Automatic Human Posture Recognition Market Size(US$)

CAGR 2026-2032
6.5%
Market Size,2032
USD 1,151
Million
Market Snapshot
Source: Secondary research, interviews with experts, and QYResearch analysis
The global market for Automatic Human Posture Recognition was estimated to be worth US$ 746 million in 2025 and is projected to reach US$ 1151 million, growing at a CAGR of 6.5% from 2026 to 2032.
Automatic human pose recognition refers to the core technology that uses computer vision and deep learning algorithms to automatically detect and analyze the positions of key human joints (such as head, shoulders, elbows, wrists, hips, knees, and ankles) from images or videos captured by cameras, constructing a human "skeleton" model to determine the current posture or movement pattern of a person, such as standing, sitting, walking, bending over, raising hands, or falling. The system typically includes several steps: human detection, keypoint localization, skeleton modeling, and pose classification. It can run on ordinary cameras or even mobile phone cameras and is widely used in motion and rehabilitation training action evaluation, intelligent fitness/dance scoring, human-computer interaction, abnormal posture (such as falls and climbing over railings) recognition in security scenarios, and intelligent monitoring of dangerous postures and violations by workers in industrial settings.
From the demand side, automatic human pose recognition has quietly become a "fundamental capability," although most end-users are unaware of this term. On one hand, there are To C scenarios: home fitness apps, smart TVs/motion-sensing games, online rehabilitation training, and "AI motion scoring" in mini-programs are all using pose recognition to replace expensive motion capture equipment, allowing a mobile phone or camera to perform functions such as posture assessment, yoga/dance movement correction, and monitoring of adolescent hunchback; on the other hand, there are To B/To G scenarios: nursing homes and home care use it for fall/prolonged bed rest monitoring, factories, warehouses, and construction sites use it to identify violations such as bending over to carry objects, climbing to high places, and entering dangerous areas, and subways/shopping malls/scenic spots are beginning to experiment with "pose + behavior" recognition to detect abnormal gatherings, fights, and fence jumping. As the advantages of "non-intrusive, non-wearable, and low-cost" are recognized, this technology is expanding from single-point pilot projects to become a "video surveillance upgrade package" and a "standard capability for smart terminals."
From the supply and competitive landscape perspective, automatic human pose recognition has entered a stage where "general algorithms are reaching their limits, and scenarios and closed loops determine value": the underlying 2D/3D pose models have basically been leveled by large companies and open-source frameworks, and simply selling SDKs or model interfaces has high prices and high substitutability; the real bargaining power lies with players who integrate pose recognition with a complete business closed loop—for example, providing "action scoring + training prescriptions + risk warnings" in the rehabilitation/sports field, directly linking to alarms, assessments, and team management in industrial safety, and integrating with nursing systems, bedside alarms, and family apps in elderly care. Looking further ahead, as edge computing capabilities are deployed to cameras, NVRs, and other devices, whoever can develop sufficiently lightweight models that perform stably under complex lighting, occlusion, and multi-person scenarios, and who can leverage long-term data to build an "industry action library" and risk control models, will have the opportunity to upgrade from being "an algorithm provider" to a "service provider for safety, health, and efficiency improvement in a specific vertical scenario," securing recurring subscription and project-based revenue, rather than simply selling a technology solution once.
This report provides a comprehensive view of the global market for Automatic Human Posture Recognition, covering total sales revenue, the market share and ranking of key companies, along with analyses by region & country, by Type, and by Application.
The Automatic Human Posture Recognition market size, estimations, and forecasts are presented in terms of sales revenue ($ millions), with 2025 as the base year and historical and forecast data from 2021 to 2032. The report combines quantitative and qualitative analysis to help readers develop growth strategies, assess the competitive landscape, evaluate their position in the current marketplace, and make informed business decisions regarding Automatic Human Posture Recognition.
Market Segmentation
Chapter Outline
Chapter 1: Introduces the scope of the report and the global market size (value). It also summarizes market dynamics and recent developments; identifies key drivers and restraints; outlines challenges and risks for players; reviews relevant industry policies.
