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市場調查報告書

穿戴式感測器:2021年∼2031年

Wearable Sensors 2021-2031

出版商 IDTechEx Ltd. 商品編碼 963404
出版日期 內容資訊 英文 368 Slides
商品交期: 最快1-2個工作天內
價格
穿戴式感測器:2021年∼2031年 Wearable Sensors 2021-2031
出版日期: 2020年09月30日內容資訊: 英文 368 Slides
簡介

穿戴式技術的市場規模,2019年達到約700億美元,成為了2014年的2倍。穿戴式感測器,是穿戴式技術產業整體的基本的實現零組件,對價值鏈整體企業來說,明確理解其功能和可能性是不可或缺的。

本報告提供穿戴式感測器市場相關調查分析,所使用的感測器的技術,市場趨勢,競爭情形,加上主要企業等相關資料之系統性資訊。

目錄

第1章 摘要整理

第2章 簡介

第3章 感測器的種類

  • 慣性測量設備 (IMU)
  • 光學感測器
  • 3D成像、深度感測器
  • 穿戴式相機
  • 光學感測器:其他範例
  • 電極
  • 力量/壓力/拉伸感測器
  • 溫度感測器
  • 麥克風
  • 化學感測器
  • 氣體感測器
  • GPS
  • 其他範例和案例研究

第4章 市場預測

  • 預測:簡介和定義
  • 感測器的種類的定義與分類
  • 穿戴式感測器:銷售數量(成果資料)
  • 穿戴式感測器:銷售數量(市場預測)
  • 穿戴式感測器:銷售數量(成果資料與預測)
  • 穿戴式感測器:總收益(成果資料)
  • 穿戴式感測器:總收益(預測)
  • 穿戴式感測器:總收益(成果資料與預測)
  • 穿戴式感測器:每單位的價格(成果資料與預測)
  • 穿戴式感測器的趨勢:添加資料
目錄

Title:
Wearable Sensors 2021-2031
A comprehensive study of the global industry landscape, including the technology, players, and market forecasts.

The market for wearable sensors will reach over $5bn per year by 2025.

This report is a comprehensive study of the wearable sensors market, describing the technology, market trends and competitive landscape for sensors used in wearable electronic products. The report has been compiled over five years of research by the analyst team at IDTechEx, leveraging parallel expertise in many relevant technology and market sectors. The report covers 17 different types of sensor, across 10 major categories, characterising the technology, applications, and industry landscape for this. The report describes the activity of over 100 companies, including primary content (e.g. interviews, photographs, visits, etc.) with more than 50 key players from throughout the value chain. Finally, the report provides detailed quantitative market forecasts for each type of wearable sensor, leveraging unique primary data from interviews, collated financial statistics, and industry trends alongside IDTechEx's parallel forecasting for more than 50 different wearable technology product types.

Within many wearable electronic products, it is the sensors which provide the key value proposition. For example, smartwatches and fitness tracking are built around the provision of fitness tracking and activity data, gradually moving towards more medically relevant metrics. Virtual, augmented and mixed reality devices (VR, AR & MR) rely on a suite of sensors including combinations of cameras, inertial measurement units, depth sensing, force/pressure sensors and more to enable the user to interact with the content and the environment. Other product categories such as electronic skin patches, hearables, smart clothing and other related product types are all similar, each relying on a suite of core sensors which can interface with the body and surroundings as a key part of the product functionality.

IDTechEx's research in wearables tracks the progress of over 50 wearable electronic product types. Within each of these products, a key focus of the research is in understanding and characterising the core hardware behind the products, with sensors as a key part (alongside energy storage, communications, and other essential features). This report looks at the key sensor components in each of these wearable product categories, focusing on 17 different sensor types. The combination of the detailed wearable product forecasting and understanding of the sensor landscape and suppliers enables very detailed forecasting for wearable sensors, in terms of revenue, pricing, and volume, with historic data from 2010-2019 and forecasts from 2020-2031.

IDTechEx describes the wearable sensors market in three waves. This idea, coined back in 2016, has stood the test of time and remains true to this day. The first wave includes sensors that have been incorporated in wearables for many years, often being originally developed for wearable products over previous decades. A second wave of wearable sensors came following huge technology investment in smartphones. Many of the sensors from smartphones could be easily adapted for use in wearable products; they could be "made-wearable". Finally, with the growing maturity of the wearable technology market over the past decades, many sensors are now designed from the ground up with wearable products in mind. Many of these "made-for-wearable" sensors are already well established in the market today, with more generations of new sensors being commercialised to fuel the next generations of wearable products.

The wearable technology market was worth nearly $70bn in 2019, having doubled in size since 2014. Sensors have provided the core features for many of these different products throughout this rise, and they will continue to be critical into future generations of products. The COVID-19 pandemic in 2020 has brought additional focus to sensors, including tracking early onset of conditions, facilitation of wearables for contact tracing, and remote patient monitoring for patients in isolation. Parallel trends see smartwatches driving towards medical metrics, hearables adding more sophisticated sensor options, skin patches successfully commercialising in new applications and many industrial, military and security applications maturing. As such, wearable sensors remain a fundamental enabling component for the entire wearable technology industry, and obtaining a clear understanding of their capabilities and potential is essential for any player within the entire value chain.

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TABLE OF CONTENTS

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