Images for Book: "Medical Devices and Data: technology to measure the body and its interpretation"

C H A P T E R 1 Introduction 1.1 FIGS/FIG-PLATOCAVE Fig 1.1: Illustration of Plato’s parable of the cave. At left, slaves see only flickering shadows. At right, the free see things as they are. Origin: Origin: [Wikimedia] https://commons.wikimedia.org/wiki/ File:An_Illustration_of_The_Allegory_of_the_Cave,_from_Plato%E2%80%99s_Republic.jpg License: Creative Commons Attribution-Share Alike 4.0 International 1 2■Figures for Instrumenting the body: technologies and interpretation 1.2 FIGS/FIG-THERMOMETERINSRUMENT 50 -20 -20 50 (°C) Linear Range Flu Elevated Temperature Δy Height (mm) Δx Fig 1.2: In a liquid thermometer (centre), the liquid expands with temperature (right). Within the linear range, a it has a sensitivity, S= ∆y/∆x. The thermometer can be used to make a decision, such as whether a person has the flu, but only some people (red) have elevated temperature. In this case sensitivity is the true positive rate. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Introduction ■3 1.3 FIGS/FIG-MOVEMENTSHADOWS do di Fig 1.3: Uncertainties in information from the shadows. Dark and light boxes illustrate the depth of shadow. The horse model can move different directions, while the fire flickers. Origin: Image modified from https://en.wikipedia.org/wiki/ File:An_Illustration_of_The_Allegory_of_the_Cave,_from_Plato%E2%80%99s_Republic.jpg License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 2 Beautiful Models 2.1 FIGS/FIG-ANATOMYFEM AB C Fig 2.1: FEM based on segmented MRI data[11], using 6.9m tetrahedral elements: (B) shows the heart; main arteries (red) and veins (blue); (C) shows a slice through the central plane, including the element boundaries. Origin: (C) 2025 Andy Adler. Own work Based on http://doi.org/10.5281/zenodo.13316995 Bartłomiej Grychtol, Andy Adler (2024) "Anatomically accurate torso mesh for EIT" p. 84, Conf. EIT 2024, Hangzhou, China. License: Creative Commons Attribution-Share Alike 4.0 International 5 6■Figures for Instrumenting the body: technologies and interpretation 2.2 FIGS/FIG-BLOODFLOWMODEL R PaPv ArteryArtery Vein Capillaries Q Fig 2.2: A capillary network with resistance R, between an artery and vein (pressures Pa and Pv) and the direction of blood flow (Q). Below is shown a single-compartment model of the network. Origin: (C) 2025 Andy Adler. Own work Modified from [wikimedia] https://commons.wikimedia.org/wiki/File:Blood_vessels-en.svg License: Creative Commons Attribution-Share Alike 4.0 International Original work under Creative Commons Attribution-Share Alike 3.0 Unported license. Beautiful Models ■7 2.3 FIGS/FIG-FEMMODEL V1 V2 V3 Fig 2.3: Solving a FEM. Left: a triangular mesh converted to equivalent resistors (black). At right, parallel resistors (coloured pairs) are converted into equivalent resistances and the voltage distribution on the entire mesh solved. Origin: (C) 2025 Andy Adler. Own work Code: code/ch_models_fem.m License: Creative Commons Attribution-Share Alike 4.0 International 8■Figures for Instrumenting the body: technologies and interpretation 2.4 FIGS/FIG-CURRENTFLOWINCAPACITORS ΔV ΔV C R IR IC Fig 2.4: Current flow in flow resistors (R, narrowed pipe) and capacitors (C, diaphragm). Flow is in-phase with ∆Vin R, but out-of-phase in C. Bottom: current flow in Rand Cas a phasor. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Beautiful Models ■9 2.5 FIGS/FIG-BULKRESISTANCE ℓ A ρ = σ –1 Fig 2.5: Resistor with resistivity, ρ; resistance R=ρ×ℓ/A Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 5 Uncertainty 5.1 FIGS/FIG-ERRORSBULLSEYE Precise Accurate Fig 5.1: Errors as a Bull’s eye chart. Accurate measures (bottom row) hit the bullseye on average. Precise measures are (right column) are consistent. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 17 18 ■Figures for Instrumenting the body: technologies and interpretation 5.2 FIGS/FIG-ERRORSHRILLUSTRATION 60 70 80 90 100 110 120 130 140 60 80 100 120 140 80 90 100 110 120 Actual HR (bpm) Measured HR (bpm) Fig 5.2: Illustration of HR measurement, a 15 s measurement on a watch which runs fast by 2 %. The vertical bar graph is the distribution of measured values at an actual HR = 100bpm. The horizontal bar graph is the distribution of true values which correspond to a measured HR = 100bpm. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Uncertainty ■19 5.3 FIGS/FIG-WILDERNESSBLOCKDIAGRAM Emergency Non-emergency HR stays high 80 (True Positive) 90 (False Positive) HR decreasing 20 (False Negative) 810 (True Negative) HR Measurement: True case: Fig 5.3: Four-quadrant diagram showing HR measurements and severity in our wilderness scenario. Numbers indicate how often each case occured in 1000 hypothetical tests. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 6 Ethics and Medical Devices 6.1 FIGS/FIG-ETHICSFRAMEWORKS Duty, Rights Act Consequence Good Intents Good Consequences Fig 6.1: The ethical analysis of an act can focus on the intentions or the consequences Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 21 C H A P T E R 7 Weighing Scales 7.1 FIGS/FIG-SANTORISWEIGHINGCHAIR Fig 7.1: Santori’s studies of metabolism using an aparatus to weigh the subject, their meals and excreta [15]. Origin: SOURCE: Quincy (1718) [15] License: Out of copyright (1718) 23 24 ■Figures for Instrumenting the body: technologies and interpretation 7.2 FIGS/FIG-BATHROOMSCALE Tension AB Compression C Fig 7.2: A bathroom scale (A). Weight through the feet compresses posts (B) onto which are attached strain gauges, which may be in tension or compression. (C) image of a spiral post spring with two strain gauges. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Weighing Scales ■25 7.3 FIGS/FIG-STRAINGAUGE BA Strain C D E Fig 7.3: Strain Gauge from bathroom scale (A). Layout of conductive traces, with active (B) and compensation (C) strain gauge. Deformation under strain in active (D), and compensation directions (E). Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 9 Blood Pressure Measurement 9.1 FIGS/FIG-HYPERTENSIONDEATHS Ischaemic heart disease Other cardiovascular diseases Stroke Chronic kidney disease 2019 1990 0% 5% 10% 15% 20% 9% 6% Fig 9.1: Percentage of global deaths attributable to high systolic blood pressure (1990 and 2019), by cause of death [17] Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 33 34 ■Figures for Instrumenting the body: technologies and interpretation 9.2 FIGS/FIG-INVASIVEBLOODPRESSURE Flush valve Gel Transducer Sensing BP port Stopcock Connector Fig 9.2: Blood pressure transducer and its typical connection to a patient’s artery and saline drip. The transducer itself is shown below. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Blood Pressure Measurement ■35 9.3 FIGS/FIG-DIFFERENTIALPRESSURETRANSDUCER PReference PApplied RActive RComp RActive R Comp V − + In.Amp Fig 9.3: Differential pressure transducer Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 36 ■Figures for Instrumenting the body: technologies and interpretation 9.4 FIGS/FIG-NONINVASIVEBP Pressure (mmHg) Cuff Pressure (mmHg) Korotkoff sound intensity Pump PCuff Stethoscope Psystolic Pdiastolic Fig 9.4: Non-invasive blood pressure measurement, using the auscultatory method. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Blood Pressure Measurement ■37 9.5 FIGS/FIG-OSCILLOMETRICMETHOD 0 100 200 -1 0 1 15 20 25 30 35 40 45 (time, s) -1 0 1 PCuff [mmHg] Pcuff, HPF ECG (mV) 260ms 300ms Pdiastolic Pmean (MAP) Psystolic AsAmAd Fig 9.5: Oscillometric method showing Pcuff (top row, absolute, and middle row, highpass filtered) and ECG. Shaded background illustrated the pressure envelope. Timings are between QRS peak and pressure peak. The pressure pulses before 17 s are due pumping of the cuff blub. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 10 Optical Measurements 10.1 FIGS/FIG-OPTICALPROPERTIESHB 300 400 500 600 700 800 Wavelength [nm] 1.0 0.1 10 100 Visible Light Ultravoilet Green Red Infrared Attenuation [/mm blood] Hb HbO2 Fig 10.1: Attenuation of a 1 mm thick layer of blood at hormal hematocrit, for oxy- (red) and deoxy-hemoglobin (blue), as a function of light wavelength. Origin: (C) 2025 Andy Adler. Own work Data from https://omlc.org/spectra/hemoglobin/summary.html License: Creative Commons Attribution-Share Alike 4.0 International 39 40 ■Figures for Instrumenting the body: technologies and interpretation 10.2 FIGS/FIG-SAMSUNGWATCHOPTICALSENSOR PPG Sensor Optical Barrier Fig 10.2: Samsung Galaxy Watch 3 (released 2020). The sensor has a central LED, an optical barrier and surrounding photodetectors. [6] Origin: (C) 2025 Andy Adler. Own work Image modified from [6] License: Creative Commons Attribution-Share Alike 4.0 International Optical Measurements ■41 10.3 FIGS/FIG-OPTICALPATHSSKIN Epidermis (0.1 – 1mm) Dermis (1 – 4 mm) Subcutaneous (1 – 30mm) LED Photodetector Green ~530nm Red ~670nm IR ~800nm Optical Barier Fig 10.3: A LED and photodetector placed onto skin, and the optical paths of green, red and IR light. Skin illustrates (left to right): sweat pore, capillaries, gland, hair follicle, nerve. Origin: (C) 2025 Andy Adler. Own work Modified from https://de.wikipedia.org/wiki/Haut#/media/Datei:Schemazeichnung_haut.svg License: Creative Commons Attribution-Share Alike 3.0 International 48 ■Figures for Instrumenting the body: technologies and interpretation 11.4 FIGS/FIG-SEMG 2 4 6 8 2 4 6 8 [s] 0.2 mV spherical power grasp wrist flexion Fig 11.4: Surface EMG recordings (raw: lighter colour, and dark: signal envelope) from electrodes on the hand during two different tasks (data from [9]). Origin: (C) 2025 Andy Adler. Own work based on data from [9] License: Creative Commons Attribution-Share Alike 4.0 International Electrophysiology ■49 11.5 FIGS/FIG-RESTINGEEG 1 s Alpha (8–15 Hz) Beta (15–30 Hz) Gamma (30–90 Hz) Theta (4–10 Hz) Delta (1–4 Hz) Fig 11.5: Example EEG signals (data from [13]) Origin: (C) 2025 Andy Adler. Own work based on data from [13] License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 12 Electrocardiogram 12.1 FIGS/FIG-ELECTROCARDIOGRAPH Fig 12.1: Photo of an early electrocardiograph [2]. The patient’s hands and foot are immersed in saline solution as electrodes. Origin: (C) Welcome Collection. https://wellcomecollection.org/works/km4x3rye License: Image out of copyright 51 52 ■Figures for Instrumenting the body: technologies and interpretation 12.2 FIGS/FIG-SIGNALSMIMIC 80 100 120 PART [mmHg] 0.5 1 II 0.2 0.4 V 0.2 0.4 AVR 312 314 316 318 320 322 324 326 328 Resp Paorta PLV VLV P R T QS Pressure (mmHg) 120 80 40 0 Volume (mL) 130 90 50 ECG Fig 12.2: Arterial blood pressure, ECG, Respiratory signals from a critical-care patient [5] (left), and the Wiggers’ diagram (fig 8.3, right). Origin: (C) 2025 Andy Adler. Own work SOURCE: MIMIC III database: record 3531764_0003, Physionet License: Creative Commons Attribution-Share Alike 4.0 International Electrocardiogram ■53 12.3 FIGS/FIG-HEARTCONDUCTION Depolarisation Depolarised cells Repolarization A B C D P R T Q S Fig 12.3: Electrical Conduction in the heart. Upper left: slice of model heart (ch ??). Upper right: The ECG signal corresponding to borrom row images. A: beginning of atrial depolarization; B: end of atrium depolarization, beginning of ventricular depolarization; C: active ventricular depolarization; D: repolarization of ventricles. Origin: (C) 2025 Andy Adler. Own work Modified from https://en.wikipedia.org/wiki/File:Basic_representation_of_cardiac_conduction.gif License: Creative Commons Attribution-Share Alike 4.0 International Original work: Creative Commons Attribution 2.5 Generic license. 