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| 1. | EXECUTIVE SUMMARY |
| 1.1. | Report Scope |
| 1.2. | Megatrends Driving Skin Sensor Technologies |
| 1.3. | Skin Sensor Devices Take Many Forms |
| 1.4. | Skin Parameters Analyzed |
| 1.5. | Technology Advancements and Trends |
| 1.6. | Deep learning (DL) makes automated image recognition possible |
| 1.7. | The Rise of Microbiome Research |
| 1.8. | Key Market Segments for Skin Sensor Technologies |
| 1.9. | Skin Sensors in Research & Development |
| 1.10. | Skin Sensors as a Marketing Tool |
| 1.11. | Skin Sensors for Consumers at Home |
| 1.12. | Marketing: Business Models |
| 1.13. | Beauty Tech at CES 2020 |
| 1.14. | Market Forecast 2020 - 2030 |
| 1.15. | Market Forecast: Marketing |
| 2. | INTRODUCTION |
| 2.1. | Report Scope |
| 2.2. | Skin Structure |
| 2.3. | Epidermis |
| 2.4. | Dermis |
| 2.5. | Skincare as Protection Against the Environment |
| 2.6. | Skincare in the Race Against Time |
| 2.7. | Upselling Through Personalization |
| 2.8. | Moving Sales Online |
| 2.9. | Megatrends Driving Skin Sensor Technologies |
| 3. | SKIN PARAMETERS AND MEASUREMENT TECHNIQUES |
| 3.1.1. | Chapter Overview |
| 3.1.2. | Skin Sensor Devices Take Many Forms |
| 3.1.3. | Image Requirements |
| 3.1.4. | Hydration/Moisture |
| 3.1.5. | Hydration: Capacitance |
| 3.1.6. | Hydration: Capacitance Examples |
| 3.1.7. | Hydration: Bioelectrical Impedance Analysis |
| 3.1.8. | Hydration: Players and Methods |
| 3.1.9. | Hydration: Discussion |
| 3.1.10. | Hydration: Examples of Optical Measurements |
| 3.1.11. | Wrinkles and Pores |
| 3.1.12. | Wrinkles and Pores: Parallel Polarized Light |
| 3.1.13. | Wrinkles and Pores: 3D Photography |
| 3.1.14. | Wrinkles and Pores: Image Analysis |
| 3.1.15. | Wrinkles and Pores: Players and Methods |
| 3.1.16. | Wrinkles and Pores: Discussion |
| 3.1.17. | Sebum |
| 3.1.18. | Sebum: Sebutape |
| 3.1.19. | Sebum: Examples of Film-Based Devices |
| 3.1.20. | Sebum: UV |
| 3.1.21. | Sebum: Other Optical Means |
| 3.1.22. | Sebum: Players and Methods |
| 3.1.23. | Color |
| 3.1.24. | Color: Multispectral Imaging |
| 3.1.25. | Color: White Light |
| 3.1.26. | Color: Canfield Scientific RBX Technology |
| 3.1.27. | Color: UV |
| 3.1.28. | Color: Example of Hyperspectral Imaging |
| 3.1.29. | Melanin: Players and Methods |
| 3.1.30. | Hemoglobin: Players and Methods |
| 3.1.31. | Color: Skin Tone |
| 3.1.32. | Elasticity/Firmness |
| 3.1.33. | Elasticity: Contact-Based Methods |
| 3.1.34. | Elasticity: Image-Based Method |
| 3.1.35. | Elasticity: Players and Methods |
| 3.1.36. | Other Common Parameters |
| 3.2. | Emerging Parameters in Skin Sensors |
| 3.2.1. | Genetic Testing |
| 3.2.2. | Genetic Testing: Categories |
| 3.2.3. | Genetic Testing: Players |
| 3.2.4. | Microbiome |
| 3.2.5. | The Rise of Microbiome Research |
| 3.2.6. | Skin Sensor Technologies: Summary and Outlook |
| 3.3. | Artificial Intelligence |
| 3.3.1. | What is artificial intelligence (AI)? Terminologies explained |
