8,961 research outputs found
Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
Recent years have witnessed the rapid development of human activity
recognition (HAR) based on wearable sensor data. One can find many practical
applications in this area, especially in the field of health care. Many machine
learning algorithms such as Decision Trees, Support Vector Machine, Naive
Bayes, K-Nearest Neighbor, and Multilayer Perceptron are successfully used in
HAR. Although these methods are fast and easy for implementation, they still
have some limitations due to poor performance in a number of situations. In
this paper, we propose a novel method based on the ensemble learning to boost
the performance of these machine learning methods for HAR
Human activity recognition making use of long short-term memory techniques
The optimisation and validation of a classifiers performance when applied to real
world problems is not always effectively shown. In much of the literature describing
the application of artificial neural network architectures to Human Activity
Recognition (HAR) problems, postural transitions are grouped together and treated as
a singular class. This paper proposes, investigates and validates the development of
an optimised artificial neural network based on Long-Short Term Memory techniques
(LSTM), with repeated cross validation used to validate the performance of the
classifier. The results of the optimised LSTM classifier are comparable or better to
that of previous research making use of the same dataset, achieving 95% accuracy
under repeated 10-fold cross validation using grouped postural transitions. The work
in this paper also achieves 94% accuracy under repeated 10-fold cross validation
whilst treating each common postural transition as a separate class (and thus
providing more context to each activity)
Emotion Detection Using Noninvasive Low Cost Sensors
Emotion recognition from biometrics is relevant to a wide range of
application domains, including healthcare. Existing approaches usually adopt
multi-electrodes sensors that could be expensive or uncomfortable to be used in
real-life situations. In this study, we investigate whether we can reliably
recognize high vs. low emotional valence and arousal by relying on noninvasive
low cost EEG, EMG, and GSR sensors. We report the results of an empirical study
involving 19 subjects. We achieve state-of-the- art classification performance
for both valence and arousal even in a cross-subject classification setting,
which eliminates the need for individual training and tuning of classification
models.Comment: To appear in Proceedings of ACII 2017, the Seventh International
Conference on Affective Computing and Intelligent Interaction, San Antonio,
TX, USA, Oct. 23-26, 201
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Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.
Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios
Automated Home Oxygen Delivery for Patients with COPD and Respiratory Failure: A New Approach
Long-term oxygen therapy (LTOT) has become standard care for the treatment of patients with chronic obstructive pulmonary disease (COPD) and other severe hypoxemic lung diseases. The use of new portable O-2 concentrators (POC) in LTOT is being expanded. However, the issue of oxygen titration is not always properly addressed, since POCs rely on proper use by patients. The robustness of algorithms and the limited reliability of current oximetry sensors are hindering the effectiveness of new approaches to closed-loop POCs based on the feedback of blood oxygen saturation. In this study, a novel intelligent portable oxygen concentrator (iPOC) is described. The presented iPOC is capable of adjusting the O-2 flow automatically by real-time classifying the intensity of a patient's physical activity (PA). It was designed with a group of patients with COPD and stable chronic respiratory failure. The technical pilot test showed a weighted accuracy of 91.1% in updating the O-2 flow automatically according to medical prescriptions, and a general improvement in oxygenation compared to conventional POCs. In addition, the usability achieved was high, which indicated a significant degree of user satisfaction. This iPOC may have important benefits, including improved oxygenation, increased compliance with therapy recommendations, and the promotion of PA
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