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市场调查报告书
商品编码
1702392
全球手势辨识市场 - 2025-2032Global Gesture Recognition Market - 2025-2032 |
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2024 年全球手势辨识市场规模达到 213.3 亿美元,预计到 2032 年将达到 667.2 亿美元,在 2025-2032 年预测期内的复合年增长率为 15.32%。
全球手势辨识市场正在蓬勃发展,其日益融入汽车、医疗保健、工业自动化和消费性电子产品的人机互动系统。根据惯性感测器和基于雷达的输入等即时感测技术的最新进展,手势控制系统能够提供低于 50 毫秒的延迟,这对于在自动驾驶汽车和智慧医疗设备等关键环境中使用至关重要。
此外,该技术在穿戴式装置和扩增实境中的适用性已将其使用案例从静态环境扩展到动态的现实世界环境。手势辨识与电脑视觉和讯号处理技术的融合可以提高复杂环境条件下的精确度,正如 IEEE 对多模式系统和嵌入式感测架构的评论所强调的那样。
手势识别市场趋势
一个关键的新兴趋势是向多模式手势识别的转变,其中手势、语音和脸部提示被联合解释,以提高情境感知和准确性。人们越来越关注在医疗保健和公共场所中使用基于雷达的感测技术来取代传统的基于摄影机的系统来保护隐私。同样值得注意的是,人工智慧驱动的节能辨识模型正在转变,该模型可以在对电池敏感的可穿戴设备中实现手势控制。这些进步正在推动需要直觉、非语言人机互动的领域的应用。
全球手势辨识市场动态
将手势控制整合到非接触式公共介面中,以符合健康和安全要求
手势控制在公共介面中的整合越来越多地受到非接触式互动需求的推动,特别是出于对高流量环境中的卫生和安全问题的考虑。 IEEE 的研究重点是利用雷达和支援 AI 的惯性设备开发即时手势辨识系统,特别是针对紧急人机互动和公共终端。这些技术透过减少身体接触的需要,为机场、医院和交通枢纽的使用者体验提供更安全的保障。
多用户和复杂环境场景中的高错误率
由于输入重迭、光照变化和运动模糊,手势辨识系统在复杂的多用户环境中面临巨大的准确性挑战。根据各种标准,依赖基于影像的生物辨识技术(如手势或脸部辨识)的系统在民用应用中的故障率可能高达 2.5%,这是因为影像品质不佳或环境噪音足以影响公共资讯亭或汽车等即时应用。
当多个使用者同时互动或系统部署在不受控制的环境中时,可靠性问题会加剧,从而限制基于手势的介面在交通或智慧城市等领域的可扩展性。这些限制促使 NIST 等联邦机构开发新的测试框架和资料集,以便在现实世界的复杂性下更好地评估系统性能。
Global gesture recognition market size reached US$ 21.33 billion in 2024 and is expected to reach US$ 66.72 billion by 2032, growing with a CAGR of 15.32% during the forecast period 2025-2032.
The global gesture recognition market is gaining momentum with growing integration into human-machine interaction systems across automotive, healthcare, industrial automation, and consumer electronics. According to the recent advances in real-time sensing technologies, including inertial sensors and radar-based input, have enabled gesture control systems to deliver latency under 50 milliseconds-key for use in critical environments like autonomous vehicles and smart medical equipment.
Moreover, the technology's applicability in wearable devices and augmented reality has expanded its use cases beyond static environments to dynamic, real-world contexts. The convergence of gesture recognition with computer vision and signal processing techniques has allowed for increased precision in complex ambient conditions, as highlighted by IEEE reviews of multi-modal systems and embedded sensing architectures.
