.3 + 0.2; AG, 0.2 + 0.1 kcal/min, p \u3c 0.05) (racquetball, K4, 9.6 + 0.8 kcal/minute vs. FB, 7.4 + 0.6 kcal/minute, vs. AG, 6.5 + 0.4 kcal/minute, p \u3c 0.05). Conclusion: The NFB overestimated PAEE during most activities with arm movements and tended to overestimate sport activities, while the AG and FB overestimated walking and underestimated activities with arm movements. Overall, the wrist-worn NFB had similar accuracy to the waist-worn triaxial accelerometers; however, none of the devices were able to estimate PAEE across a range of activities

    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

    Using wearable devices to measure physical activity in manual wheelchair users with spinal cord injuries

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    Manual wheelchair users (MWUs) with spinal cord injury (SCI) generally exhibit low levels of physical activity (PA), placing them at a greater risk for many chronic diseases. Accurately measuring levels of PA in this population could potentially lead to better health management among these individuals. Recently, there has been a growth in the use of wearable devices to help individuals track free-living PA for self-management. This has been explored extensively in the ambulatory population, specifically with research grade activity monitors such as ActiGraph wearable devices. However, the literature lacks adequate investigation for energy expenditure (EE) assessment and PA estimation using wearable devices in the non-ambulatory population. The objective of this thesis is to assess the ability of wearable devices in estimating EE and PA in wheelchair users with SCI. In the first study, we conducted a literature search for existing EE predictive algorithms using an ActiGraph activity monitor for MWUs with SCI and evaluated their validity using an out-of-sample dataset collected from MWUs with chronic SCI. None of the five sets of predictive equations demonstrated equivalence within 20% of the criterion measure based on an equivalence test. The mean absolute error (MAE) for the five sets of predictive equations ranged from 0.87 – 6.41 kilocalories per minute (kcalmin-1) when compared with the criterion measure, and the intraclass correlation (ICC) estimates ranged from 0.06 – 0.59. Given the unsatisfactory performance of the existing EE predictive models, in the second study, we used machine learning techniques to develop a random forest model (RFM) for activity intensity estimation using data collected from MWUs with SCIs. Based on a 10-fold cross validation, the RFM had an average overall accuracy of 81.3% in distinguishing among sedentary, light-intensity PA, and MVPA with a precision of 0.82, 0.77, and 0.87, and a recall of 0.84, 0.79, and 0.82 for each intensity category, respectively. The results indicate that the RFM could classify sedentary and MVPA time reasonably well, but may lack the ability to classify light-intensity PA

    Multi-sensor fusion based on multiple classifier systems for human activity identification

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    Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

    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

    Securing IoT Attacks: A Machine Learning Approach for Developing Lightweight Trust-Based Intrusion Detection System

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    The routing process in the Internet of Things (IoT) presents challenges in industrial applications due to its complexity, involving multiple devices, critical decision-making, and accurate data transmission. The complexity further increases with dynamic IoT devices, which creates opportunities for potential intruders to disrupt routing. Traditional security measures are inadequate for IoT devices with limited battery capabilities. Although RPL (Routing Protocol for Low Energy and Lossy Networks) is commonly used for IoT routing, it remains vulnerable to security threats. This study aims to detect and isolate three routing attacks on RPL: Rank, Sybil, and Wormhole. To achieve this, a lightweight trust-based secured routing system is proposed, utilizing machine learning techniques to derive values for devices in new networks, where initial trust values are unavailable. The system demonstrates successful detection and isolation of attacks, achieving an accuracy of 98.59%, precision of 98%, recall of 99%, and f-score of 98%, thereby reinforcing its effectiveness. Attacker nodes are identified and promptly disabled, ensuring a secure routing environment. Validation on a generated dataset further confirms the reliability of the system

    STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

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    To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration

    Enhancing Activity Recognition by Fusing Inertial and Biometric Information

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    Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information

    The Emerging Wearable Solutions in mHealth

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    The marriage of wearable sensors and smartphones have fashioned a foundation for mobile health technologies that enable healthcare to be unimpeded by geographical boundaries. Sweeping efforts are under way to develop a wide variety of smartphone-linked wearable biometric sensors and systems. This chapter reviews recent progress in the field of wearable technologies with a focus on key solutions for fall detection and prevention, Parkinson’s disease assessment and cardiac disease, blood pressure and blood glucose management. In particular, the smartphone-based systems, without any external wearables, are summarized and discussed
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