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1401312

全球情感運算市場 - 2023-2030

Global Affective Computing Market - 2023-2030

出版日期: 2023年12月29日 | 出版商:

DataM Intelligence | 英文 211 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

全球情緒運算市場在2022年達到509億美元，預計2030年將達到5,929億美元，2023-2030年預測期間CAGR為36.2%。

深度學習中人工智慧和機器學習技術的進步顯著增強了情緒運算系統的能力。先進的演算法現在可以更準確地分析和解釋複雜的情緒線索。對自然、直覺的人機互動的需求不斷成長，正在推動情感運算的採用。企業和產業正在利用機器中的情感智慧來增強使用者體驗和參與度。

穿戴式裝置的激增和物聯網的擴展為整合情感運算提供了機會。配備情緒識別感測器的穿戴式設備和具有情緒感知功能的物聯網設備有助於市場成長。虛擬助理和聊天機器人在從客戶服務到虛擬伴侶的各種應用中的廣泛使用，正在推動對有效計算的需求。情緒智商虛擬助理可增強使用者互動和滿意度。

由於情感運算在個人化學習教育中的使用越來越多，北美成為全球情感運算市場的主導地區。該地區對創新和研究驅動型發展的重視有助於情感識別、情感分析和情感運算應用相關技術的進步。北美的醫療保健、零售和娛樂等行業很早就表現出了對情感運算應用程式的興趣和採用。

動力學

對虛擬助理的需求不斷成長

情感運算允許虛擬助理根據使用者的情緒表達來個性化他們的反應。個人化程度有助於打造更量身訂做、更具吸引力的使用者體驗。情感運算技術使虛擬助理能夠成為具有情感智慧的會話代理。它識別並回應使用者的情緒，創造更自然、更富同理心的互動。由情感運算提供支援的虛擬助理可以根據使用者的情緒狀態調整其介面和回應。這種適應性有助於提供動態且以使用者為中心的體驗。

在客戶服務應用中，配備情緒運算功能的虛擬助理可以更好地理解和處理客戶的情緒和情緒。這對於解決問題和提供支援特別有價值。情感運算有助於識別使用者聲音中的情感。無論是智慧型手機、智慧型揚聲器或其他裝置中的虛擬助理都使用此功能根據偵測到的情緒基調自訂回應和互動。

技術進步

機器學習和人工智慧的不斷進步有助於開發更複雜的情感識別演算法。改進的演算法提高了情緒計算系統的準確性和效率。臉部辨識攝影機、語音辨識麥克風和生理感測器等感測器技術的進步有助於更好的資料擷取和分析。增強的感測技術可以更精確地測量情緒線索。

深度學習和神經網路的發展帶來了模式識別的突破，使情緒運算系統能夠識別臉部表情、語音和其他情緒訊號中的複雜模式。技術進步使得情緒辨識的多種模式得以整合，例如將臉部表情與語音分析和生理訊號結合。多模態方法提高了情緒分析的全面性。

精度和可靠性低

情感運算系統嚴重依賴準確識別和解釋人類情感的演算法。情緒辨識準確度低會導致對使用者情緒狀態的誤解，影響技術的可靠性。對情緒線索的解釋是主觀的並且依賴上下文。情感運算演算法很難一致地解釋不同個體和情況下的不同情感表達，從而導致結果不一致。

人類的情緒很複雜，表現形式多種多樣，因此開發準確涵蓋所有情緒狀態的演算法具有挑戰性。表達式中的細微差別和變化增加了複雜性。不同文化的情感表達方式不同，情感運算系統並不總是能解釋這些文化差異。這會導致對情緒線索的誤解，尤其是在多元化和全球性的使用者群體中。

Technological advancements in AI and ML technologies in deep learning significantly enhanced the capabilities of affective computing systems. Advanced algorithms now analyze and interpret complex emotional cues with greater accuracy. The rising demand for natural and intuitive human-machine interaction is driving the adoption of affective computing. Businesses and industries are leveraging emotional intelligence in machines to enhance user experiences and engagement.

