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

Novel Fourier Quadrature Transforms and Analytic Signal Representations for Nonlinear and Non-stationary Time Series Analysis

The Hilbert transform (HT) and associated Gabor analytic signal (GAS) representation are well-known and widely used mathematical formulations for modeling and analysis of signals in various applications. In this study, like the HT, to obtain quadrature component of a signal, we propose the novel discrete Fourier cosine quadrature transforms (FCQTs) and discrete Fourier sine quadrature transforms (FSQTs), designated as Fourier quadrature transforms (FQTs). Using these FQTs, we propose sixteen Fourier-Singh analytic signal (FSAS) representations with following properties: (1) real part of eight FSAS representations is the original signal and imaginary part is the FCQT of the real part, (2) imaginary part of eight FSAS representations is the original signal and real part is the FSQT of the real part, (3) like the GAS, Fourier spectrum of the all FSAS representations has only positive frequencies, however unlike the GAS, the real and imaginary parts of the proposed FSAS representations are not orthogonal to each other. The Fourier decomposition method (FDM) is an adaptive data analysis approach to decompose a signal into a set of small number of Fourier intrinsic band functions which are AM-FM components. This study also proposes a new formulation of the FDM using the discrete cosine transform (DCT) with the GAS and FSAS representations, and demonstrate its efficacy for improved time-frequency-energy representation and analysis of nonlinear and non-stationary time series.Comment: 22 pages, 13 figure

Auditory Spectrum-Based Pitched Instrument Onset Detection

In this paper, a method for onset detection of music signals using auditory spectra is proposed. The auditory spectrogram provides a time-frequency representation that employs a sound processing model resembling the human auditory system. Recent work on onset detection employs DFT-based features describing spectral energy and phase differences, as well as pitch-based features. These features are often combined for maximizing detection performance. Here, the spectral flux and phase slope features are derived in the auditory framework and a novel fundamental frequency estimation algorithm based on auditory spectra is introduced. An onset detection algorithm is proposed, which processes and combines the aforementioned features at the decision level. Experiments are conducted on a dataset covering 11 pitched instrument types, consisting of 1829 onsets in total. Results indicate that auditory representations outperform various state-of-the-art approaches, with the onset detection algorithm reaching an F-measure of 82.6%

Sound and noise

Sound and noise problems in space environment and human tolerance criteria at varying frequencies and intensitie

Audio- ja puhesignaalien aika-asteikon muuttaminen

In audio time-scale modification (TSM), the duration of an audio recording is changed while retaining its local frequency content. In this thesis, a novel phase vocoder based technique for TSM was developed, which is based on the new concept of fuzzy classification of points in the time-frequency representation of an input signal. The points in the time-frequency representation are classified into three signal classes: tonalness, noisiness, and transientness. The information from the classification is used to preserve the distinct nature of these components during modification. The quality of the proposed method was evaluated by means of a listening test. The proposed method scored slightly higher than a state-of-the-art academic TSM technique, and similarly as a commercial TSM software. The proposed method is suitable for high-quality TSM of a wide variety of audio and speech signals.Äänen aika-asteikon muuttamisessa äänitteen pituutta muokataan niin, että sen paikallinen taajuussisältö säilyy samanlaisena. Tässä diplomityössä kehitettiin uusi, vaihevokooderiin pohjautuva menetelmä äänen aika-asteikon muuttamiseen. Menetelmä perustuu äänen aikataajuusesityksen pisteiden sumeaan luokitteluun. Pisteet luokitellaan soinnillisiksi, kohinaisiksi ja transienttisiksi määrittämällä jatkuva totuusarvo pisteen kuulumiselle kuhunkin näistä luokista. Sumeasta luokittelusta saatua tietoa käytetään hyväksi näiden erilaisten signaalikomponenttien ominaisuuksien säilyttämiseen aika-asteikon muuttamisessa. Esitellyn menetelmän laatua arvioitiin kuuntelukokeen avulla. Esitelty menetelmä sai kokeessa hieman paremmat pisteet kuin viimeisintä tekniikkaa edustava akateeminen menetelmä, ja samanlaiset pisteet kuin kaupallinen ohjelmisto. Esitelty menetelmä soveltuu monenlaisien musiikki- ja puhesignaalien aika-asteikon muuttamiseen