Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

The effects of auditory contrast tuning upon speech intelligibility

We have previously identified neurons tuned to spectral contrast of wideband sounds in auditory cortex of awake marmoset monkeys. Because additive noise alters the spectral contrast of speech, contrast-tuned neurons, if present in human auditory cortex, may aid in extracting speech from noise. Given that this cortical function may be underdeveloped in individuals with sensorineural hearing loss, incorporating biologically-inspired algorithms into external signal processing devices could provide speech enhancement benefits to cochlear implantees. In this study we first constructed a computational signal processing algorithm to mimic auditory cortex contrast tuning. We then manipulated the shape of contrast channels and evaluated the intelligibility of reconstructed noisy speech using a metric to predict cochlear implant user perception. Candidate speech enhancement strategies were then tested in cochlear implantees with a hearing-in-noise test. Accentuation of intermediate contrast values or all contrast values improved computed intelligibility. Cochlear implant subjects showed significant improvement in noisy speech intelligibility with a contrast shaping procedure

Phoneme Compression: processing of the speech signal and effects on speech intelligibility in hearing-Impaired listeners

Hearing-aid users often continue to have problems with poor speech understanding in difficult acoustical conditions. Another generally accounted problem is that certain sounds become too loud whereas other sounds are still not audible. Dynamic range compression is a signal processing technique that may be used in hearing aids to compensate for these remaining disabilities. Its main function is to provide sufficient amplification at low input levels without overloading the auditory system at high input levels. The time constants define the time needed by the compressor to realize a change in amplification. When using relatively large time constants, the compressor only reduces differences in overall level. This type of compression is known as Automatic Gain Control (AGC) or Automatic Volume Control (AVC).With short time constants the compressor also reduces the dynamic range of a fast-fluctuating signal like speech. This last type of system is therefore often called a syllabic or a phoneme compressor. The main goal of using phoneme compression is to optimize speech intelligibility by improving the detection of weak speech cues. We developed a phoneme compression system to improve the perception of high-frequency speech cues in hearing impaired listeners. The basic mechanism is a continuously changing balancing between low- and high-frequency amplification, steered by the input level of each speech part. As a consequence, the system should provide a relatively high amount of amplification to weak high-frequency speech cues. A specific configuration was developed to additionally reduce the negative effect of low-frequency amplification on the detection of high-frequency cues. This type of configuration is called anti-USOM processing as it is meant to compensate for “Upward-Spread-Of-Masking” (USOM) of high-frequency information by low-frequency signal parts. The main goal of the present thesis was to evaluate the effect of the different compression configurations on speech intelligibility in a group of hearing-impaired listeners with moderate-to-severe perceptive high-frequency losses (chapters 4 to 7). Additionally, we have investigated the effect of various types of compression on amplitude-modulated signals and speech(-like) signals (chapters 2 and 3). The acoustical measurements in chapters 2 and 3 provided a good insight in the effect of compression on modulating signals like speech. Speech can be considered as a stream of sounds with a continuously varying spectrum. These spectral differences lead to fluctuations of the envelope of the signal within individual frequency bands. The modulation depth is a measure for the amount of fluctuations. Phoneme compression will normally reduce the amount of fluctuations, resulting in a smaller modulation depth. By comparing the modulation depth in a signal before and after compression, the effective amount of compression can be obtained. This method was applied using an amplitude-modulated signal (chapter 2) and using speech(-like) signals (chapter 3). Another method compared the average level distributions of speech with and without compression (chapter 2). The results show that relatively short time constants were needed to affect the range of modulations that are relevant to speech intelligibility. Furthermore, an effective reduction of intensity differences within separate frequency channels was only possible if the compression was applied within independent frequency channels as well. Interestingly, the results were not only influenced by the compressor settings but also by the acoustical properties of the test signal. Intensity differences were reduced more effectively for speech in a stationary background noise compared to speech only. Chapters 4 and 6 describe the effects of different types of phoneme compression on speech intelligibility in hearing-impaired listeners. Phoneme scores were obtained in conditions with and without background noise. We evaluated the difference in performance between phoneme compression and a linear reference condition near comfortable presentation levels. This implies that the results could not be influenced by