The proliferation of wearable devices and the expansion of the Internet of Things provide opportunities for integrating affective computing. Wearables equipped with sensors for emotion recognition and IoT devices with emotion-aware features contribute to market growth. The widespread use of virtual assistants and chatbots in various applications, from customer service to virtual companions, is fueling the demand for effective computing. Emotionally intelligent virtual assistants enhance user interactions and satisfaction.

North America is a dominating region in the global affective computing market due to the growing use of affective computing in education for personalized learning. The region's emphasis on innovation and research-driven development contributes to the advancement of technologies related to emotion recognition, sentiment analysis and affective computing applications. Industries such as healthcare, retail and entertainment in North America have shown early interest and adoption of affective computing applications.

Dynamics

Growing Demand for Virtual Assistants

Affective computing allows virtual assistants to personalize their responses based on users' emotional expressions. The level of personalization contributes to a more tailored and engaging user experience. Affective computing technologies enable virtual assistants to become emotionally intelligent conversational agents. It recognize and respond to users' emotions, creating a more natural and empathetic interaction. Virtual assistants, powered by affective computing, adapt their interfaces and responses based on users' emotional states. The adaptability contributes to a dynamic and user-centric experience.

In customer service applications, virtual assistants equipped with affective computing capabilities better understand and address customers' emotions and sentiments. The is particularly valuable for resolving issues and providing support. Affective computing facilitates the recognition of emotions in users' voices. Virtual assistants, whether in smartphones, smart speakers or other devices use this capability to tailor responses and interactions based on the detected emotional tone.

Technological Advancement

Ongoing advancements in machine learning and artificial intelligence contribute to the development of more sophisticated algorithms for emotion recognition. Improved algorithms enhance the accuracy and efficiency of affective computing systems. Progress in sensor technologies, including facial recognition cameras, voice recognition microphones and physiological sensors, contributes to better data capture and analysis. Enhanced sensing technologies enable more precise measurement of emotional cues.

The evolution of deep learning and neural networks has led to breakthroughs in pattern recognition, enabling affective computing systems to discern intricate patterns in facial expressions, voice Tons and other emotional signals. Technological advancements enable the integration of multiple modalities for emotion recognition, such as combining facial expressions with voice analysis and physiological signals. The multi-modal approach improves the comprehensiveness of emotional analysis.

Low Accuracy and Reliability

Affective computing systems heavily rely on algorithms designed to recognize and interpret human emotions accurately. Low accuracy in emotion recognition lead to misinterpretation of users' emotional states, affecting the reliability of the technology. The interpretation of emotional cues is subjective and context-dependent. Affective computing algorithms struggle to consistently interpret diverse emotional expressions across different individuals and situations, leading to inconsistencies in results.

Human emotions are complex and manifest in a wide range of expressions, making it challenging to develop algorithms that cover the full spectrum of emotional states accurately. Subtle nuances and variations in expressions add to the complexity. Emotions are expressed differently across cultures and affective computing systems do not always account for these cultural variations. The results in misinterpretations of emotional cues, especially in diverse and global user populations.

Segment Analysis

The global affective computing market is segmented based on technology, component, enterprise size, end-user and region.

Growing Adoption of Touch-based Technology in Affective Computing Market

Based on the technology, the affective computing market is segmented into touch-based and touchless. Touch-based technology is a more natural form of human-computer interaction compared to touchless technology. Touch-based sensors and devices capture subtle nuances in touch interactions, providing a means to recognize and interpret emotional cues. The pressure, duration and patterns of touch convey emotional information, contributing to affective computing applications.

The widespread adoption of smartphones, tablets and wearables has driven the integration of touch-based interfaces. The devices often incorporate touch sensors to facilitate user interactions. The use of affective computing in these devices enhances user experiences, especially in applications related to health and wellness.

Haptic feedback, a component of touch-based technology, allows devices to provide tactile sensations in response to user interactions. The feature enhances emotional engagement by creating a sense of touch, adding an extra dimension to the user experience. Growing product launches in the automotive industry with touch-based affective computing help to boost segment growth over the forecast period.