| 3.3.2. | Algorithm training process in a single layer |
| 3.3.3. | Towards deep learning by deepening the neutral network |
| 3.3.4. | Deep learning (DL) makes automated image recognition possible |
| 3.3.5. | Image recognition AI is based on convolutional neural networks (CNNs) |
| 3.3.6. | AI - Disrupting Skin Cancer Detection |
| 3.3.7. | Skin cancer |
| 3.3.8. | Skin cancer: Key players |
| 3.3.9. | Skin cancer: Machine learning algorithms |
| 3.3.10. | Skin cancer: The ABCDE criteria |
| 3.3.11. | Skin cancer: Dermoscopic melanoma recognition (2018) |
| 3.3.12. | Skin cancer: Dermoscopic melanoma recognition and its challenges |
| 3.3.13. | Skin cancer: SkinVision |
| 3.3.14. | Skin cancer: MetaOptima |
| 3.3.15. | Skin cancer detection AI: Performance comparison |
| 3.4. | Skin Sensors for Advanced Wound Care |
| 3.4.1. | Trends in Wound Monitoring |
| 3.4.2. | Bacterial Detection: Point-of-Care Visualization |
| 3.4.3. | Bacterial Detection: Rapid Diagnostics |
| 3.4.4. | Hyperspectral Technology: HyperMed |
| 3.4.5. | Near-Infrared: Kent Imaging |
| 3.4.6. | Thermography: WoundVision |
| 3.4.7. | Bioimpedance: Bruin Biometrics |
| 3.4.8. | 3D Imaging: Infrared from Tablets |
| 3.4.9. | 3D Imaging: Imaging in Stereo |
| 3.4.10. | 3D Imaging: Point Tracking |
| 3.4.11. | 3D Imaging: Laser Lines |
| 3.4.12. | Smartphone Apps |
| 3.4.13. | Smartphone Apps: Swift Medical |
| 3.4.14. | Trends in Wound Monitoring |
| 3.4.15. | Relationship with Cosmetics Industry |
| 4. | MARKET ANALYSIS AND COMPANY PROFILES |
| 4.1. | Key Market Segments |
| 4.2. | Players and Market Segments |
| 4.3. | Skin Sensors in Research & Development |
| 4.4. | Research & Development: Key Players |
| 4.5. | Courage + Khazaka Electronic |
| 4.6. | Cortex Technology |
| 4.7. | Miravex |
| 4.8. | Miravex: Antera 3D |
| 4.9. | Research & Development: Business Models |
| 4.10. | Skin Sensors as a Marketing Tool |
| 4.11. | Marketing - High end players |
| 4.12. | Facial Imaging |
| 4.13. | PIE |
| 4.14. | Skin Sensors for Consumers at Home |
| 4.15. | Improvements in Cameras and Phones Critical for Success |
| 4.16. | Skin Sensors Drives Ecommerce |
| 4.17. | FitSkin |
| 4.18. | Skin Sensors Benefit All |
| 4.19. | Cutitronics |
| 4.20. | Haut.AI |
| 4.21. | Sequential Skin |
| 4.22. | Marketing: Business Models |
| 4.23. | Skin Sensor Technologies and the Subscription Model |
| 4.24. | How Have Brands Responded? |
| 4.25. | CES 2020 |
| 4.26. | Beauty Tech at CES 2020 |
| 4.27. | HiMirror |
| 4.28. | HiMirror: HiMirror Slide |
| 4.29. | lululab |
| 4.30. | lululab: Lumini Home |
| 4.31. | Revieve |
| 4.32. | Revieve: Partnership with CareOS |
| 4.33. | Perfect Corp |
| 4.34. | Procter and Gamble: Opté |
| 4.35. | L'Oréal: Perso |
| 5. | MARKET FORECASTS |
| 5.1. | Methodology |
| 5.2. | Market Forecast 2020 - 2030 |
| 5.3. | Market Forecast: Research & Development |
| 5.4. | Market Forecast: Marketing |
| 5.5. | Market Forecast: Consumers |
| Slides | 150 |
|---|---|
| Forecasts to | 2030 |