differences in overall level between the various conditions. The results described in chapter 4 show that hearing-impaired listeners may benefit from our type of phoneme compression in conditions without background noise. Consonant perception was improved by phoneme compression whereas the anti-USOM processing had an additional positive effect on vowel perception. Unfortunately, no such positive effects were found in conditions with background noise. Even substantially negative effects were found with the anti-USOM configuration that gave the best performance in quiet. The use of a more moderate type of anti-USOM in chapter 6 also resulted in a negative effect on phoneme recognition in background noise. No benefit was found for other types of phoneme compression in background noise. The use of a compression ratio of 4 resulted even in negative effects (chapter 4). This means that the performance in background noise gets poorer with an increasing amount of phoneme compression. The temporal behaviour of the background noise did not influence the results (chapter 6). We hoped to find positive effects from phoneme compression in a fluctuating background noise, but no such improvement was found. The results of chapter 5 can be used to understand the measured effects of compression on speech intelligibility. Two methods were used to analyse the perceptual confusions of chapter 4. INDSCAL was used to identify and visualise the most relevant differences in phoneme perception. However the interpretation of these differences was not always easy because the perceptual dimensions could be related to several perceptual features. Therefore, SINFA was used as a second method. The advantage of using this method was that the various effects could be separated for the different predefined articulatory features. In quiet, positive effects were found on the perception of features containing mainly high-frequency information. This is according to our original goal to improve the identification of high-frequency cues by phoneme compression. However, the perception of high-frequency cues appeared to be highly deteriorated at critical background noise conditions. As a consequence, the features containing low-frequency information had become of major importance. The use of anti-USOM processing removed low-frequency information that appeared to be relevant for the perception of low-frequency cues. Additionally, we evaluated three phoneme compression conditions in a small field study using an experimental body-worn hearing aid (chapter 7). The phoneme compression configurations were embedded in a slow-acting non-linear system to compensate for differences in overall level. The listeners used the system for a period of six weeks next to the own hearing aids. The performance with the various compression programs was measured every week. The main question was if the performance could be influenced by a frequent use of the system. In general, the results were similar to that in previous experiments. The performance in background noise tended to be poorer than the performance in quiet. Interestingly, the overall recognition score with phoneme compression improved over time. However, a large part of this improvement was also found for the reference condition. The tendency for a small additional improvement with phoneme compression may be attributed to acclimatization to the speech processing. The experiences of the hearing-impaired listeners with the phoneme compression programs differed between listeners and depended of the difference in performance with the own hearing aids. Generally they had no problems with the sound of the new programs.Hoortoesteldragers houden vaak problemen met het verstaan van spraak in lastige akoestische omstandigheden. Een ander bekend probleem is dat door de versterking van het hoortoestel bepaalde geluiden als te hard ervaren worden, terwijl andere belangrijke geluiden nog steeds niet worden gehoord. Ter compensatie van deze beperkingen kan dynamiekcompressie worden toegepast in het hoortoestel. Dit is een vorm van signaalbewerking die voor voldoende versterking zorgt bij lage niveaus, zonder het gehoor te overbelasten bij hoge signaalniveaus. Een compressiesysteem heeft tijd nodig om een verandering in versterking te realiseren. Deze is instelbaar via de zogenaamde tijdsconstanten. Bij hoge waarden van de tijdsconstanten reduceert de compressor alleen globale verschillen in signaalniveau. Deze vorm van compressie wordt Automatische Volume Controle (AVC) of Automatic Gain Control (AGC) genoemd. Indien de tijdsconstanten kort gehouden worden reduceert de compressor ook intensiteitsverschillen tussen opeenvolgende spraakklanken. Deze vorm van dynamiekcompressie wordt daarom vaak aangeduid met syllabische compressie of foneemcompressie. Het doel hiervan is het optimaliseren van spraakverstaan door een verbeterde detectie van zwakkere spraakklanken. In dit proefschrift wordt een vorm van foneemcompressie onderzocht die bedoeld is om de perceptie van hoogfrequente spraakklanken te verbeteren bij slechthorenden. Het systeem verandert continu de verhouding tussen laag- en hoogfrequente versterking, afhankelijk van de niveaus van de afzonderlijke spraakklanken. Dit leidt tot extra versterking van de zwakke klanken. Verder is een aparte configuratie ontwikkeld om de negatieve invloed van laagfrequente versterking te verminderen op de perceptie van hoogfrequente spraak (“anti-upward-spread-of-masking” of “anti-USOM” genoemd). Ons hoofddoel is om de effecten van deze vormen van foneemcompressie te evalueren op