Index

  • 2HD44780 controllers, 128–129
  • Abbott Healthcare, 6
  • Accelerometer, 83–88, 91–94
  • Adjusted R-squared, 156
  • AlexNet, 326
  • Algorithm-level–based, 69–71, 75
  • Alopecia, 307, 311
  • Anaplastic thyroid cancer, 37
  • Anatomy, 60, 63
  • Apple watch, 5–6
  • APR 33A3 voice kit, 92
  • APR300 kit, 87
  • Apriori algorithm, 262
  • Architecture, 25
  • Architecture of SDN-IoT for healthcare system, 344–345
  • Arduino mega 2560 microcontroller, 83, 88, 92
  • Area under curve, 152
  • ARM microcontroller, 130
  • ARM7 TDMI core, 120–121
  • Artificial neural networks, 13
  • Assisted vision smart glasses, 109
  • Association, 261
  • Atrial fibrillation, 5–6
  • Attacks, 45
  • Attribute-based access control (ABAC), 218
  • Attribute-based encryption (ABE), 219
  • Audition of healthcare data, 27
  • Augmented reality (AR), 85
  • Authorization, 46
  • Automate, 296, 303
  • Automated home safety and security, 104
  • Automated wheel chair, 110
  • Automatic lights control, 104
  • Automatic methods, 60
  • Availability, 47
  • AWAK Techologies, 11
  • Bagging, 69–72, 75, 76
  • Barriers of IoT, 45
  • Bernoulli distribution, 146
  • Binomial distribution, 147
  • BlueBox, 11
  • Bluetooth, 181
  • Bluetooth low energy (BLE), 198
  • Boosting, 71
  • Braille watch, 109
  • Brain tumor, 69–71, 73, 75, 76, 295–297, 300, 302, 303
  • Bridgera rescue, 9
  • Brown out detector, 125
  • C4ISRT system, 117–118
  • CAGR, 37–38
  • Canny edge algorithm, 86
  • Chronic disease, 306
  • Clinical-grade wearables, 5–7
  • Cloud computing, 116, 118
  • Cloud layer, 26
  • CloudIoT, 174
  • Clustering, 261, 262
  • Clusters, 300
  • CNN algorithm, 328–329
  • Compaq, 2
  • Computed tomography (CT) images, 59–65
  • Conceptual framework of IoT, 44
  • Conditional probability, 296
  • Conductive gel, 14
  • Confidentiality, 46
  • Confirm Rx, 6
  • Constrained application protocol (CoAP), 188
  • Consumer fitness smart wearables, 4–5
  • Correlation matrix, 311, 312
  • Covariance matrix, 311–312
  • Cross-validation, 267
  • Cryptographic technique, 117
  • Crystal oscillator, 123–124
  • Current controlled oscillator (CCO), 124
  • Current health, 6
  • Data analysis, 142
    • descriptive analysis, 142
    • diagnostic analysis, 143
    • predictive analysis, 143
    • prescriptive analysis, 143
  • Data bandwidth, 4
  • Data collection, 326–327
  • Data privacy, 4
  • Data security, 4
  • Data segments, 272
  • Data-intensive, 258
  • Data-level, 69–71
  • Decision boundary, 298
  • Decision tree algorithm, 38–39
  • Deep learning architecture, 326
  • Definition of IoT, 44
  • Denial of service (DoS), 206
  • Device layer, 25
  • Diabetes mellitus, 306
  • Diagnosis, 256
  • Differently abled, 102
  • Discrete probability distribution, 146
  • Douglas-Peucker algorithm, 85
  • E-alarm, 110
  • Edema, 300
  • Edge computing-based solution, 321
  • E-health sensors, 118
  • Elderly people, 101
  • Electronic health record (EHR), 138, 183
  • Embedded C, 131
  • Ethical issues in telehealth, 12
  • Evaluation metrics, 150
    • classification accuracy, 150
    • confusion matrix, 150
    • logarithmic loss, 151
  • Exponential distribution, 149
  • F1 score, 153
  • Feature extraction, 325
  • Feeding robot, 110
  • Finger reader, 109
  • Fog layer, 26
  • Follicular thyroid cancer, 36–37
  • Fuzzy logic, 13
  • Fuzzy rules for prediction of diabetes, 164
  • Fuzzy rules for prediction of heart disease, 163
  • Gestational diabetes mellitus, 306
  • Gesture to speech system (G2S system), 84
  • Glioblastoma, 300–302
  • Glioblastoma multiforme (GBM), 70 GPS receiver, 135
  • GPS-based system, 117, 126–127, 131
  • Gray level co-occurrence matrices (GLCMs), 323
  • Growth of IoT, 102
  • GSM, 116
  • Hand gestures, 87
  • Health kiosks, 9
  • Healthcare, 104
  • Heart rate sensor, 129
  • Heartbeat sensor, 90, 131, 133
  • Home automation, 103
  • Horizon seizure, 272
  • Hyperthyroidism, 34
  • Hypothyroidism, 35
  • Image processing, 325
  • Imbalance data, 69–72, 75, 76
  • Inception net, 326
  • InceptionV3, 324
  • Information gain, 310, 311
  • Infrastructure as a service (IaaS), 184
  • Ingestible event marker (IEM), Proteus, 7
  • Insulin, 306
  • In-system programming/In-application programming (ISP/IAP), 121
  • Integrity, 46
  • Integrity of patient data, 27
  • Internet engineering task force (IETF), 187
  • Internet of medical things (IoMT), 2–14
    • community segment, 9
    • evolution of IoT to, 2–3
    • in healthcare logistics and asset management, 12–13
    • in-home segment, 8–9
    • IoMT use in monitoring during COVID-19, 13–14
    • market size, 4
    • reduction of hospital-acquired infections, 8
    • smart pills, 7–8
    • smart wearable technology, 4–7
    • telehealth and remote patient monitoring, 9–12
  • Internet of Things (IoT), 21, 101, 174, 180
  • Internet of things, cloud technology, 139, 169
  • Internet protocol security (IPsec), 187
  • Introduction to IoT, 44
  • IoT devices, 103
  • IoT health system for speech-impaired person,
    • introduction, 82–84
    • literature survey, 84–86
    • procedure, 86–92
    • results, 93–94
  • IoT system for soldiers,
    • implementation, 129–131
    • introduction, 116–117
    • literature survey, 117–118
    • results and discussions, 133, 135–136
    • system design, 119–129
    • system requirements, 118–119
  • IoT-based automated healthcare system, 335–341
    • network function virtualization, 337
    • sensor used in IoT devices, 338–341
    • software-defined network, 336–337
  • Keil ìVision3, 131
  • Keras, 322, 327
  • Kinect devices, 85
  • K-nearest neighbor (KNN) algorithm, 323
  • Knowledge-based, 63
  • Labeled faces in the wild (LFW), 322
  • LCDs, 128–129
  • Leap motion device, 85
  • LeNet, 326
  • LifeVest, 14
  • Linear embedding-CNN (LLE-CNN), 324
  • Literature survey, 343–344
  • LM35, 89
  • LM35 temperature sensor, 130, 135
  • Logistic regression, 39–40, 307
  • Machine learning, 138
  • MAFA, 324
  • Magnetic resonance image (MRI), 69–71, 75, 76, 295–297, 300, 301
  • Management, 256
  • Masked face detection, implementation framework for,
    • implementation approach, 325–328
    • introduction, 320–321
    • literature review, 321–325
    • observation and analysis, 328–332
  • Mean absolute error, 154
  • Mean squared error, 154
  • Medical-grade wearables, 6
  • Medline, 10
  • Medullary thyroid cancer, 37
  • Memory mapping control, 125
  • M-health system, 117
  • ML algorithm, 258
  • MobileNet mask, 323, 326
  • MobileNetV2, 322, 326
  • Models,
  • Morals of the use of calculations in medicinal services, 284
  • Multi-canal EEG, 265
  • MyWay, 8
  • Naïve Bayes algorithm, 40
  • Navigation apps for hospitals, 8
  • NCNN, 321
  • Necrotic, 301
  • Need for IoT, 102
  • Neural network, 158, 263
  • Normal distribution, 148
  • Northwestern university, 13
  • NVIDIA Jetson nano, 322
  • On-chip flash memory, 122
  • On-chip static RAM, 122
  • Ongoing preferences of ML in human services, 281
  • OpenCV, 322, 325–326
  • Opportunities in healthcare quality improvement, 288
  • Otsuka Pharmaceutical, 7
  • Panic button, 135
  • Papillary thyroid cancer, 36
  • PCA, 273
  • Personal emergency response system (PERS), 8–9
  • Phase-locked loop (PLL), 124
  • Pin control block, 122–123
  • Pixels,
  • Platform as a service (PaaS), 184
  • Poisson distribution, 148
  • Polydipsia, 307, 310, 311
  • Polyphagia, 307, 310, 311
  • Polyuria, 307, 310
  • Precision, 153, 312
  • Preictal state, 270
  • Pre-labeled data, 297, 298, 303
  • Probability distributions, 145
  • Profound learning, 263
  • Proteus digital health, 7
  • Pulmonary CT image, 60
  • Pulse oximeter, 13
  • Pulse rate sensor, 130
  • Python, 325–326
  • Radio frequency identifier (RFID), 181, 185, 188
  • Radio-frequency identification (RFID), 2, 12–13
  • Random forest, 39
  • Rapid cycling mood disorders, 34
  • Rasberry Pi, 118, 322
  • Raspberry Pi-based real-time face mask recognition system, 323
  • Real monitor software, 121
  • Real-time clock (RTC), 121
  • Real-time debugging, 126–127
  • Real-world masked face dataset (RMFD), 322
  • Recall, 153
  • Reduction of hospital-acquired infections, 8
  • Region growing process, 60
  • Region of interest, 62, 64, 65
  • Reinforcement learning, 145
  • ReLU, 313, 325
  • Remote patient monitoring, 11
  • Reset timer, 124
  • ResNet, 326
  • Respiration sensor, 90
  • Root mean squared error, 155
  • Root mean squared logarithmic error, 155
  • R-squared/adjusted R-squared, 156
  • Sampling,
    • cluster-based, 70–72
    • over, 71, 73
    • random, 71
    • under, 71–73
  • Science citation index, 35
  • SDN-based IoT framework, 341–343
  • Secure socket layer (SSL), 187
  • Seed pixel, 300
  • Segmentation,
  • Seizure identification, 265
  • Seizure prediction, 271
  • Selenium effect, 35
  • Semi-automated, 60, 61
  • Semi-structured data, 142
  • Semi-supervised learning, 145
  • Serum, 36
  • Shirley Ryan AbilityLab, 13
  • Simulated masked face dataset (SMFD), 322
  • Single-nucleotide polymorphisms, 307
  • Single-shot detector, 322
  • Smart appliances, 104
  • Smart assistant, 105
  • Smart blood pressure monitor, 107
  • Smart coffee machines, 106
  • Smart glove, 111
  • Smart glucose monitor, 107
  • Smart hearing aid, 110
  • Smart insulin pump, 108
  • Smart oven, 105
  • Smart pills, 7–8
  • Smart refrigerators, 106
  • Smart thermometer, 107
  • Smart wand, 109
  • Smart washers, 106
  • Smart watches, 107
  • Smart wearable technology, 4–7
    • clinical-grade wearables, 5–7
    • consumer fitness smart wearables, 4–5
  • SMFD, 324
  • Social science citation index, 35
  • Software as service (SaaS), 184
  • Software defined network (SDN), 215
  • Software requirement specification (SRS), 119
  • SSDMNV2 approach, 322
  • SSDMNV2 technology, 321
  • SSDNETV2 algorithm, 330–331
  • Structured data, 142
  • Supervised learning, 144, 259, 260
    • decision trees, 144
    • pattern recognition, 144
    • regression, 144
    • support vector machine, 144
    • Support vector machines, 39
  • SVM, 273, 331–332
  • SVM technology, 321
  • Taptilo Braille device, 110
  • Telehealth, 11–12
    • during COVID-19 pandemic, 14
  • Temperature sensor, 127, 131, 135
  • TensorFlow module, 322, 326, 327
  • Threats, 45
  • Threshold value, 60, 61, 64
  • Thyroid disease, study using machine learning algorithm,
    • category of thyroid cancer, 36–37
    • introduction, 34
    • machine learning approach toward the detection of thyroid cancer, 37–40
    • related works, 34–35
    • thyroid functioning, 35–36
  • Thyroxine total (T4), 35–36
  • Tissues,
  • Transport layer security (TLS), 187
  • Treatment-resistant unipolar depression, 34
  • Triangular membership function, 158
  • Triiodothyronine total (T3), 35
  • TSH, 36
  • U special kids (USK), 10
  • Uniform distribution, 147
  • Universal asynchronous receivertransmitter (UART), 123, 131
  • Universal synchronous and asynchronous receivertransmitter (USART), 131
  • Unsupervised, 296, 297
  • Unsupervised learning, 145, 261
    • K-means clustering, 145
  • Usability of patient information, 27
  • Uses of machine learning in pharma and medicine, 276
  • Vectored interrupt controller (VIC), 122
  • VGG-16 CNN model, 323, 325–326
  • Virtual clinic systems, 10–11
  • Vital sign parameter (VSP) measurement, 10
  • VitalPatch, 13
  • Vulnerability, 45
  • Wake-up timer, 124
  • Wearable asthma monitor, 108
  • Wearable cardiac defibrillator (WCD), 6
  • Wearable waterproof sensors, 7
  • Wi-Fi module: EPS Wi-Fi 8266 module, 131
  • Wireless body area network (WBAN), 195
  • Wireless sensors, 133
  • Wrist-worn wearable devices, 6
  • YOLO (deep learning model), 321
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