Saeid Sanei - Body Sensor Networking, Design and Algorithms

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A complete guide to the state of the art theoretical and manufacturing developments of body sensor network, design, and algorithms In
, professionals in the field of Biomedical Engineering and e-health get an in-depth look at advancements, changes, and developments. When it comes to advances in the industry, the text looks at cooperative networks, noninvasive and implantable sensor microelectronics, wireless sensor networks, platforms, and optimization—to name a few.
Each chapter provides essential information needed to understand the current landscape of technology and mechanical developments. It covers subjects including Physiological Sensors, Sleep Stage Classification, Contactless Monitoring, and much more.
Among the many topics covered, the text also includes additions such as:
● Over 120 figures, charts, and tables to assist with the understanding of complex topics
● Design examples and detailed experimental works
● A companion website featuring MATLAB and selected data sets 
Additionally, readers will learn about wearable and implantable devices, invasive and noninvasive monitoring, biocompatibility, and the tools and platforms for long-term, low-power deployment of wireless communications. It’s an essential resource for understanding the applications and practical implementation of BSN when it comes to elderly care, how to manage patients with chronic illnesses and diseases, and use cases for rehabilitation.

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6 Chapter 6Figure 6.1 (a) DiaMonTech (DMT) and (b) Dexcom G6 devices for noninvasive gl...Figure 6.2 Amplification of colour changes in four video frames from a subje...Figure 6.3 Recording of video frames using a digital video camera, shown in ...Figure 6.4 Region of interest: (a) the face registration has been performed ...Figure 6.5 The PPG signal (15 seconds) generated from a subject's face using...Figure 6.6 The PPG signal (15 seconds) generated from a background behind su...Figure 6.7 The frequency component and zero-pole plot using an AR model are ...Figure 6.8 Estimation of HR using the derived rPPG. Poles not related to the...Figure 6.9 Estimation of RR using derived rPPG [20].Figure 6.10 Estimation of blood oxygen saturation level (SpO 2) using derived...Figure 6.11 Demonstration of body sites to record ECG, finger PPG, and rPPGs...Figure 6.12 The ECG R peaks are denoted as small circles in the top signal. ...Figure 6.13 (a) Functional principle of the peel-away sheath introducer set;...

7 Chapter 7Figure 7.1 GRF measured from a subject walking on a force-plate during stanc...Figure 7.2 (a) Limb joints of a lower human body part; (b) an IMU sensor att...Figure 7.3 Hip, knee, and ankle movements: (a) hip flexion and extension; (b...Figure 7.4 Force signal generated for the left foot – curve with the nonzero...Figure 7.5 Force signal generated for the left foot (green curve) and right ...Figure 7.6 (a) A subject walking on a GAITRite walkway ((b) an example o...Figure 7.7 Camera-based motion analysis (https://codamotion.com) and force-p...Figure 7.8 Cartesian Optoelectronic Dynamic Anthropometer motion analysis sy...Figure 7.9 (a) The markers based on the Optotrak system for a selected subje...Figure 7.10 RGB-D camera setup for multi-Kinect v2 setup.Figure 7.11 Trunk mounted accelerometer (Dynaport ® MiniModsystem) used...Figure 7.12 A subject wearing a single ear-worn accelerometer (e-AR) sensor ...Figure 7.13 (a) Two-sensor configuration [38]; (b) three-sensor configuratio...Figure 7.14 (a) Output acceleration signals for a subject walking on a tread...Figure 7.15 Gait events, from right heel contact (RHC) to right toe-off (RTO...Figure 7.16 Inertial frame from [56].

8 Chapter 8Figure 8.1 Architecture of WBAN.Figure 8.2 General architecture of an HMS. A rapid intervention is expected ...Figure 8.3 Portable fall detection system using a MEMS sensor placed on the ...Figure 8.4 Structure of FoG implementable in hardware [82].Figure 8.5 The PKG system (https://medtechengine.com/article/global-kinetics...Figure 8.6 Recorded data from the wrist-worn PKG watch are processed and the...Figure 8.7 EOG measures for a healthy (control) individual and a schizophren...