spraakverstaan bij slechthorenden (hoofdstukken 4 t/m 7). Tevens is onderzocht hoe het spraaksignaal beïnvloed wordt door verschillende vormen van compressie (hoofdstukken 2 en 3). De akoestische metingen in hoofdstukken 2 en 3 geven een goed inzicht in hoe spraak en andere snel fluctuerende signalen beïnvloed worden door compressie. Spraak kan beschouwd worden als een continue stroom geluiden met een variërend spectrum. Deze spectrale verschillen leiden tot fluctuaties in de omhullende van het spraaksignaal binnen verschillende frequentiebanden, ook wel modulaties genoemd. De sterkte van de modulaties wordt uitgedrukt door de modulatiediepte. Foneemcompressie zal, indien effectief toegepast, de modulatiediepte binnen het spraaksignaal verkleinen. De effectieve mate van compressie kan geschat worden door de modulatiediepte voor en na compressie met elkaar te vergelijken. Deze methode is toegepast voor “eenvoudige” amplitude-gemoduleerde signalen (hoofdstuk 2) en spraak(-achtige) signalen (hoofdstuk 3). Een andere methode bestaat uit het vergelijken van de niveauverdelingen binnen spraak voor en na compressie (hoofdstuk 2). De resultaten laten zien dat relatief korte tijdsconstanten nodig zijn om de voor het spraakverstaan relevante modulaties te beïnvloeden. Verder blijkt dat alleen compressie uitgevoerd in verschillende frequentiekanalen in staat is intensiteitsverschillen te reduceren in de afzonderlijke frequentiebanden. Opmerkelijk genoeg blijken niet alleen de instellingen van de compressor invloed te hebben op de mate van effectieve compressie, maar ook het gebruikte testsignaal. Voor spraak in stationaire ruis wordt een hogere mate van effectieve compressie gemeten dan voor spraak afzonderlijk. In hoofdstukken 4 en 6 worden de resultaten beschreven van spraaktesten bij slechthorenden met verschillende vormen van foneemcompressie. Er zijn foneemscores bepaald in condities met en zonder achtergrondruis. Hierbij is het verschil bekeken tussen de resultaten met foneemcompressie en een geoptimaliseerde referentieconditie aangeboden op het zelfde signaalniveau. De resultaten in hoofdstuk 4 laten zien dat slechthorenden in staat zijn te profiteren van foneemcompressie zolang er geen achtergrondgeluid wordt toegevoegd aan de spraak. Compressie blijkt vooral een positieve invloed te hebben op de herkenning van de beginmedeklinker. Daarnaast geeft anti-USOM een positief effect op de klinkerherkenning. De beste resultaten worden behaald met een combinatie van beide technieken. Helaas wordt er voor de condities met achtergrondgeluid juist negatieve effecten gevonden met deze configuratie. Ook met een meer gematigde vorm van anti-USOM blijft het resultaat negatief (hoofdstuk 6). Andere configuraties met foneemcompressie geven eveneens geen verbetering van spraakverstaan in achtergrondlawaai. Voor een grote mate van compressie wordt een verslechtering gevonden. Ook bij een fluctuerende achtergrondruis worden geen duidelijke voordelen gevonden van foneemcompressie. De resultaten in hoofdstuk 5 geven meer inzicht in de effecten van foneemcompressie op de perceptie van spraakklanken. De verwisselingen van spraakklanken in de spraaktesten met slechthorenden zijn onderzocht door middel van twee methoden. INDSCAL identificeert en visualiseert de meest relevante verschillen in foneemperceptie door het definiëren van onafhankelijke perceptuele dimensies. De interpretatie van deze dimensies in termen van fonetische kenmerken blijkt echter niet altijd even gemakkelijk. Als aanvulling is daarom SINFA gebruikt, met als voordeel dat de verschillende fonetische kenmerken hierbij vooraf gedefinieerd kunnen worden. Zowel foneemcompressie als anti-USOM blijken een positief effect te hebben op de perceptie van hoogfrequente spraakklanken. Met name de kenmerken fricatief en de hogere klinkerformanten worden beter doorgegeven. Dit klopt met de originele doelstelling. In de condities met achtergrondgeluid is de perceptie van hoogfrequente spraakklanken echter zodanig verstoord dat het gebruik van foneemcompressie geen uitkomst meer biedt. De laagfrequente spraakkenmerken worden nu belangrijker voor het spraakverstaan. Juist deze blijken te worden aangetast worden door het toepassen van anti-USOM, waarmee de in hoofdstukken 4 en 6 gevonden negatieve effecten verklaard kunnen worden. In een kleine veldstudie met experimentele digitale hoortoestellen zijn een drietal van de eerder geteste compressieconfiguraties geëvalueerd door slechthorenden (hoofdstuk 7). De foneemcompressor is opgenomen in een langzaam niet-lineair systeem waarmee de globale niveauverschillen gereduceerd kunnen worden. De programma’s zijn paarsgewijs getest over een periode van 6 weken. Vanwege praktische redenen was het toegestaan om ook de eigen hoortoestellen te blijven gebruiken. Wekelijks zijn er spraaktesten uitgevoerd, met als hoofdvraag of de prestaties beïnvloed worden door een regelmatig gebruik van de programma’s. Er worden opnieuw slechtere resultaten met compressie gevonden in achtergrondlawaai. Opmerkelijk is echter dat de spraakscores toenemen over de periode van 6 weken. Een groot deel van deze toename wordt ook gevonden voor de referentieconditie met alleen langzame compressie. Het gebruik van foneemcompressie lijkt tot een lichte extra stijging te leiden. Deze kan alleen verklaard worden door gewenning aan het luisteren met foneemcompressie. De ervaringen van de slechthorenden met de compressieprogramma’s zijn wisselend, afhankelijk van de prestaties ten opzichte van het eigen toestel. Het geluid wordt over het algemeen als positief ervaren