Chapter 2: Provides a detailed analysis of the Automatic Human Posture Recognition companies' competitive landscape—including revenue shares, recent development plans, and mergers and acquisitions (M&A).
Chapter 3: Analyzes market segmentation by Type, presenting the size and growth potential of each segment to help readers identify blue-ocean opportunities.
Chapter 4: Analyzes market segmentation by Application, presenting the size and growth potential of each downstream segment to help readers identify blue-ocean opportunities.
Chapter 5: Presents Automatic Human Posture Recognition revenue at the regional level. It offers a quantitative assessment of market size and growth potential by region and summarizes market development, future prospects, addressable space, and country-level market size worldwide.
Chapter 6: Presents Automatic Human Posture Recognition revenue at the country level. It provides segmented data by Type and by Application for each country/region.
Chapter 7: Profiles key players, detailing the main companies' product revenue, gross margin, product portfolios, recent developments, etc.
Chapter 8: Analysis of Value Chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.
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Table of Contents
1 Market Overview
1.1 Automatic Human Posture Recognition Product Introduction
1.2 Global Automatic Human Posture Recognition Market Size Forecast (2021–2032)
1.3 Automatic Human Posture Recognition Market Trends & Drivers
1.3.1 Automatic Human Posture Recognition Industry Trends
1.3.2 Automatic Human Posture Recognition Market Drivers & Opportunities
1.3.3 Automatic Human Posture Recognition Market Challenges
1.3.4 Automatic Human Posture Recognition Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Automatic Human Posture Recognition Players Revenue Ranking (2025)
2.2 Global Automatic Human Posture Recognition Revenue by Company (2021–2026)
2.3 Key Companies’ R&D and Operations Footprint and Headquarters
2.4 Key Companies Automatic Human Posture Recognition Product Offerings
2.5 Key Companies General Availability (GA) Timeline for Automatic Human Posture Recognition
2.6 Automatic Human Posture Recognition Market Competitive Analysis
2.6.1 Automatic Human Posture Recognition Market Concentration Rate (2021–2026)
2.6.2 Top 5 and Top 10 Global Companies by Automatic Human Posture Recognition Revenue in 2025
2.6.3 Global Companies by Tier (Tier 1, Tier 2, Tier 3), based on Automatic Human Posture Recognition revenue, 2025
2.7 Mergers & Acquisitions and Expansion
3 Segmentation Automatic Human Posture Recognition Market Classification
3.1 Introduction by Type
3.1.1 2D
3.1.2 3D
3.1.3 Global Automatic Human Posture Recognition Sales Value by Type
3.1.3.1 Global Automatic Human Posture Recognition Sales Value by Type (2021 vs 2025 vs 2032)
3.1.3.2 Global Automatic Human Posture Recognition Sales Value, by Type (2021–2032)
3.1.3.3 Global Automatic Human Posture Recognition Sales Value, by Type (%), 2021–2032
3.2 Introduction by Model
3.2.1 Real-time Human Pose Estimation
3.2.2 Offline / High-precision Pose Estimation
3.2.3 Global Automatic Human Posture Recognition Sales Value by Model
3.2.3.1 Global Automatic Human Posture Recognition Sales Value by Model (2021 vs 2025 vs 2032)
3.2.3.2 Global Automatic Human Posture Recognition Sales Value, by Model (2021–2032)
3.2.3.3 Global Automatic Human Posture Recognition Sales Value, by Model (%), 2021–2032
3.3 Introduction by Quantity
3.3.1 Single-person Pose Estimation
3.3.2 Multi-person Pose Estimation
3.3.3 Global Automatic Human Posture Recognition Sales Value by Quantity
3.3.3.1 Global Automatic Human Posture Recognition Sales Value by Quantity (2021 vs 2025 vs 2032)
3.3.3.2 Global Automatic Human Posture Recognition Sales Value, by Quantity (2021–2032)
3.3.3.3 Global Automatic Human Posture Recognition Sales Value, by Quantity (%), 2021–2032
