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

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

MEG, PSYCHOPHYSICAL AND COMPUTATIONAL STUDIES OF LOUDNESS, TIMBRE, AND AUDIOVISUAL INTEGRATION

Natural scenes and ecological signals are inherently complex and understanding of their perception and processing is incomplete. For example, a speech signal contains not only information at various frequencies, but is also not static; the signal is concurrently modulated temporally. In addition, an auditory signal may be paired with additional sensory information, as in the case of audiovisual speech. In order to make sense of the signal, a human observer must process the information provided by low-level sensory systems and integrate it across sensory modalities and with cognitive information (e.g., object identification information, phonetic information). The observer must then create functional relationships between the signals encountered to form a coherent percept. The neuronal and cognitive mechanisms underlying this integration can be quantified in several ways: by taking physiological measurements, assessing behavioral output for a given task and modeling signal relationships. While ecological tokens are complex in a way that exceeds our current understanding, progress can be made by utilizing synthetic signals that encompass specific essential features of ecological signals. The experiments presented here cover five aspects of complex signal processing using approximations of ecological signals : (i) auditory integration of complex tones comprised of different frequencies and component power levels; (ii) audiovisual integration approximating that of human speech; (iii) behavioral measurement of signal discrimination; (iv) signal classification via simple computational analyses and (v) neuronal processing of synthesized auditory signals approximating speech tokens. To investigate neuronal processing, magnetoencephalography (MEG) is employed to assess cortical processing non-invasively. Behavioral measures are employed to evaluate observer acuity in signal discrimination and to test the limits of perceptual resolution. Computational methods are used to examine the relationships in perceptual space and physiological processing between synthetic auditory signals, using features of the signals themselves as well as biologically-motivated models of auditory representation. Together, the various methodologies and experimental paradigms advance the understanding of ecological signal analytics concerning the complex interactions in ecological signal structure

The frequency-following response (FFR) to speech stimuli: a normative dataset in healthy newborns

The Frequency-Following Response (FFR) is a neurophonic auditory evoked potential that reflects the efficient encoding of speech sounds and is disrupted in a range of speech and language disorders. This raises the possibility to use it as a potential biomarker for literacy impairment. However, reference values for comparison with the normal population are not yet established. The present study pursues the collection of a normative database depicting the standard variability of the newborn FFR. FFRs were recorded to /da/ and /ga/ syllables in 46 neonates born at term. Seven parameters were retrieved in the time and frequency domains, and analyzed for normality and differences between stimuli. A comprehensive normative database of the newborn FFR is offered, with most parameters showing normal distributions and similar robust responses for /da/ and /ga/ stimuli. This is the first normative database of the FFR to characterize normal speech sound processing during the immediate postnatal days, and corroborates the possibility to record the FFRs in neonates at the maternity hospital room. This normative database constitutes the first step towards the detection of early FFR abnormalities in newborns that would announce later language impairment, allowing early preventive measures from the first days of life

A Two-Step Adaptive Noise Cancellation System for Dental-Drill Noise Reduction

This paper introduces a two-step dental-drill Noise Reduction (NR) technique based upon the Adaptive Noise Cancellation (ANC) system. The proposed technique is particularly designed for the NR headphone, which the patients should be wearing while having their dental treatment. In the first step, a tone-frequency extraction algorithm is proposed to estimate the main sinusoidal frequency of the dental-drill noise. The estimated sinusoidal signal is therefore removed significantly from the dental-drill noise by the use of the ANC system. Then, by using another ANC system and a high-pass filter in the second step, the residual high-frequency components of the dental-drill noise are eliminated sufficiently. Computer simulations based on recorded dental-drill sounds and real speech signals demonstrate the efficiency of the proposed two-step ANC system for dental-drill noise reduction, both in terms of the noise attenuation performance and the speech quality of the enhanced speech signal, as compared to the conventional two-microphone ANC system under ideal situation. Moreover, results of a subjective listening test with 15 listeners are also given to guarantee satisfied speech quality of the enhanced speech signal employing the proposed two-step dental-drill NR technique

Neural encoding of voice pitch and formant structure at birth as revealed by frequency-following responses

Detailed neural encoding of voice pitch and formant structure plays a crucial role in speech perception, and is of key importance for an appropriate acquisition of the phonetic repertoire in infants since birth. However, the extent to what newborns are capable of extracting pitch and formant structure information from the temporal envelope and the temporal fine structure of speech sounds, respectively, remains unclear. Here, we recorded the frequency-following response (FFR) elicited by a novel two-vowel, rising-pitch-ending stimulus to simultaneously characterize voice pitch and formant structure encoding accuracy in a sample of neonates and adults. Data revealed that newborns tracked changes in voice pitch reliably and no differently than adults, but exhibited weaker signatures of formant structure encoding, particularly at higher formant frequency ranges. Thus, our results indicate a well-developed encoding of voice pitch at birth, while formant structure representation is maturing in a frequency-dependent manner. Furthermore, we demonstrate the feasibility to assess voice pitch and formant structure encoding within clinical evaluation times in a hospital setting, and suggest the possibility to use this novel stimulus as a tool for longitudinal developmental studies of the auditory system

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies