- 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,
- discriminative, 296, 298, 300, 303
- generative, 296, 297, 300, 303
- hypothesis, 297, 298
- 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,
- automatic, 69, 70, 76
- brain tumor, 75, 76, 295, 296, 302
- image, 59, 63, 65, 296
- lung, 60, 63–64
- multilevel, 300
- organ, 60, 63
- region-based, 300
- semi-automatic, 69, 70
- 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,
- cancerous, 69, 70
- healthy, 69, 70, 71, 76
- malignant, 71, 73, 75, 76
- 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
- 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
..................Content has been hidden....................
You can't read the all page of ebook, please click
here login for view all page.