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 Personal
4.1.2 Commercial
4.2 Global Automatic Human Posture Recognition Sales Value by Application
4.2.1 Global Automatic Human Posture Recognition Sales Value by Application (2021 vs 2025 vs 2032)
4.2.2 Global Automatic Human Posture Recognition Sales Value by Application (2021–2032)
4.2.3 Global Automatic Human Posture Recognition Sales Value by Application (%), 2021–2032
5 Segmentation by Region
5.1 Global Automatic Human Posture Recognition Sales Value by Region
5.1.1 Global Automatic Human Posture Recognition Sales Value by Region: 2021 vs 2025 vs 2032
5.1.2 Global Automatic Human Posture Recognition Sales Value by Region (2021–2026)
5.1.3 Global Automatic Human Posture Recognition Sales Value by Region (2027–2032)
5.1.4 Global Automatic Human Posture Recognition Sales Value by Region (%), 2021–2032
5.2 North America
5.2.1 North America Automatic Human Posture Recognition Sales Value, 2021–2032
5.2.2 North America Automatic Human Posture Recognition Sales Value by Country (%), 2025 vs 2032
5.3 Europe
5.3.1 Europe Automatic Human Posture Recognition Sales Value, 2021–2032
5.3.2 Europe Automatic Human Posture Recognition Sales Value by Country (%), 2025 vs 2032
5.4 Asia Pacific
5.4.1 Asia Pacific Automatic Human Posture Recognition Sales Value, 2021–2032
5.4.2 Asia Pacific Automatic Human Posture Recognition Sales Value by Subregion (%), 2025 vs 2032
5.5 South America
5.5.1 South America Automatic Human Posture Recognition Sales Value, 2021–2032
5.5.2 South America Automatic Human Posture Recognition Sales Value by Country (%), 2025 vs 2032
5.6 Middle East & Africa
5.6.1 Middle East & Africa Automatic Human Posture Recognition Sales Value, 2021–2032
5.6.2 Middle East & Africa Automatic Human Posture Recognition Sales Value by Country (%), 2025 vs 2032
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Automatic Human Posture Recognition Sales Value Growth Trends, 2021 vs 2025 vs 2032
6.2 Key Countries/Regions Automatic Human Posture Recognition Sales Value, 2021–2032
6.3 United States
6.3.1 United States Automatic Human Posture Recognition Sales Value, 2021–2032
6.3.2 United States Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.3.3 United States Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.4 Europe
6.4.1 Europe Automatic Human Posture Recognition Sales Value, 2021–2032
6.4.2 Europe Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.4.3 Europe Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.5 China
6.5.1 China Automatic Human Posture Recognition Sales Value, 2021–2032
6.5.2 China Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.5.3 China Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.6 Japan
6.6.1 Japan Automatic Human Posture Recognition Sales Value, 2021–2032
6.6.2 Japan Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.6.3 Japan Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.7 South Korea
6.7.1 South Korea Automatic Human Posture Recognition Sales Value, 2021–2032
6.7.2 South Korea Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.7.3 South Korea Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.8 Southeast Asia
6.8.1 Southeast Asia Automatic Human Posture Recognition Sales Value, 2021–2032
6.8.2 Southeast Asia Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.8.3 Southeast Asia Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
6.9 India
6.9.1 India Automatic Human Posture Recognition Sales Value, 2021–2032
6.9.2 India Automatic Human Posture Recognition Sales Value by Type (%), 2025 vs 2032
6.9.3 India Automatic Human Posture Recognition Sales Value by Application, 2025 vs 2032
7 Company Profiles
7.1 OpenPose
7.1.1 OpenPose Profile
7.1.2 OpenPose Main Business
7.1.3 OpenPose Automatic Human Posture Recognition Products, Services, and Solutions
