1. | EXECUTIVE SUMMARY |
1.1. | Introduction to wearable sensors |
1.2. | Sensors enable key product value propositions |
1.3. | 10 major wearable sensor categories (by function) |
1.4. | 17 types of wearable sensor used today |
1.5. | Wearable sensors in three waves |
1.6. | The first wave: "The originals" |
1.7. | The second wave: "Made-wearable" sensors |
1.8. | The third wave: "Made-for-wearable" sensors |
1.9. | Historic data (2010-2020): Wearable sensors (revenue) |
1.10. | Market forecast (2021-2031): Wearable sensors (revenue) |
2. | INTRODUCTION |
2.1. | Origins and early potential in wearables |
2.2. | Shifting hype in wearables as markets evolve |
2.3. | Key metrics for wearables: Search terms |
2.4. | Key metrics for wearables: Funding trends |
2.5. | Key metrics for wearables: Patent trends |
2.6. | Historic market data by sector |
2.7. | Wearables in 2020 |
2.8. | Sensors enable key product value propositions |
2.9. | Definitions |
2.10. | Common wearable sensors deployed today |
2.11. | Sensors on the body: what do we want to measure? |
2.12. | Appropriate data for the desired outcome |
2.13. | Appropriate data: Example |
2.14. | Example: effort and reward in heart monitoring |
2.15. | Example: Useful data at different levels of inference |
2.16. | Sensor fusion is essential and expected |
2.17. | Different product types from the same sensors |
2.18. | Wider industry context for each sensor type |
2.19. | Wearable sensors in three waves |
3. | SENSOR TYPES |
3.1. | Inertial measurement units |
3.1.1. | IMUs - Introduction |
3.1.2. | MEMS - Background |
3.1.3. | MEMS - Manufacturing techniques |
3.1.4. | MEMS - Becoming a commodity |
3.1.5. | MEMS Accelerometers |
3.1.6. | MEMS Gyroscopes |
3.1.7. | Digital compasses |
3.1.8. | Magnetometer types |
3.1.9. | Magnetometer types (figure) |
3.1.10. | Magnetometer suppliers and industry dynamic |
3.1.11. | Magnetometer suppliers by type |
3.1.12. | MEMS Barometers |
3.1.13. | Pressure sensors in wearable devices |
3.1.14. | Example: Interview with Bosch Sensortec |
3.1.15. | Limitations and common errors with MEMS sensors |
3.1.16. | MEMS manufacturers: characteristics and examples |
3.1.17. | Case study: ST Microelectronics |
3.1.18. | Case study: InvenSense |
3.1.19. | Apple: iPhone sensor choice case study |
3.1.20. | Conclusion: IMUs are here to stay, with some limitations |
3.2. | Optical sensors |
3.2.1. | Optical sensors - introduction |
3.2.2. | Optical sensors - Heart rate |
3.2.3. | Photoplethysmography (PPG) - Basic background |
3.2.4. | Transmission and reflectance |
3.2.5. | Reflectance-mode PPG for fitness wearables |
3.2.6. | Key players |
3.2.7. | Valencell |
3.2.8. | Valencell - more product examples |
3.2.9. | Well Being Digital Ltd. (WBD101) |
3.2.10. | CSEM |
3.2.11. | Philips |
3.2.12. | cosinuss° |
3.2.13. | APM |
3.2.14. | Georgia Tech |
3.2.15. | Optical sensors - Pulse oximetry and other cardiac metrics |
3.2.16. | Wearable pulse oximetry via a smartwatch |
3.2.17. | Smartwatch pulse oximetry: Examples |
3.2.18. | Examples: Garmin |
3.2.19. | Medical device examples: Oxitone |
3.2.20. | How pulse oximetry data is used |
3.2.21. | Other related approaches |
3.2.22. | Reveal Biosensors |
3.3. | 3D imaging and depth sensors |
3.3.1. | 3D imaging and motion capture |
3.3.2. | Application example: Motion capture in animation |
3.3.3. | Stereoscopic vision |
3.3.4. | Time of flight |
3.3.5. | Structured light |
3.3.6. | Comparison of 3D imaging technologies |
3.3.7. | Example: Leap Motion (now Ultraleap) |
3.3.8. | Example: Microsoft; from Kinect to Hololens |
