An end-to-end framework for real-time automatic sleep stage classification.

Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client-server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising ≈1700 polysomnography records (≈12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson's disease. Using this system, an entire night's sleep was staged with an accuracy on par with expert human scorers but much faster (≈5 s compared with 30-60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep

Detecting head movement using gyroscope data collected via in-ear wearables

Abstract. Head movement is considered as an effective, natural, and simple method to determine the pointing towards an object. Head movement detection technology has significant potentiality in diverse field of applications and studies in this field verify such claim. The application includes fields like users interaction with computers, controlling many devices externally, power wheelchair operation, detecting drivers’ drowsiness while they drive, video surveillance system, and many more. Due to the diversity in application, the method of detecting head movement is also wide-ranging. A number of approaches such as acoustic-based, video-based, computer-vision based, inertial sensor data based head movement detection methods have been introduced by researchers over the years. In order to generate inertial sensor data, various types of wearables are available for example wrist band, smart watch, head-mounted device, and so on. For this thesis, eSense — a representative earable device — that has built-in inertial sensor to generate gyroscope data is employed. This eSense device is a True Wireless Stereo (TWS) earbud. It is augmented with some key equipment such as a 6-axis inertial motion unit, a microphone, and dual mode Bluetooth (Bluetooth Classic and Bluetooth Low Energy). Features are extracted from gyroscope data collected via eSense device. Subsequently, four machine learning models — Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes, and Perceptron — are applied aiming to detect head movement. The performance of these models is evaluated by four different evaluation metrics such as Accuracy, Precision, Recall, and F1 score. Result shows that machine learning models that have been applied in this thesis are able to detect head movement. Comparing the performance of all these machine learning models, Random Forest performs better than others, it is able to detect head movement with approximately 77% accuracy. The accuracy rate of other three models such as Support Vector Machine, Naïve Bayes, and Perceptron is close to each other, where these models detect head movement with about 42%, 40%, and 39% accuracy, respectively. Besides, the result of other evaluation metrics like Precision, Recall, and F1 score verifies that using these machine learning models, different head direction such as left, right, or straight can be detected

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Machine learning for localization in narrowband IoT networks

Low power wide area networks (LPWANs) are designed for Internet of Things (IoT) appli- cations because of their long-range coverage, low bit rate, and low battery consumption. In the LPWAN networks, Narrow-band IoT (NB-IoT) is a type of network that uses the licensed cellular spectrum, working over the deployed LTE infrastructure. It is rising as a promising technology because of its characteristics and deployment advantages against other LPWAN networks. In NB-IoT networks, localization is an essential service for applications such as smart cities, traffic control, logistics tracking, and others. The outdoor localization is often performed using a Global Navigation Satellite System (GNSS) like Global Positioning System (GPS) to send the current device position with some meters accuracy. However, due to GPS¿s power and size drawbacks, recent reports focus on alternatives to replace GPS-based localization systems with cost and power efficient solutions. This work analyses a database collected over an NB-IoT deployed network in the city of Antwerp in Belgium and implements a solution for outdoor localization based on Machine Learning (ML) methods for distance estimation. The data analysis starts in the pre-processing step, where the databases are cleaned and prepared for the ML analysis. The following process merges and debugs the data to obtain an integrated database with classification for urban and rural areas. The localization solution performs a support vector regression, random forest regression, and a multi-layer perceptron regression using as input parameters the received signal strength indicator (RSSI) and the base station (BS) position details in order to predict the distance to the IoT nodes and estimate the current position (latitude and longitude) of them. This implementation includes hyper-parameter tuning, the train and test process, and mathematical calculations to obtain the estimated position with mean and median location estimation errors expressed in meters. The implementation of the methodology processes results in 280 and 220 meters corre- sponding to the mean and median location errors for the urban area and 920 and 570 meters for the rural area. The accuracy levels obtained in the results turn this solution suitable for the most common uses of localization in IoT instead of using a GPS device. As a result, this study proposes a new approach for localization in IoT networks. In addition to the implemented solution defines valuable research lines to improve the accuracy levels and generate more contributions to optimize the equipment resources and reduce the IoT device¿s final cost.OutgoingObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenible