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

Heart rate monitoring using human speech spectral features

This paper attempts to establish a correlation between the human speech, emotions and human heart rate. The study highlights a possible contactless human heart rate measurement technique useful for monitoring of patient condition from realtime speech recordings. The distance between the average peak-to-peak distances in speech Mel-frequency cepstral coefficients are used as the speech features. The features when tested on 20 classifiers from the data collected from 30 subjects indicate a non-separable classification problem, however, the classification accuracies indicate the existence of strong correlation between the human speech, emotion and heart-rates

Speech rhythm: a metaphor?

Is speech rhythmic? In the absence of evidence for a traditional view that languages strive to coordinate either syllables or stress-feet with regular time intervals, we consider the alternative that languages exhibit contrastive rhythm subsisting merely in the alternation of stronger and weaker elements. This is initially plausible, particularly for languages with a steep ‘prominence gradient’, i.e. a large disparity between stronger and weaker elements; but we point out that alternation is poorly achieved even by a ‘stress-timed’ language such as English, and, historically, languages have conspicuously failed to adopt simple phonological remedies that would ensure alternation. Languages seem more concerned to allow ‘syntagmatic contrast’ between successive units and to use durational effects to support linguistic functions than to facilitate rhythm. Furthermore, some languages (e.g. Tamil, Korean) lack the lexical prominence which would most straightforwardly underpin prominence alternation. We conclude that speech is not incontestibly rhythmic, and may even be antirhythmic. However, its linguistic structure and patterning allow the metaphorical extension of rhythm in varying degrees and in different ways depending on the language, and that it is this analogical process which allows speech to be matched to external rhythms

Classification of stress based on speech features

Contemporary life is filled with challenges, hassles, deadlines, disappointments, and endless demands. The consequent of which might be stress. Stress has become a global phenomenon that is been experienced in our modern daily lives. Stress might play a significant role in psychological and/or behavioural disorders like anxiety or depression. Hence early detection of the signs and symptoms of stress is an antidote towards reducing its harmful effects and high cost of stress management efforts. This research work thereby presented Automatic Speech Recognition (ASR) technique to stress detection as a better alternative to other approaches such as chemical analysis, skin conductance, electrocardiograms that are obtrusive, intrusive, and also costly. Two set of voice data was recorded from ten Arabs students at Universiti Utara Malaysia (UUM) in neural and stressed mode. Speech features of fundamental, frequency (f0); formants (F1, F2, and F3), energy and Mel-Frequency Cepstral Coefficients (MFCC) were extracted and classified by K-nearest neighbour, Linear Discriminant Analysis and Artificial Neural Network. Result from average value of fundamental frequency reveals that stress is highly correlated with increase in fundamental frequency value. Of the three classifiers, K-nearest neighbor (KNN) performance is best followed by linear discriminant analysis (LDA) while artificial neural network (ANN) shows the least performance. Stress level classification into low, medium and high was done based of the classification result of KNN. This research shows the viability of ASR as better means of stress detection and classification

Speech discrimination using wavelets and zero crossing counting

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Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

CNN AND LSTM FOR THE CLASSIFICATION OF PARKINSON'S DISEASE BASED ON THE GTCC AND MFCC

Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. In this paper, we present a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC

Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification

Hearing versus Listening: Attention to Speech and Its Role in Language Acquisition in Deaf Infants with Cochlear Implants

The advent of cochlear implantation has provided thousands of deaf infants and children access to speech and the opportunity to learn spoken language. Whether or not deaf infants successfully learn spoken language after implantation may depend in part on the extent to which they listen to speech rather than just hear it. We explore this question by examining the role that attention to speech plays in early language development according to a prominent model of infant speech perception – Jusczyk’s WRAPSA model – and by reviewing the kinds of speech input that maintains normal-hearing infants’ attention. We then review recent findings suggesting that cochlear-implanted infants’ attention to speech is reduced compared to normal-hearing infants and that speech input to these infants differs from input to infants with normal hearing. Finally, we discuss possible roles attention to speech may play on deaf children’s language acquisition after cochlear implantation in light of these findings and predictions from Jusczyk’s WRAPSA model