Sensing and Compression Techniques for Environmental and Human Sensing Applications

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

SURF: Subject-Adaptive Unsupervised ECG Signal Compression for Wearable Fitness Monitors

Recent advances in wearable devices allow non-invasive and inexpensive collection of biomedical signals including electrocardiogram (ECG), blood pressure, respiration, among others. Collection and processing of various biomarkers are expected to facilitate preventive healthcare through personalized medical applications. Since wearables are based on size- and resource-constrained hardware, and are battery operated, they need to run lightweight algorithms to efficiently manage energy and memory. To accomplish this goal, this paper proposes SURF, a subject-adaptive unsupervised signal compressor for wearable fitness monitors. The core idea is to perform a specialized lossy compression algorithm on the ECG signal at the source (wearable device), to decrease the energy consumption required for wireless transmission and thus prolong the battery lifetime. SURF leverages unsupervised learning techniques to build and maintain, at runtime, a subject-adaptive dictionary without requiring any prior information on the signal. Dictionaries are constructed within a suitable feature space, allowing the addition and removal of code words according to the signal's dynamics (for given target fidelity and energy consumption objectives). Extensive performance evaluation results, obtained with reference ECG traces and with our own measurements from a commercial wearable wireless monitor, show the superiority of SURF against state-of-the-art techniques, including: 1) compression ratios up to 90-times; 2) reconstruction errors between 2% and 7% of the signal's range (depending on the amount of compression sought); and 3) reduction in energy consumption of up to two orders of magnitude with respect to sending the signal uncompressed, while preserving its morphology. SURF, with artifact prone ECG signals, allows for typical compression efficiencies (CE) in the range $\text {CE} \in [{40,50}]$ , which means that the data rate of 3 kbit/s that would be required to send the uncompressed ECG trace is lowered to 60 and 75 bit/s for CE = 40 and CE = 50, respectively

Reflection on the Moderating Role of Islamic Work Ethics and Psychological Capital on the Impact of Abusive Supervision on Deviant Behaviors among Semnan Public organizations Employees

Workplace deviance is one of the undesirable behaviors which are responded by employees due to abusive supervision in the workplace. Abusive supervision is one of the crucial topics in the knowledge of the body of management literature. The employees display aggression and retaliation behavior against supervisor interpersonal mistreatment, and ultimately employees engage with deviant behavior. Psychological capital and Islamic work ethic reduces the effect of workplace deviance in the presence of abusive supervision. The purpose of this study was to investigate the Moderating Role of Islamic Work Ethics and Psychological Capital on the Impact of Abusive Supervision on Deviant Behaviors. The present study was conducted with the purpose of applied and descriptive-correlation method. The statistical population of the study was all employees of public organizations in Semnan province. Samples were selected through simple random sampling, which 218 people was determined according to the size of society in each organization. Tepper (2000) standard questionnaire was used to measure abusive supervision variables, The standard questionnaire of Bennett and Robinson (2000) was used to measure deviant behavior, Islamic work ethics was obtained from the Tufail et al (2017) standard questionnaire and psychological capital from the standard Luthans et al (2015) questionnaire. Questionnaires were analyzed using structural equations’ modeling in Smart-PLS software. The results showed that Abusive supervision has a positive and significant effect on employee's deviant behavior. Also, Islamic Work Ethic and psychological capital moderates the relationship between abusive supervision and employees deviant behaviors

Covariogram-Based Compressive Sensing for Environmental Wireless Sensor Networks

In this paper, we propose covariogram-based compressive sensing (CB-CS), a spatio-temporal compression algorithm for environmental wireless sensor networks. CB-CS combines a novel sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal and leverages the signal\u2019s spatio-temporal correlation structure through the Kronecker CS framework. CB-CS\u2019s performance is systematically evaluated in the presence of synthetic and real signals, comparing it against a number of compression methods from the literature, based on linear approximations, Fourier transforms, distributed source coding, and against several approaches based on CS. CB-CS is found superior to all of them and able to effectively and promptly adapt to changes in the underlying statistical structure of the signal, while also providing compression versus energy tradeoffs that approach those of idealized CS schemes (where the signal correlation structure is perfectly known at the receiver)

Evaluating the gap between compressive sensing and distributed source coding in WSN

Despite the large body of theoretical research available on compression algorithms for wireless sensor networks (WSNs), only recently have researchers started to design and analyze practical distributed compression techniques. Also, approaches belonging to different fields such as signal processing (e.g., discrete Fourier transforms and compressive sensing) or information theory (e.g., distributed source coding) and networking are seldom evaluated against one another. In the present contribution, we consider practical lossy compression schemes that rely on different techniques, such as the exploitation of the temporal and spatial dynamics of the signal as well as recent algorithms based on Compressive Sensing (CS). These techniques are adapted so as to be efficiently applied, within the same data collection framework, to a distributed WSN. Hence, we carry out a comparative performance analysis of these schemes, assessing their performance in terms of reconstruction error vs energy requirements. From this, several interesting observations are derived, which allow the identification of the best performing algorithm(s) as a function of the spatio-temporal characteristics of the signal. For CS, we assess the impact of the node selection scheme (scheduling) and gauge its performance gap with respect to an idealized CS scheme where the signal covariance matrix is perfectly known at the reconstruction point. We finally identify areas that need improvement, especially for the enhancement of CS-based compression

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

Modern wearable IoT devices enable the monitoring of vital parameters such as heart or respiratory rates (RESP), 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 one dimensional 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

P-21: Prevalence of Neck Pain Among Athletes: A Systematic Review

PURPOSE: Many studies have investigated the prevalence of neck pain (NP) and its risk factors in general population. However, the prevalence of NP among athletes has not been systematically investigated yet. We aimed to systematically review the NP prevalence in athletes. As respects of various definitions of NP, response rates and reliability of studies instruments, we considered risk of bias for including studies. METHODS: A comprehensive search was conducted in Sep 2015, using PubMed, Ovid SP Medline, ISI and Google Scholar. We included studies in English that reported the prevalence of NP in an athletic population in a specifically defined period of time. Two reviewers independently assessed the studies' quality, and performed data extractions. RESULTS: Of 1675 titles identified, 8 articles were assessed for bias risk and 6 with low and moderate risk were included. NP was shown to be prevalent in athletes with a oneweek prevalence ranging from 8% to 45%, a one-year prevalence ranging from 38% to 73% and life-time prevalence about 48%. CONCLUSION: Similar to general population, the prevalence of NP in the athletes is high. Therefore, more studies regarding pain prevalences and risk factors may be useful for planning the educational programs, appropriate rehabilitation protocols, and development of preventive guidelines. Researchers are encouraged to perform epidemiologic studies with low risk of bias in this regard