Body Sensor Networking, Design and Algorithms. Saeid Sanei
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rel="nofollow" href="#ulink_a5decebb-556b-5a67-965e-3042d1d9c646">Figure 3.5 (a) ECoG and (b) foramen ovale electrodes denoted by pointers. In...Figure 3.6 New patch type ECG systems including electrodes and wireless conn...Figure 3.7 Electronic stethoscope; Littmann 3200 model.Figure 3.8 Pictorial illustration of the concepts of transmissive and reflec...Figure 3.9 Two different models of oximeters: (a) digital pulse oximeter use...Figure 3.10 A quartz crystal microbalance sensor; a thin film sample is coat...Figure 3.11 (a) An EnzymFET and (b) its electrical output versus urea concen...Figure 3.12 A semiconductor biosensor schematic [51].Figure 3.13 A semiconductor biosensor schematic; backside contacts [51].
4 Chapter 4Figure 4.1 Sample ECG and PPG signals are shown at rest and during motion. R...Figure 4.2 (a) The spectrum of one accelerometer axis; (b) the spectrum of t...Figure 4.3 (a) A breathing cycle of normal capnography waveform with all the...Figure 4.4 ECG and PPG signals are shown at rest. Different respiratory indu...Figure 4.5 A phantom subject equipped with ECG electrodes, pulse oximetry wi...Figure 4.6 Estimation of RR from simultaneous PPG, accelerometer, and ECG si...Figure 4.7 Absorption spectral characteristics of oxygenated (HbO2) and deox...Figure 4.8 Waveform of transmitted or reflected light at two wavelengths: 66...Figure 4.9 (a) An invasive BP measurement using arterial catheter; (b) a non...Figure 4.10 The PTT can be calculated as the time between an ECG R peak and ...Figure 4.11 Patient CBT monitoring using 3M™ SpotOn™ Temperature Monitoring ...
5 Chapter 5Figure 5.1 A subject equipped with PSG monitoring system. Leg EMG electrodes...Figure 5.2 An example of sleep EEG signals and waveforms that appear in diff...Figure 5.3 (a) An NN with two hidden layers and (b) graphical model of a sin...Figure 5.4 One-dimensional convolution for (a) a stride length of 1 (S = 1...Figure 5.5 Two-dimensional convolution: (a) first sliding window; (b) second...Figure 5.6 CNN architecture for a single-channel sleep EEG signal [50].Figure 5.7 The results of sleep stage classification using CNN architecture;...Figure 5.8 A CNN architecture with EEG and EOG as its inputs [66].Figure 5.9 Multitask CNN for joint classification and prediction [67].Figure 5.10 Body postures for a subject lying down: (a) facing upwards; (b) ...Figure 5.11 QRS morphologies for different body postures using coupled elect...Figure 5.12 (a) The WISP is a 145 mm × 20 mm × 2 mm device which needs to be...
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 (SpO2) 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® MiniMod system) 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