54 ■Figures for Instrumenting the body: technologies and interpretation 12.4 FIGS/FIG-BIHARRHYMIASAMPLE 50 51 52 53 0 0.5 1 50 51 52 53 -1 0 1 50 51 52 53 -1 0 1 2 40 41 42 43 -1 0 1 56 57 58 58.5 59 -2 -1 0 1 60 61 62 63 [s] -2 -1 0 1 A B C D1 D2 D3 Fig 12.4: Several ECG signals of arrhythmias, from[10]. A: Normal beat, B: Paced beat, C: Left bundle branch block beat also showing ST segment depression, D1: Premature ventricular contraction and the start of V-fib (ventricular fibrillation) D2: V-fib D3: end of V-fib, followed by premature ventricular contraction Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Electrocardiogram ■55 12.5 FIGS/FIG-ECGLEADPLACEMENT ..... . V1V 2V3V4V 5V6 . VLA VRA . . VLL Einthoven’s Triangle V RL . Fig 12.5: Electrodes for a 12-lead ECG Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 13 The body electric 13.1 FIGS/FIG-ELECTRORECEPTION Fig 13.1: Electroreceptors in a shark’s head Origin: (C) 2007 Date 6 March 2007 https://commons.wikimedia.org/wiki/File:Electroreceptors_in_a_sharks_head.svg License: Public Domain. 57 64 ■Figures for Instrumenting the body: technologies and interpretation 13.8 FIGS/FIG-IMPEDANCECARDIOGRAPHYMODELS A B C D E Fig 13.8: FEM Model of impedance-cardiography measurements (A & B). Transverse slice image shows lines of equal voltage (equipotentials) for diastole (C) and systole (D). E: sensitivity (darker = more sensitive) distribution to imedance changes. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International The body electric ■65 13.9 FIGS/FIG-LITHICDATASAMPLE Z’max Z’max 0 0.5 1 -0.1 0 0.1 0.2 -20 0 20 -5 0 5 300 350 400 450 100 200 300 400 299 299.5 185 190 195 463 463.5 time [s] ECG (mV) dBioZ/dt (Ω) PPG (AU) Q B X PEP = 75 ms PEP = 60 ms VET = 370 ms VET = 335 ms RR = 815 ms RR = 605 ms PTT = 235 ms PTT = 215 ms exercise Fig 13.9: Sample of measured data, showing the ECG, BioZ and PPG (arbirary units) at rest (t < 310s), exercise (320s < t < 360s), and rest again (t > 370s). Right columns zoom 1.2 s of data before and after exercise are shown. Parameters: are R-R interval (=HR−1), Pre-ejection period (PEP), Ventricular ejection time (VET), Pulse-transit time (PTT) and Z’max=|dZ dt |max from which stroke volume is calculated. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 14 Breath of life 14.1 FIGS/FIG-CHESTLUNGS Abdomen Trachea Heart Ribs Diaphragm Inter - costal muscles VL PMus PAO VL ⋅ VL PCW VL PL + Alveoli Artery Vein Capillary = A B C Fig 14.1: A: Chest anatomy and breathing movements. B: inset of lung tissue, showing air and blow flow to alveoli. C: Model of lungs mechanics, with separate lung and chest components. Origin: (C) 2025 Andy Adler. Modified images from several open sources. License: Creative Commons Attribution-Share Alike 4.0 International 67 68 ■Figures for Instrumenting the body: technologies and interpretation 14.2 FIGS/FIG-LUNGFLOWSVOLUMES 6.0L 3.0L 3.5L 1.5L TLC FRC IC RV ERV TV IRV Inspiratory Flow Expiratory Flow Fig 14.2: Lung Volumes (above) and flows (below) for two tidal breaths and a maximal inspiration and expiration. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Breath of life ■69 14.3 FIGS/FIG-PNEUMOTACH VL ⋅ Heater Spirometer Pressure Transducer ΔP θ Ultrasound Transducer C AB Mask Air flow resistance Fig 14.3: Three ways to measure air flow: A) pneumotach using a pressure transducer to measure the pressure drop over a flow resistance. B) pneumotach measuring the difference in travel time of ultrasound with and against the flow. C) water-filled spirometer filling an air chamber and writing on a rotating drum. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 70 ■Figures for Instrumenting the body: technologies and interpretation 14.4 FIGS/FIG-STATICLUNGMECHANICS FRC IC RV ERV TV IRV –20 0+20 [mmHg] 3.0L 1.5L 3.5L 6.0L PCW PL PRS = PCW || PL ΔPmus TV TV ΔPH ΔPP FRCH FRCP H P A B C D Fig 14.4: Left: Lung Volumes. Centre: Volume volume vs ∆Pmus, and the chest-wall, PCW, lung, PL, and respiratory system, PRS =PCW∥PL, pressure. Inset Right: solid lines indicate the healthy condition (H) and dotted lines indicate pathological (P) stiffer lungs. Inset Below: Pressures for the same tidal volume. Compared to H, P has a lower FRC and increased pressure to achieve TV. Origin: (C) 2025 Andy Adler. Own work. Adapted from Agostini and Mead. License: Creative Commons Attribution-Share Alike 4.0 International Breath of life ■71 14.5 FIGS/FIG-LUNGLINEARMODEL PMus ⋅ RAW PAO PMus PAO CRS VL Fig 14.5: Dynamic lung mechanics (left) and equivalent electrical model (right). The image illustrates flow resistances in the upper and lower airways. Origin: (C) 2025 Andy Adler. Own work. License: Creative Commons Attribution-Share Alike 4.0 International 72 ■Figures for Instrumenting the body: technologies and interpretation 14.6 FIGS/FIG-DYNAMICLUNGMECHANICS FRC TV 3.0L 3.5L ΔFRC Inspiratory Flow Expiratory Flow ΔPmus Healthy Obstructive (RL×2) Restrictive (CL÷2) Fig 14.6: Dynamic lung mechanics of healthy, obstructive and restrictive lungs. In each case muscular pressure is generated to first, inhale at constant flow, and then hold the breath. Finally muscular pressure is dropped to zero. Origin: (C) 2025 Andy Adler. Own work. License: Creative Commons Attribution-Share Alike 4.0 International Breath of life ■73 14.7 FIGS/FIG-CPAPWITHEIT V 10 20 30 40 50 60 70 [s] R L Left-dorsal Right-dorsal Left-ventral Right-Ventral Fig 14.7: Lung volume (in four lung quadrants, measured with EIT) in a brachysephalic dog CPAP pressure is increased from 8 to 12 cmH2Oat t= 23s. Images show the dog on its back and the distribution of tidal volume at the indicated time points [1]. Origin: (C) 2025 Andy Adler. Data from Araos et al (in press) License: Creative Commons Attribution-Share Alike 4.0 International 80 ■Figures for Instrumenting the body: technologies and interpretation 15.6 FIGS/FIG-DOPPLERWAVEFORMS TT (1/fT) TR (1/fR) Δd d ΔT Δd = u ( TT + ΔT ) Δd = c ΔT c c u TT Fig 15.6: Doppler waveforms for an object with velocity cand sound speed c. Tranmitted sound is at f=fT, and recieved at f=fR. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 16 Stress 16.1 FIGS/FIG-TYPESOFSTRESS Fig 16.1: Types of stress: psychological, physiological, and mechanical. Origin: Work by Liz Adler (c) 2025-07-23 for this publication. License: Creative Commons Attribution-Share Alike 4.0 International 81 82 ■Figures for Instrumenting the body: technologies and interpretation 16.2 FIGS/FIG-MECHANICSSTRESSTYPES Tension Compression Shear Bending Torsion L ½ ΔL ½ ΔL Fig 16.2: Types of Mechanical stress. Left: strain for tension, σ= ∆L/L Origin: (C) 2025 Andy Adler. Own work Modified from https://commons.wikimedia.org/wiki/File:Different-types-of-mechanicalstress_EN.svg License: Creative Commons Attribution-Share Alike 4.0 International Stress ■83 16.3 FIGS/FIG-STRESSSTRAINCURVES Δε Strain (ε=ΔL/L) Stress (σ = F/A) Δσ Steel Bone Tendon Failure Ultimate strength “Toe region” Fig 16.3: Representative stress-strain curves of engineering materials (steel), bone and soft tissue (tendon). At low strains bone and steel have a linear region characterized by a slope (∆σ/∆ϵ). Soft tissue has a toe region where deformation happens with little stress. At higher strains all materials show a yield region, an ultimate strength and finally, failure. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 84 ■Figures for Instrumenting the body: technologies and interpretation 16.4 FIGS/FIG-NERVOUSSYSTEM Nervous System Central Nervous System (Brain + spinal cord) Peripheral Nervous System Somatic N.S. (with senses & voluntary muscles) - Sensory N.S (nerve input) - Motor N.S (motor output) Autonomic N.S. (with internal organs & glands) - Sympathetic N.S (arousing) - Parasympathetic N.S. (calming) Fig 16.4: Divisions of the nervous system (NS): the central NS and the peripheral NS (composed of a Somatic and Autonomic parts) Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Stress ■85 16.5 FIGS/FIG-SYMPATHETICHPA Hypothalamus Pituitary gland Sympathetic N.S - Immediate - Short duration - Blood Pressure🡅 - Heart Rate🡅 - Blood Glucose🡅 - Core Temperature🡅 - Smooth Muscle Tone🡇 Hypothalamic-Pituitary axis (HPA) - After ~ 10 minutes - Longer duration (hours) - Blood Pressure🡅 - Blood Glucose🡅 - Core Temperature🡅 - HR Variability🡇 - Inflammation🡇 Adrenaline Cortisol/ Glucocorticoids Adrenal cortex Fig 16.5: Activation via the sympathetic nervous system and the HPA. Origin: (C) 2025 Andy Adler. Own work Including images from https://en.wikipedia.org/wiki/File:HPA-axis_-_anterior_view_(with_text).svg License: Creative Commons Attribution-Share Alike 4.0 International 86 ■Figures for Instrumenting the body: technologies and interpretation 16.6 FIGS/FIG-STRESSLIONS Fig 16.6: Psychologial and physiological stress (from: smbc-comics.com/comic/lions) Origin: (C) 2021 Zach Weinersmith ([email protected]) Origin info: Zac Weinersmith at smbc-comics.com License: smbc-comics.com/comic/lions Permission granted by email: 2025-07-20 Stress ■87 16.7 FIGS/FIG-LAZARUSMODEL Primary Appraisal What’s at stake? - Harm/Loss: (Previously hurt) - Threat? (Maybe future harm) - Challenge? (Maybe gain / benefit) Secondary Appraisal What can I do? - Available resources (Are they sufficient?) - Coping options time, energy, finances, abilities, social Stressor Stress Response Fig 16.7: Stress response as a two-step appraisal [7]. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 88 ■Figures for Instrumenting the body: technologies and interpretation 16.8 FIGS/FIG-HRVSTIMEOFDAY 6h00 65 70 75 Female Male Heart Rate (beat/min) 12h00 18h00 24h00 Fig 16.8: Average Heart Rate vs time of day (data from [12]) Origin: (C) 2025 Andy Adler. Own work Data from [12] License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 17 Into the digital 17.1 FIGS/FIG-ADCBLOCKDIAGRAM Continuous-Time signals x(t) Discrete-Time signals x[n] Quantized signals xq[n] Fig 17.1: Block diagram of analog-to-digital conversion Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 89 96 ■Figures for Instrumenting the body: technologies and interpretation 18.4 FIGS/FIG-SMOOTHINGMEANVSMEDIAN 0.35 0.4 0.45 0.35 0.4 0.45 0.35 0.4 0.45 [s] Original Mean Median Fig 18.4: An arterial pressure waveform (blue) and with interference (red) (60 Hz noise and a disconnect at t= 0.37s). The original signal is grey. Left: original signals. Signals filtered with a mean (centre) and median (right) filter. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Filtering ■97 18.5 FIGS/FIG-PANTOMPKINSALG -1 0 1 2 -1 0 1 2 A -1 0 1 2 B -1 0 1 2 C -1 0 1 2 D 4 4.5 5 5.5 6 6.5 -1 0 1 2 E 6.5 6.55 6.6 6.65 6.7 F Fig 18.5: Stages of the Pan Tompkins Algorithm for QRS detection: A: Original signal, B: Low-pass filter, C: High-pass filter, D: Derivative E: Absolute value F: Low-pass filter. Next a threshold is selected an peaks detected. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 98 ■Figures for Instrumenting the body: technologies and interpretation 18.6 FIGS/FIG-ENSEMBLEAVERAGING 3 3.5 4 4.5 5 5.5 6 6.5 [s] -1 0 1 mV ... + 6 samples 60 samples Fig 18.6: Ensemble averaging. A synchronization event (the QRS peak) is identified in a signal (left) and then each occurance of the event is time aligned (right) and the average signal taken. Averages for 6 and 60 events are shown. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 19 Learning Machines 19.1 FIGS/FIG-DERSANDMANNIMAGE Fig 19.1: The hero fell in love with a machine in Hoffmann’s Der Sandmann Origin: (C) 1816 by Hoffmann. Work available from https://commons.wikimedia.org/wiki/File:Hoffmann_sandmann.png License: Work (1816) out of copyright 99 100 ■Figures for Instrumenting the body: technologies and interpretation 19.2 FIGS/FIG-MLFEATURES 1019.4 0 1 2 1019 1020 1021 [s] 0 1 2 -0.6 -0.4 -0.2 0 0.2 0.4 -0.5 0 0.5 Δvx Δvy Δty Δt xx = Δvx /Δtx y = Δvy /Δty Interval with QRS Interval without QRS [V] [V/s] [V/s] Fig 19.2: Features for QRS detection. For each 10 ms period the signal slope ∆v/∆tis calculated (bottom left). At each time, we define a feature vector (x,y)of the currentand former-period’s slope. Right: time interval features with (+) and without (o) a QRS event. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Learning Machines ■101 19.3 FIGS/FIG-MLCLASSIFICATION 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 True class: C=1 C=0 Classify=>1 Classify=>0 Classify =0 Classify = 1 Class=0 TN=49 FP=1 Class=1 FN=1 TP=49 Fig 19.3: A classifier with two features, along with sample data and the gold-standard classification Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 102 ■Figures for Instrumenting the body: technologies and interpretation 19.4 FIGS/FIG-MLPERCEPTRON 0 1 0 1 Classify=>1 Classify=>0 Σ f x y ⋮ z wy wzx y f( wy×x + wy×y + ... wz×z + b ) wx b Fig 19.4: Left: a neuron and artificial one. Right: linear classification of data with one neuron. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Learning Machines ■103 19.5 FIGS/FIG-MLHILLCLIMBING opentopomap.org/#map=16/45.48725/-75.86068 Fig 19.5: One way to climb: take the most direct way up at each step Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 104 ■Figures for Instrumenting the body: technologies and interpretation 19.6 FIGS/FIG-MLMULTILAYER 1 10 100 1,000 10,000 100,000 -2 0 0 0.2 0.4 0.6 0.8 1 0 0.5 1 0 0.2 0.4 0.6 0.8 1 0 0.5 1 Σ f 2 2 3 4 5 Objective Function Parameter Values Σ f Σ f b3 wy3 wx3 wx1 wy2 wx2 wy1 b2 b1 x y Fig 19.6: Two-layer artificial neural network with 9 parameters. Below, the value of the objective function and of each parameter as a function of training iteration number. Right: the optimized classification boundary. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International Learning Machines ■105 19.7 FIGS/FIG-MLFITTING Under-fitting Appropriate-fitting Over-fitting Fig 19.7: Over-, underand appropriate-fitting Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International 112 ■Figures for Instrumenting the body: technologies and interpretation 21.2 FIGS/FIG-PULSEOXIMETERTISSUECOMPONENTSSKIN LED Pulsatile variations of arterial blood Time Arterial Blood Venous Blood Tissue Attenuation Photodetector Skin Unknown Thickness 700 800 λ [nm] Red Infrared Hb HbO2 Tissue Skin 1.0 0.1 Attenuation [/mm blood] 600 Fig 21.2: Left: Tissue components and (Centre) variation in the components of tissue attenuation over time. Right: Optical attenuation and range of values for skin and tissue. Note that both skin thickness and attenuation vary between people. Origin: (C) 2025 Andy Adler. Own work License: Creative Commons Attribution-Share Alike 4.0 International C H A P T E R 22 Mean data? Peak data? 22.1 FIGS/FIG-INFORMATIONSTRESS Fig 22.1: Stressful information? Origin: Work by Liz Adler (c) 2025-08-05 for this publication. License: Creative Commons Attribution-Share Alike 4.0 International 113 114 ■Figures for Instrumenting the body: technologies and interpretation 22.2 FIGS/FIG-MOONJUPITER Fig 22.2: Galileo’s sketch of Jupiter’s moons, subset with observations Jan 10–15, 1610. Origin: Galileo sketch of Jupiter’s moons https://www.astro.umontreal.ca/ paulchar/grps/site/images/galileo.4.html License: Out of copyright Mean data? Peak data? ■115 22.3 FIGS/FIG-MORTALITYHAZARD 0 1 2 3 4 # Healthy Habits 1 2 5 10 Mortality Hazard Ratio Normal Overweight Obese Fig 22.3: Mortality ratio (risk of dying over the study years divided by the risk for the healthiest group) versus healthy habits Origin: (C) 2025 Andy Adler. 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