7.1.4 OpenPose Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.1.5 OpenPose Recent Developments
7.2 MoveNet
7.2.1 MoveNet Profile
7.2.2 MoveNet Main Business
7.2.3 MoveNet Automatic Human Posture Recognition Products, Services, and Solutions
7.2.4 MoveNet Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.2.5 MoveNet Recent Developments
7.3 PoseNet
7.3.1 PoseNet Profile
7.3.2 PoseNet Main Business
7.3.3 PoseNet Automatic Human Posture Recognition Products, Services, and Solutions
7.3.4 PoseNet Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.3.5 PoseNet Recent Developments
7.4 ChivaCare
7.4.1 ChivaCare Profile
7.4.2 ChivaCare Main Business
7.4.3 ChivaCare Automatic Human Posture Recognition Products, Services, and Solutions
7.4.4 ChivaCare Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.4.5 ChivaCare Recent Developments
7.5 Sensor Medica
7.5.1 Sensor Medica Profile
7.5.2 Sensor Medica Main Business
7.5.3 Sensor Medica Automatic Human Posture Recognition Products, Services, and Solutions
7.5.4 Sensor Medica Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.5.5 Sensor Medica Recent Developments
7.6 APECS
7.6.1 APECS Profile
7.6.2 APECS Main Business
7.6.3 APECS Automatic Human Posture Recognition Products, Services, and Solutions
7.6.4 APECS Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.6.5 APECS Recent Developments
7.7 DCpose
7.7.1 DCpose Profile
7.7.2 DCpose Main Business
7.7.3 DCpose Automatic Human Posture Recognition Products, Services, and Solutions
7.7.4 DCpose Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.7.5 DCpose Recent Developments
7.8 Yugamiru Cloud
7.8.1 Yugamiru Cloud Profile
7.8.2 Yugamiru Cloud Main Business
7.8.3 Yugamiru Cloud Automatic Human Posture Recognition Products, Services, and Solutions
7.8.4 Yugamiru Cloud Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.8.5 Yugamiru Cloud Recent Developments
7.9 Egoscue
7.9.1 Egoscue Profile
7.9.2 Egoscue Main Business
7.9.3 Egoscue Automatic Human Posture Recognition Products, Services, and Solutions
7.9.4 Egoscue Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.9.5 Egoscue Recent Developments
7.10 ErgoMaster - NexGen Ergonomics
7.10.1 ErgoMaster - NexGen Ergonomics Profile
7.10.2 ErgoMaster - NexGen Ergonomics Main Business
7.10.3 ErgoMaster - NexGen Ergonomics Automatic Human Posture Recognition Products, Services, and Solutions
7.10.4 ErgoMaster - NexGen Ergonomics Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.10.5 ErgoMaster - NexGen Ergonomics Recent Developments
7.11 ProtoKinetics
7.11.1 ProtoKinetics Profile
7.11.2 ProtoKinetics Main Business
7.11.3 ProtoKinetics Automatic Human Posture Recognition Products, Services, and Solutions
7.11.4 ProtoKinetics Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.11.5 ProtoKinetics Recent Developments
7.12 PhysicalTech
7.12.1 PhysicalTech Profile
7.12.2 PhysicalTech Main Business
7.12.3 PhysicalTech Automatic Human Posture Recognition Products, Services, and Solutions
7.12.4 PhysicalTech Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.12.5 PhysicalTech Recent Developments
7.13 Bodiometer Home
7.13.1 Bodiometer Home Profile
7.13.2 Bodiometer Home Main Business
7.13.3 Bodiometer Home Automatic Human Posture Recognition Products, Services, and Solutions
7.13.4 Bodiometer Home Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.13.5 Bodiometer Home Recent Developments
7.14 PostureRay
7.14.1 PostureRay Profile
7.14.2 PostureRay Main Business
7.14.3 PostureRay Automatic Human Posture Recognition Products, Services, and Solutions
7.14.4 PostureRay Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.14.5 PostureRay Recent Developments
7.15 Tracy Dixon-Maynard
7.15.1 Tracy Dixon-Maynard Profile
7.15.2 Tracy Dixon-Maynard Main Business
7.15.3 Tracy Dixon-Maynard Automatic Human Posture Recognition Products, Services, and Solutions