3.3.9. | Example: Intel's RealSense™ |
3.3.10. | Example: Occipital |
3.3.11. | Commercial 3D camera examples |
3.4. | Wearable Cameras |
3.4.1. | Cameras in wearable devices |
3.4.2. | Established players exploiting profitable niches |
3.4.3. | Applications in safety and security |
3.4.4. | Other applications: Enhancing sports media |
3.4.5. | Cameras in smartwatches? |
3.4.6. | Social applications: drivers and challenges |
3.4.7. | Example: Spectacles by Snap Inc. |
3.4.8. | Other applications: Automatic digital diary |
3.5. | Optical sensors - other examples |
3.5.1. | Optical chemical sensors |
3.5.2. | Example - Delektre |
3.5.3. | Implantable optical glucose sensors |
3.5.4. | Optical method for non-invasive glucose sensing |
3.5.5. | Start-up example: eLutions |
3.5.6. | Related platform: UV exposure indicators |
3.5.7. | Speech recognition using lasers - VocalZoom |
3.5.8. | Infrared spectroscopy |
3.5.9. | Example: Temperature from NIR spectroscopy |
3.5.10. | Example: Alcohol detection by NIR spectroscopy |
3.5.11. | Example: Lactate detection by NIR spectroscopy |
3.5.12. | Example: Body hydration |
3.6. | Electrodes |
3.6.1. | Introduction |
3.6.2. | Applications and product types |
3.6.3. | Biopotential - ECG, EEG, EMG |
3.6.4. | Introduction - Measuring biopotential |
3.6.5. | Introduction - The circuitry for measuring biopotential |
3.6.6. | Introduction - Electrocardiography (ECG, or EKG) |
3.6.7. | Examples - devices for cardiac monitoring |
3.6.8. | Introduction - Electroencephalography (EEG) |
3.6.9. | Examples - Consumer EEG products and prototypes |
3.6.10. | Introduction - Electromyography (EMG) |
3.6.11. | Examples - Consumer EMG products and prototypes |
3.6.12. | Bioimpedance / skin conductance |
3.6.13. | Introduction - Bioimpedance |
3.6.14. | Technology overview - Galvanic skin response (GSR) |
3.6.15. | Device examples |
3.6.16. | Skin conductance: Terminology and approaches |
3.6.17. | Skin conductance change under stress |
3.6.18. | GSR algorithms: Managing noise and other errors |
3.6.19. | GSR algorithms: Data interpretation challenges |
3.6.20. | GSR algorithms: signal processing |
3.6.21. | GSR algorithms: Conclusions and outlook |
3.6.22. | Commercial devices for hydration monitoring |
3.6.23. | Example: InBody |
3.6.24. | Electrode materials and properties |
3.6.25. | Technology overview - electrode properties |
3.6.26. | Wet vs dry electrodes |
3.6.27. | Wet electrodes |
3.6.28. | Disposable Ag/AgCl electrodes |
3.6.29. | Electrodes: Traditional approaches |
3.6.30. | Skin patches with disposable electrodes |
3.6.31. | Skin patches with integrated electrodes |
3.6.32. | Dry electrodes |
3.6.33. | Introduction - Dry electrodes |
3.6.34. | Example - Textile electrodes |
3.6.35. | Examples of e-textiles electrodes |
3.6.36. | E-textile material use over time |
3.6.37. | E-textile material use in 2020 |
3.6.38. | E-textile products with conductive inks |
3.6.39. | Emerging options |
3.6.40. | Emerging options - Microneedle electrodes |
3.6.41. | Example: Tyndall National Institute |
3.6.42. | Example: Sun Yat-Sen University |
3.6.43. | Company examples - approaches to wearable electrodes |
3.6.44. | DuPont |
3.6.45. | Henkel - new electrode materials |
3.6.46. | Nissha GSI Technologies |
3.6.47. | Quad Industries |
3.6.48. | Screentec OY |
3.6.49. | Holst Centre: Comments on electrode performance |
3.6.50. | Toyobo |
3.6.51. | Nanoleq |
3.7. | Force / pressure / stretch sensors |
3.7.1. | Different modes for sensing motion |
3.7.2. | What is piezoresistance? |
3.7.3. | Early examples of wearable textile FSRs: socks |
3.7.4. | Percolation dependent resistance |
3.7.5. | Quantum tunnelling composite |
3.7.6. | QTC® vs. FSR™ vs. piezoresistor? |
3.7.7. | Printed piezoresistive sensors: Anatomy |
3.7.8. | Pressure sensing architectures |
3.7.9. | Thru mode sensors |
3.7.10. | Shunt mode sensors |
3.7.11. | Force vs resistance characteristics |
3.7.12. | Textile-based pressure sensing |
3.7.13. | Knitting as a route to textile sensors |
3.7.14. | Example: Knitted conductors by Gunze, Japan |
3.7.15. | Strain sensor examples: BeBop Sensors |
3.7.16. | Large-area pressure sensors |
3.7.17. | Force sensor examples: Sensing Tex |
3.7.18. | Textile-based applications of printed FSR |
3.7.19. | Force sensor examples: Vista Medical |
3.7.20. | Pressure sensitive fabric (Vista Medical) |
3.7.21. | SOFTswitch: Force sensor on fabric |
3.7.22. | Examples: Sensoria |
3.7.23. | Technological development of piezoresistive sensors. |
3.7.24. | Curved sensors with consistent zero (Tacterion) |
3.7.25. | Piezoelectricity: An introduction |
3.7.26. | Piezoelectric polymers |
3.7.27. | Printed piezoelectric sensor |
3.7.28. | Printed piezoelectric sensors: prototypes |
3.7.29. | High-strain sensors (capacitive) |
3.7.30. | How they work |
3.7.31. | Printed capacitive stretch sensors |
3.7.32. | Use of dielectric electroactive polymers (EAPs) |
3.7.33. | Key players in DE EAP commercialisation today |
3.7.34. | Players with EAPs: Parker Hannifin |
3.7.35. | Players with EAPs: StretchSense |
3.7.36. | Other examples: Polymatech |
3.7.37. | C Stretch Bando: Progress on stretchable sensors |
3.7.38. | Players with EAPs: Bando Chemical |
3.7.39. | C Stretch Bando: Progress on stretchable sensors |
3.7.40. | Other strain sensors (capacitive & resistive) |
3.7.41. | Strain sensor examples: Polymatech |
3.7.42. | Strain sensor example: Yamaha and Kureha |
3.7.43. | Hybrid FSR/capacitive sensors |
3.7.44. | Research with emerging advanced materials |
3.7.45. | Other novel types of pressure sensor |
3.8. | Temperature sensors |
3.8.1. | Two main roles for temperature sensors in wearables |
3.8.2. | Types of temperature sensor |
3.8.3. | Approaches and standards for medical sensors |
3.8.4. | Examples: Blue Spark |
3.8.5. | Core body temperature |
3.8.6. | Ear-based core body temperature measurements |
3.8.7. | Measuring core body temperature: new approaches |
3.9. | Microphones |
3.9.1. | Using sound to investigate the body |
3.9.2. | Types of microphones |
3.9.3. | Example: MEMS microphones |
3.9.4. | The need for waterproof, breathable encapsulation |
3.9.5. | Example: Electret microphones |
3.9.6. | Bioacoustics |
3.9.7. | Bioacoustics using IMUs |
3.9.8. | Microphones and AI for respiratory diagnostics |
3.9.9. | Microphones in social and clinical trials |
3.9.10. | Examples: Microphones for sleep apnea |
3.10. | Chemical sensors |
3.10.1. | Introduction: Chemical sensing |
3.10.2. | Selectivity and signal transduction |
3.10.3. | Analyte selection and availability |
3.10.4. | Optical chemical sensors |
3.10.5. | Example: Analytes in the sweat |
3.10.6. | Glucose monitoring & diabetes management |
3.10.7. | Introduction - Diabetes management |
3.10.8. | Diabetes management device roadmap: Summary |
3.10.9. | Glucose test strips |
3.10.10. | The case for continuous glucose monitoring (CGM) |
3.10.11. | CGM is deployed via skin patches |
3.10.12. | Market share in 2019 (revenue) |
3.10.13. | Market share in 2019 (volume) |
3.10.14. | CGM device structure and chemistry |
3.10.15. | Anatomy of a typical CGM device |