9 Chapter 9Figure 9.1 A 2D feature space with three clusters, each with members of diff...Figure 9.2 Schematic diagram of deep clustering [17]. The deep features are ...Figure 9.3 An example of a DT to show the humidity level at 9 a.m. when ther...Figure 9.4 The SVM separating hyperplane and support vectors for a separable...Figure 9.5 Soft margin and the concept of slack parameter.Figure 9.6 Nonlinear discriminant hyperplane (separation margin) for SVM.Figure 9.7 A simple three-layer NN for node localisation in WSNs in 3D space...Figure 9.8 An exponential activation function (ReLU).Figure 9.9 An example of a CNN and its operations.Figure 9.10 A synthetic ECG segment of a healthy individual and its correspo...Figure 9.11 An HMM for the detection of a healthy heart from an ECG sequence...Figure 9.12 CSPs related to right-hand movement (a) and left-hand movement (...

10 Chapter 10Figure 10.1 Four channels of the EEG of a patient with tonic-clonic seizure ...Figure 10.2 (a) An EEG seizure signal including preictal, ictal, and postict...Figure 10.3 A linear model for the generation of signals from the optimally ...Figure 10.4 Mixture of Gaussians (dotted curves) model of a multimodal unkno...Figure 10.5 Morlet's wavelet: real (a) and imaginary (b) parts.Figure 10.6 Block diagram of an adaptive filter for single-channel filtering...Figure 10.7 A network of nodes with a cooperation neighbourhood around senso...Figure 10.8 Multichannel recording of brain signals (EEG) and separating the...

11 Chapter 11Figure 11.1 A typical BAN network.Figure 11.2 BAN on a dummy subject, including sensors, in-body, on-body, and...Figure 11.3 The frequency ranges allocated to narrow and wideband BAN commun...Figure 11.4 A typical ZigBee network topology. ( See color plate section for ...Figure 11.5 The pdfs representing on-body channel-gain agglomerate from ever...Figure 11.6 Routing design for MHRP [96].Figure 11.7 Two-relay cooperative network used in [47].

12 Chapter 12Figure 12.1 A typical node in an energy harvesting sensor network [5].Figure 12.2 Environment (solar and wind) energy harvesting [6].Figure 12.3 Some methods of energy harvesting [8].Figure 12.4 Energy sources (rectangular blocks) and their extraction techniq...Figure 12.5 A translational inertial generator model using mass, spring, and...Figure 12.6 Simplified models of different vibration energy harvesters [14]....Figure 12.7 Two modes of energy harvesting through the triboelectric effect ...Figure 12.8 Simplified model of a photovoltaic cell [14].Figure 12.9 Simplified illustration of thermoelectric effect leading to elec...Figure 12.10 Microbial fuel cell concept: bacteria remove electrons from org...Figure 12.11 The structure of boot-installed energy harvesting system [42]....

13 Chapter 13Figure 13.1 IEEE 802.15.6 security hierarchy [1].Figure 13.2 A DES structure.Figure 13.3 A DES round function.Figure 13.4 An AES structure.Figure 13.5 The process steps of the first round.

14 Chapter 14Figure 14.1 Popular network topologies: (a) peer-to-peer; (b) star topology ...Figure 14.2 (a) Mica2 mote with its sensor board; (b) MicaZ mote.Figure 14.3 TelosB mote.Figure 14.4 IRIS Mote: (a) top and (b) back views.Figure 14.5 iSense core.Figure 14.6 Preon32 wireless module.Figure 14.7 Wasp mote.Figure 14.8 WiSense mote.Figure 14.9 panStamp NRG 3 mote.Figure 14.10 Jennic JN5139-Z01.

Guide

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2 Table of Contents

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