7.15.4 Tracy Dixon-Maynard Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.15.5 Tracy Dixon-Maynard Recent Developments
7.16 DensePose
7.16.1 DensePose Profile
7.16.2 DensePose Main Business
7.16.3 DensePose Automatic Human Posture Recognition Products, Services, and Solutions
7.16.4 DensePose Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.16.5 DensePose Recent Developments
7.17 HighHRNet
7.17.1 HighHRNet Profile
7.17.2 HighHRNet Main Business
7.17.3 HighHRNet Automatic Human Posture Recognition Products, Services, and Solutions
7.17.4 HighHRNet Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.17.5 HighHRNet Recent Developments
7.18 AiphaPose
7.18.1 AiphaPose Profile
7.18.2 AiphaPose Main Business
7.18.3 AiphaPose Automatic Human Posture Recognition Products, Services, and Solutions
7.18.4 AiphaPose Automatic Human Posture Recognition Revenue (US$ Million), 2021–2026
7.18.5 AiphaPose Recent Developments
8 Industry Chain Analysis
8.1 Automatic Human Posture Recognition Value Chain
8.2 Automatic Human Posture Recognition Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Key Suppliers of Raw Materials
8.2.3 Cost Structure
8.3 Midstream Analysis
8.4 Downstream (Customer) Analysis
8.5 Sales Model and Sales Channelss
8.5.1 Automatic Human Posture Recognition Sales Model
8.5.2 Sales Channels
8.5.3 Automatic Human Posture Recognition Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.1.1 Research Programs/Design
10.1.1.2 Market Size Estimation
10.1.1.3 Market Breakdown and Data Triangulation
10.1.2 Data Source
10.1.2.1 Secondary Sources
10.1.2.2 Primary Sources
10.2 Author Details
10.3 Disclaimer
Table of Figures
List of Tables
List of Figures
Related Reports
The global market for Automatic Human Posture Recognition was estimated to be worth US$ 705 million in 2024 and is forecast to a readjusted size of US$ 1089 million by 2031 with a CAGR of 6.5% during the forecast period 2025-2031.
Published Date: 2025-01-28
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The global Automatic Human Posture Recognition market size was US$ 746 million in 2025 and is forecast to reach a readjusted size of US$ 1151 million by 2032 with a CAGR of 6.5% during the forecast period 2026-2032.
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The global market for Automatic Human Posture Recognition was estimated to be worth US$ 705 million in 2024 and is forecast to a readjusted size of US$ 1089 million by 2031 with a CAGR of 6.5% during the forecast period 2025-2031.
Published: 2025-01-28
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The global market for Automatic Human Posture Recognition was valued at US$ 705 million in the year 2024 and is projected to reach a revised size of US$ 1089 million by 2031, growing at a CAGR of 6.5% during the forecast period.
Published: 2025-01-28
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The global Automatic Human Posture Recognition market size was US$ 705 million in 2024 and is forecast to a readjusted size of US$ 1089 million by 2031 with a CAGR of 6.5% during the forecast period 2025-2031.
Published: 2025-01-28
Pages: 104
The global Automatic Human Posture Recognition market size was US$ 746 million in 2025 and is forecast to reach a readjusted size of US$ 1151 million by 2032 with a CAGR of 6.5% during the forecast period 2026-2032.
Published: 2026-03-11
Pages: 110
The global Automatic Human Posture Recognition market was valued at US$ 746 million in 2025 and is anticipated to reach US$ 1151 million by 2032, at a CAGR of 6.5% from 2026 to 2032.
Published: 2026-03-11
Pages: 136
The global Automatic Human Posture Recognition market is projected to grow from US$ 746 million in 2025 to US$ 1151 million by 2032, at a CAGR of 6.5% (2026-2032), driven by critical product segments and diverse end‑use applications.
Published: 2026-03-11
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