3.10.16. | CGM sensor chemistry |
3.10.17. | Comparison metrics for CGM devices |
3.10.18. | Example: Accuracy of CGM devices over time |
3.10.19. | Sensor filament structure |
3.10.20. | Abbott: "Wired enzyme" |
3.10.21. | Abbott - Device and sensor structure |
3.10.22. | Abbott - Sensor filament and structure |
3.10.23. | Abbott - Flux-limiting membranes on the sensor |
3.10.24. | Dexcom - G4 and G5 sensor design |
3.10.25. | Dexcom - Changes in G6 |
3.10.26. | Medtronic - also coaxial |
3.10.27. | Other examples - Medtrum |
3.10.28. | Others - mixture of approaches |
3.10.29. | Non-invasive CGM |
3.10.30. | Example: Indigo |
3.10.31. | Other applications for wearable chemical sensors |
3.10.32. | Diagnostics with chemical sensors |
3.10.33. | Cholesterol |
3.10.34. | Monitoring blood cholesterol using biosensors |
3.10.35. | Towards wearable cholesterol monitoring |
3.10.36. | Alcohol detection |
3.10.37. | Example: sweat alcohol detection |
3.10.38. | Lactic acid detection |
3.10.39. | Lactic acid monitoring for athletes |
3.10.40. | Traditional lactic acid monitors |
3.10.41. | Microneedles to analyse lactic acid in interstitial fluid |
3.10.42. | Other analytes |
3.10.43. | Increasingly portable diagnosis of bovine and human TB |
3.10.44. | Wearable diagnostic tests for cystic fibrosis |
3.10.45. | Example players |
3.10.46. | Biolinq |
3.10.47. | Kenzen |
3.10.48. | Milo Sensors |
3.10.49. | Eccrine Systems |
3.10.50. | PARC / UCSD |
3.10.51. | Stanford and UC Berkeley |
3.10.52. | Xsensio |
3.10.53. | Epicore Biosystems |
3.11. | Gas sensors |
3.11.1. | Introduction: Wearable gas sensors |
3.11.2. | Gas sensor industry |
3.11.3. | Concentrations of detectable atmospheric pollutants |
3.11.4. | Transition to miniaturised gas sensors |
3.11.5. | Comparison between classic and miniaturised sensors |
3.11.6. | Comparison of miniaturised sensor technologies |
3.11.7. | Technology requirements for wearable gas sensors |
3.11.8. | Metal oxide semiconductors (MOS) gas sensors |
3.11.9. | Miniaturisation of MOS Gas Sensors |
3.11.10. | Suppliers for MOS sensors |
3.11.11. | Electrochemical (EC) gas sensors |
3.11.12. | Flat electrochemical sensors |
3.11.13. | Miniaturisation of electrochemical gas sensors |
3.11.14. | Suppliers for Electrochemical sensors |
3.11.15. | Electronic nose (e-Nose) |
3.11.16. | Algorithms and software to solve the multiple gas detection |
3.11.17. | Some of the commercial eNose |
3.11.18. | HiCling |
3.11.19. | Technology for Social Impact / Grameen Intel |
3.11.20. | H2S Professional Gas Detector watch |
3.11.21. | Future opportunities with wearable gas sensors |
3.12. | GPS |
3.12.1. | Prominent wearable GPS devices |
3.12.2. | Challenges with GPS power consumption |
3.13. | Other examples and case studies |
3.13.1. | Gastric electrolyte |
3.13.2. | Example: Proteus Digital Health |
4. | MARKET FORECASTS |
4.1. | Forecasting: Introduction and definitions |
4.2. | Definitions and categorisation for sensor types |
4.3. | Wearable sensors: Sales volumes (historic data, 2010-2019) |
4.4. | Wearable sensors: Sales volumes (market forecast, 2020-2031) |
4.5. | Wearable sensors: Sales volumes (historic data and forecast) |
4.6. | Wearable sensors: Total revenue (historic data, 2010-2019) |
4.7. | Wearable sensors: Total revenue (forecast, 2020-2031) |
4.8. | Wearable sensors: Total revenue (historic data and forecast) |
4.9. | Wearable sensors: Price per unit (historic data and forecast) |
4.10. | Wearable sensors: Price per unit (historic data and forecast) |
4.11. | Waves of wearable sensors: Supporting data |