For instance, on August 15, 2022, Mahindra & Mahindra, India's leading SUV manufacturer launched its new state-of-the-art INGLO EV platform and five e-SUVs under two EV brands showcasing its vision for the future of electric mobility. The brake-by-wire technology is completely decoupled from the hydraulic system; this allows multiple brake modes for pedal feel and recuperation. Its behind the wheel enjoy the Intelligent Drive Modes that govern various aspects including modulation of powertrain response, suspension response, brake feel, electronic stability control intervention and many more features at the touch of a button

Geographical Penetration

North America is a Dominating Affective Computing Market Due To The Rapid Growth In Research

North America accounted for the largest market share in the global affective computing market due to the growing research and innovation in the region. North America is renowned for leading advances in technical innovation. A robust ecosystem of startups, research centers and technology firms exist in the area, all of which actively support the creation and application of efficient computer technologies. Affective computing is an area of study that is heavily researched by renowned research institutions and universities in North America.

Growing technological advancements in the region help to boost the regional market growth. For instance, on August 03, 2022, Gartner identified four emerging technologies expected to have a transformational impact on digital advertising. The four technologies are artificial intelligence (AI) for marketing, emotion AI, influence engineering and generative AI. A technology or application's evolutionary trajectory might be seen through the Gartner Hype Cycle, which offers valuable insights for managing the implementation of a particular business objective.

Competitive Landscape

The major global players in the market include Amazon Web Services Inc., Affectiva Inc., Nuance Communications Inc., Nemesysco Ltd., Eyesight Technologies Ltd., Element Human Ltd., Emotibot Technologies Limited, Kairos AR, Inc., Realeyes Data Services Ltd. and AUDEERING GmbH.

COVID-19 Impact Analysis

The pandemic accelerated the pace of digital transformation across industries as organizations sought to adapt to remote work, virtual communication and changes in consumer behavior. Affective Computing technologies, which focus on understanding and responding to human emotions have found increased relevance in virtual communication tools and customer engagement platforms.

Affective Computing plays a role in healthcare applications, including mental health monitoring and virtual care. With the increased demand for remote healthcare solutions during the pandemic, there could be a growing interest in technologies that facilitate emotional understanding and well-being monitoring.

Remote work and the challenges associated with it, including isolation and stress, prompted organizations to focus on employee well-being. Affective Computing tools that gauge and respond to employee emotions have gained attention in the context of remote workforce management. With changes in consumer behavior and an increased reliance on online services, businesses have looked to affective computing solutions to enhance virtual customer interactions. Understanding customer emotions and preferences becomes crucial in a digital-first environment.

Russia-Ukraine War Impact Analysis

Conflict disrupts supply chains, it impacts the availability of components and materials needed for the production of technology products, including affective computing solutions. Geopolitical tensions contribute to economic uncertainties, affecting business and consumer confidence. The influences investment decisions and purchasing behaviors, potentially impacting the adoption of affective computing technologies.

Governments introduce new regulations or change existing ones in response to geopolitical events. The regulatory changes affect the operations and market conditions for technology companies, including those in the affective computing sector. Geopolitical events influence global market sentiment. Investors respond to uncertainties by adjusting their portfolios, which have broader implications for technology stocks and investments.

The affective computing market, like technology markets, often involves international collaboration and partnerships. Geopolitical tensions affect such collaborations, leading to changes in research and development initiatives. Uncertain geopolitical situations influence consumer behavior. Changes in consumer confidence and spending patterns impact the market demand for affective computing applications, especially in sectors such as retail, entertainment and customer service.

By Technology

Touch-based
Touchless

By Component

Software
- Speech Recognition
- Gesture Recognition
- Facial Feature Extraction
- Analytics Software
- Enterprise Software
Hardware
- Sensors
- Cameras
- Storage Devices and Processors
- Others

By Enterprise Size

Small and Medium Enterprises
Large Enterprises

By End-User

Academia and Research
Media and Entertainment
Government and Defense
Healthcare and Life Sciences
IT and Telecom
Retail and E-Commerce
Automotive
BFSI
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On May 05, 2021, Affectiva acquired Smart Eye, the global leader in eye tracking and driver monitoring systems. By merging their highly skilled teams and industry-leading technologies they bring to market unmatched AI solutions for the automotive industry and media analytics.
On February 23, 2021, IBM announced the deployment of "PROPEL-i," a customized end-to-end cloud-native logistics platform created in partnership with IBM Global Business Services, by Safe Xpress, the top supply chain and logistics firm in India.
On May 25, 2023, to help clients select investments, JPMorgan created a ChatGPT-like software program that uses a cutting-edge kind of artificial intelligence. The corporation applied to trademark a product named IndexGPT, as per a document from the bank located in New York.

Why Purchase the Report?

To visualize the global affective computing market segmentation based on technology, component, enterprise size, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of affective computing market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global affective computing market report would provide approximately 69 tables, 70 figures and 211 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Technology
3.2. Snippet by Component
3.3. Snippet by Enterprise Size
3.4. Snippet by End-User
3.5. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Demand for Virtual Assistants
  - 4.1.1.2. Technological Advancement
- 4.1.2. Restraints
  - 4.1.2.1. Low Accuracy and Reliability
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Technology

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 7.1.2. Market Attractiveness Index, By Technology
7.2. Touch-based*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Touchless

8. By Component

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 8.1.2. Market Attractiveness Index, By Component
8.2. Software*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
  - 8.2.2.1. Speech Recognition
  - 8.2.2.2. Gesture Recognition
  - 8.2.2.3. Facial Feature Extraction
  - 8.2.2.4. Analytics Software
  - 8.2.2.5. Enterprise Software
8.3. Hardware
- 8.3.1. Sensors
- 8.3.2. Cameras
- 8.3.3. Storage Devices and Processors
- 8.3.4. Others

9. By Enterprise Size

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 9.1.2. Market Attractiveness Index, By Enterprise Size
9.2. Small and Medium Enterprises*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Large Enterprises

10. By End-User

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.1.2. Market Attractiveness Index, By End-User
10.2. Academia and Research*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Media and Entertainment
10.4. Government and Defense
10.5. Healthcare and Life Sciences
10.6. IT and Telecom
10.7. Retail and E-Commerce
10.8. Automotive
10.9. BFSI
10.10. Others

11. By Region

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 11.1.2. Market Attractiveness Index, By Region
11.2. North America
- 11.2.1. Introduction
- 11.2.2. Key Region-Specific Dynamics
- 11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.2.7.1. U.S.
  - 11.2.7.2. Canada
  - 11.2.7.3. Mexico
11.3. Europe
- 11.3.1. Introduction
- 11.3.2. Key Region-Specific Dynamics
- 11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.3.7.1. Germany
  - 11.3.7.2. UK
  - 11.3.7.3. France
  - 11.3.7.4. Italy
  - 11.3.7.5. Spain
  - 11.3.7.6. Rest of Europe
11.4. South America
- 11.4.1. Introduction
- 11.4.2. Key Region-Specific Dynamics
- 11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.4.7.1. Brazil
  - 11.4.7.2. Argentina
  - 11.4.7.3. Rest of South America
11.5. Asia-Pacific
- 11.5.1. Introduction
- 11.5.2. Key Region-Specific Dynamics
- 11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 11.5.7.1. China
  - 11.5.7.2. India
  - 11.5.7.3. Japan
  - 11.5.7.4. Australia
  - 11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
- 11.6.1. Introduction
- 11.6.2. Key Region-Specific Dynamics
- 11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Enterprise Size
- 11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

12. Competitive Landscape

12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis

13. Company Profiles

13.1. Amazon Web Services Inc.*
- 13.1.1. Company Overview
- 13.1.2. Product Portfolio and Description
- 13.1.3. Financial Overview
- 13.1.4. Key Developments
13.2. Affectiva Inc.
13.3. Nuance Communications Inc.
13.4. Nemesysco Ltd.
13.5. Eyesight Technologies Ltd.
13.6. Element Human Ltd.
13.7. Emotibot Technologies Limited
13.8. Kairos AR, Inc.
13.9. Realeyes Data Services Ltd.
13.10. AUDEERING GmbH

LIST NOT EXHAUSTIVE