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Improving the Speech Intelligibility By Cochlear Implant Users

In this thesis, we focus on improving the intelligibility of speech for cochlear implants (CI) users. As an auditory prosthetic device, CI can restore hearing sensations for most patients with profound hearing loss in both ears in a quiet background. However, CI users still have serious problems in understanding speech in noisy and reverberant environments. Also, bandwidth limitation, missing temporal fine structures, and reduced spectral resolution due to a limited number of electrodes are other factors that raise the difficulty of hearing in noisy conditions for CI users, regardless of the type of noise. To mitigate these difficulties for CI listener, we investigate several contributing factors such as the effects of low harmonics on tone identification in natural and vocoded speech, the contribution of matched envelope dynamic range to the binaural benefits and contribution of low-frequency harmonics to tone identification in quiet and six-talker babble background. These results revealed several promising methods for improving speech intelligibility for CI patients. In addition, we investigate the benefits of voice conversion in improving speech intelligibility for CI users, which was motivated by an earlier study showing that familiarity with a talker’s voice can improve understanding of the conversation. Research has shown that when adults are familiar with someone’s voice, they can more accurately – and even more quickly – process and understand what the person is saying. This theory identified as the “familiar talker advantage” was our motivation to examine its effect on CI patients using voice conversion technique. In the present research, we propose a new method based on multi-channel voice conversion to improve the intelligibility of transformed speeches for CI patients

Vibro-acoustic Analysis of Reciprocating Compressor in the Context of Fault Diagnosis

This project assessed the behaviour of a positive displacement type of compressor utilising airborne acoustic signatures. The study concentrated on finding an improved method based on airborne sound that can be suitable for diagnosing some common faults in reciprocating compressor (RC). Being a critical component of the industry, the condition monitoring of reciprocating compressor is very much needed to avoid any failure of its machine parts that can cause a sudden breakdown of RC. The compressor acoustic signal is a result of various mechanical forces related to the varied cylinder pressure, valve movement, turbulence air flow which in terms contribute to the periodic excitation along with the non-linearity caused by the valve fluttering, hence making the airborne signal complex and non-stationary in nature. The transient response due to the periodic impact of the valves, modulation effect due to the fluid-mechanical interaction and low signal to noise ratio (SNR) are the challenging aspects of this study. To demonstrate the vibro-acoustic property of the reciprocating compressor, first a model was developed. The leakages in valve and intercooler are very common in RC. The second most common fault which is often neglected is a clogged filter. Hence, taking into consideration, filter blockage fault is introduced for the first time in the existing test set up. Three faults (discharge valve leakage, intercooler leakage and filter blockage) are simulated, and corresponding acoustic responses are recorded for further study of the signal-nature. The model is then validated by the actual data from RC test bed. Along with the modelling of compressor acoustics, various signal processing techniques like Minimum Entropy Deconvolution (MED), Teager Energy Operator (TEO) are used on the test data to detect abnormalities present. MED in this case, is proved to be effective in finding the transient responses whereas, TEO serves as an energy detection tool for tracking the total mechanical energy. Still both methods find it difficult to come up with the best possible diagnosis results as they fail to take all the major characteristics of the RC acoustics into consideration. To overcome this challenge, higher order spectral analysis as a form of Modulation Signal Bi-Spectrum (MSB) is used to find out the most effective modulating components by enhancing the modulating characteristics and suppressing the noise. Moreover, the quadratic phase coupling allows MSB to handle the non-linearity that might be present in RC due to the valve fluttering. The proposed MSB based method not only provides a more consistent and accurate diagnosis of compressor faults but also shows that airborne acoustics has a good aspect in fault identification of RC by validating both model and test results. Recognizing that there is perpetual room for improvement, the performance of the proposed RC fault diagnosis method can be enhanced by incorporating a denoising technique developed using the Variational Mode Decomposition (VMD) associated with Kalman filtering method. The future study must also consider several other individual and compound faults that can be incorporated in the study for understanding vibro-acoustic phenomena of RC