36 research outputs found

    Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals

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    Modern wearable Internet of Things (IoT) devices enable the monitoring of vital parameters such as heart or respiratory (RESP) rates, electrocardiography (ECG), photo-plethysmographic (PPG) signals within e-health applications. A common issue of wearable technology is that signal transmission is power-demanding and, as such, devices require frequent battery charges and this poses serious limitations to the continuous monitoring of vitals. To ameliorate this, we advocate the use of lossy signal compression as a means to decrease the data size of the gathered biosignals and, in turn, boost the battery life of wearables and allow for fine-grained and long-term monitoring. Considering 1-D biosignals such as ECG, RESP, and PPG, which are often available from commercial wearable IoT devices, we provide a thorough review of existing biosignal compression algorithms. Besides, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As we quantify, the most efficient schemes allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4% of the peak-to-peak signal amplitude. Finally, avenues for future research are discussed. © 2014 IEEE

    Deep Learning-Based Approach for Missing Data Imputation

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    The missing values in the datasets are a problem that will decrease the machine learning performance. New methods arerecommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deeplearning are among these methods. Although deep learning methods is one of the popular subjects of today, there are limitedstudies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of themis the autoencoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasetswas estimated by using denoising autoencoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chainedequations (MICE) methods. The estimation success of the methods was compared according to the root mean square error(RMSE) criterion. It was observed that the DAE method was more successful than other statistical methods in estimating themissing values for large datasets

    Stochastic Optimization and Machine Learning Modeling for Wireless Networking

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    In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

    Sensing and Compression Techniques for Environmental and Human Sensing Applications

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    In this doctoral thesis, we devise and evaluate a variety of lossy compression schemes for Internet of Things (IoT) devices such as those utilized in environmental wireless sensor networks (WSNs) and Body Sensor Networks (BSNs). We are especially concerned with the efficient acquisition of the data sensed by these systems and to this end we advocate the use of joint (lossy) compression and transmission techniques. Environmental WSNs are considered first. For these, we present an original compressive sensing (CS) approach for the spatio-temporal compression of data. In detail, we consider temporal compression schemes based on linear approximations as well as Fourier transforms, whereas spatial and/or temporal dynamics are exploited through compression algorithms based on distributed source coding (DSC) and several algorithms based on compressive sensing (CS). To the best of our knowledge, this is the first work presenting a systematic performance evaluation of these (different) lossy compression approaches. The selected algorithms are framed within the same system model, and a comparative performance assessment is carried out, evaluating their energy consumption vs the attainable compression ratio. Hence, as a further main contribution of this thesis, we design and validate a novel CS-based compression scheme, termed covariogram-based compressive sensing (CB-CS), which combines a new sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal. As a second main research topic, we focus on modern wearable IoT devices which enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), and photo-plethysmographic (PPG) signals within e-health applications. These devices are battery operated and communicate the vital signs they gather through a wireless communication interface. A common issue of this technology is that signal transmission is often power-demanding and this poses serious limitations to the continuous monitoring of biometric signals. To ameliorate this, we advocate the use of lossy signal compression at the source: this considerably reduces the size of the data that has to be sent to the acquisition point by, in turn, boosting the battery life of the wearables and allowing for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable devices, we first provide a throughout review of existing compression algorithms. Hence, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As part of this first investigation, dictionaries are built using a suboptimal but lightweight, online and best effort algorithm. Surprisingly, the obtained compression scheme is found to be very effective both in terms of compression efficiencies and reconstruction accuracy at the receiver. This approach is however not yet amenable to its practical implementation as its memory usage is rather high. Also, our systematic performance assessment reveals that the most efficient compression algorithms allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4 % of the peak-to-peak signal amplitude. Based on what we have learned from this first comparison, we finally propose a new subject-specific compression technique called SURF Subject-adpative Unsupervised ecg compressor for weaRable Fitness monitors. In SURF, dictionaries are learned and maintained using suitable neural network structures. Specifically, learning is achieve through the use of neural maps such as self organizing maps and growing neural gas networks, in a totally unsupervised manner and adapting the dictionaries to the signal statistics of the wearer. As our results show, SURF: i) reaches high compression efficiencies (reduction in the signal size of up to 96 times), ii) allows for reconstruction errors well below 4 % (peak-to-peak RMSE, errors of 2 % are generally achievable), iii) gracefully adapts to changing signal statistics due to switching to a new subject or changing their activity, iv) has low memory requirements (lower than 50 kbytes) and v) allows for further reduction in the total energy consumption (processing plus transmission). These facts makes SURF a very promising algorithm, delivering the best performance among all the solutions proposed so far

    Human Activity Recognition Based on Multimodal Body Sensing

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    In the recent years, human activity recognition has been widely popularized by a lot of smartphone manufacturers and fitness tracking companies. It has allowed us to gain a deeper insight into our physical health on a daily basis. However, with the evolution of fitness tracking devices and smartphones, the amount of data that is being captured by these devices is growing exponentially. This paper aims at understanding the process of dimensionality reduction such as PCA so that the data can be used to make meaningful predictions along with novel techniques using autoencoders with different activation functions. The paper also looks into how using autoencoders allows us to better capture the relations between features in the data. It also covers some of the classification techniques such as k-Nearest Neighbors, SVM and Random forest that are currently being used for activity recognition that have shown promising results

    Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector

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    Cardiovascular diseases (CVDs) are the number one cause of death worldwide. While there is growing evidence that the atrial fibrillation (AF) has strong associations with various CVDs, this heart arrhythmia is usually diagnosed using electrocardiography (ECG) which is a risk-free, non-intrusive, and cost-efficient tool. Continuously and remotely monitoring the subjects' ECG information unlocks the potentials of prompt pre-diagnosis and timely pre-treatment of AF before the development of any life-threatening conditions/diseases. Ultimately, the CVDs associated mortality could be reduced. In this manuscript, the design and implementation of a personalized healthcare system embodying a wearable ECG device, a mobile application, and a back-end server are presented. This system continuously monitors the users' ECG information to provide personalized health warnings/feedbacks. The users are able to communicate with their paired health advisors through this system for remote diagnoses, interventions, etc. The implemented wearable ECG devices have been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%). To boost the battery life of the wearable devices, a lossy compression schema utilizing the quasi-periodic feature of ECG signals to achieve compression was proposed. Compared to the recognized schemata, it outperformed the others in terms of compression efficiency and distortion, and achieved at least 2x of CR at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable automated AF diagnosis/screening in the proposed system, a ResNet-based AF detector was developed. For the ECG records from the 2017 PhysioNet CinC challenge, this AF detector obtained an average testing F1=85.10% and a best testing F1=87.31%, outperforming the state-of-the-art
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