Gesture Recognition Market Trend
A key emerging trend is the shift toward multimodal gesture recognition, where hand, voice, and facial cues are jointly interpreted to improve context awareness and accuracy. There is a growing focus on using radar-based sensing over traditional camera-based systems for privacy-respecting applications in healthcare and public settings. Also notable is the movement toward AI-driven energy-efficient recognition models that enable gesture control in battery-sensitive wearables. These advancements are driving adoption in fields requiring intuitive, non-verbal human-machine interaction.
Global Gesture Recognition Market Dynamics
Integration of Gesture Control in Contactless Public Interfaces for Health and Safety Compliance
The integration of gesture control in public interfaces is increasingly driven by the demand for contactless interaction, especially in response to hygiene and safety concerns in high-traffic environments. IEEE research highlights the development of real-time gesture recognition systems using radar and AI-enabled inertial devices, specifically targeting emergency human-machine interactions and public terminals. These technologies support safer user experiences in airports, hospitals, and transit hubs by reducing the need for physical contact.
High Error Rates in Multi-User and Complex Environmental Scenarios
Gesture recognition systems face significant accuracy challenges in complex, multi-user environments due to overlapping inputs, varying lighting, and motion blur. According to various standards, systems that rely on image-based biometrics (like gesture or face recognition) can suffer failure rates as high as 2.5% in civilian applications due to poor image quality or environmental noise enough to impact real-time applications like public kiosks or automotive use.
The reliability issues are exacerbated when multiple users interact simultaneously or when the system is deployed in uncontrolled environments, limiting the scalability of gesture-based interfaces across sectors such as transportation or smart cities. These limitations have prompted federal institutions like NIST to develop new testing frameworks and datasets to better evaluate system performance under real-world complexity.
The global gesture recognition market is segmented based on technology, authentication type, component, application, and region.
Touch-Based Gesture Recognition Segment Fueling Market Growth
The adoption of touch-based gesture recognition is rapidly advancing due to its integration in smart consumer electronics, healthcare, and automotive systems. The National Institute of Standards and Technology (NIST) emphasizes the growing demand for accurate biometric systems-such as fingerprint and touch-based inputs-which are critical to securing devices and digital identities in sensitive sectors like defense and law enforcement.
Additionally, NIST's Cybersecurity for IoT Program underlines the significance of secure user-device interaction in IoT ecosystems, where touch-based gesture systems often serve as primary interfaces. Their efforts to guide secure IoT device development underscore the relevance of touch-gesture interfaces in safeguarding connected environments, especially as these systems scale globally across various industries.
Strong Adoption of Gesture Recognition in North America Driven by Automotive and Safety Innovations
North America is experiencing rising demand for gesture recognition technologies, largely due to government-led initiatives to integrate advanced AI and sensor systems into critical sectors like automotive and public safety. According to the US National Institute of Standards and Technology (NIST), it is actively developing operational design and testing standards for gesture-based controls in autonomous vehicles, including co-simulation platforms for evaluating sensor and perception systems.
In parallel, the US Artificial Intelligence Safety Institute (US AISI), housed under NIST, is collaborating with stakeholders across the public and private sectors to create trustworthy AI and HMI (Human-Machine Interaction) standards. This includes evaluating the safety and usability of gesture-based AI systems in high-risk environments, which are seeing increased deployment in defense, public infrastructure, and health monitoring systems.
Technology Analysis
The global gesture recognition market is advancing through innovations in radar-based sensing, edge-computing platforms, and AI-driven recognition systems. IEEE research highlights a shift from traditional camera-based systems to more efficient radar and sensor fusion technologies, which improve accuracy in real-time environments while reducing energy consumption. Additionally, a systematic IEEE review from 2018-2024 shows increasing integration of gesture control in consumer electronics, automotive interfaces, and healthcare applications.
The major global players in the market include Intel Corporation, Jabil Inc., Microchip Technology Inc., Sony Corporation, Ultraleap, Elliptic Laboratories AS, Google LLC, GestureTek Inc., Nice - Polska Sp. z o.o., and Dreamworth Solutions Pvt. Ltd.
Target Audience 2024
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