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 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.
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 delivers a comprehensive overview of the global Automatic Human Posture Recognition market, with both quantitative and qualitative analyses, to help readers develop growth strategies, assess the competitive landscape, evaluate their position in the current market, and make informed business decisions regarding Automatic Human Posture Recognition. The Automatic Human Posture Recognition market size, estimates, and forecasts are provided in terms of revenue (US$ millions), with 2025 as the base year and historical and forecast data for 2021–2032.
The report segments the global Automatic Human Posture Recognition market comprehensively. Regional market sizes by Type, by Application, by Model, and by player are also provided. For deeper insight, the report profiles the competitive landscape, key competitors, and their respective market rankings, and discusses technological trends and new product developments.
This report will assist Automatic Human Posture Recognition manufacturers, new entrants, and companies across the industry value chain with information on revenues, sales volume, and average prices for the overall market and its sub-segments, by company, by Type, by Application, and by region.
Market Segmentation
Chapter Outline
Chapter 1: Defines the scope of the report and presents an executive summary of market segments (by Type, by Application, by Model, etc.), including the size of each segment and its future growth potential. It offers a high-level view of the current market and its likely evolution in the short, medium, and long term.
Chapter 2: Summarizes global and regional market size and outlines market dynamics and recent developments, including key drivers, restraints, challenges and risks for industry participants, and relevant policy analysis.
Chapter 3: Provides a detailed view of the competitive landscape for Automatic Human Posture Recognition companies, covering revenue share, development plans, and mergers and acquisitions.
Chapter 4: Analyzes segments by Type, detailing the size and growth potential of each segment to help readers identify blue-ocean opportunities.
Chapter 5: Analyzes segments by Application, detailing the size and growth potential of each downstream segment to help readers identify blue-ocean opportunities.
Chapter 6–10: Regional deep dives (North America, Europe, Asia Pacific, Latin America, Middle East & Africa) broken down by country. Each chapter quantifies market size and growth potential by region and key countries, and outlines market development, outlook, addressable space, and capacity.
Chapter 11: Profiles key players, presenting essential information on leading companies, including product/ service offerings, revenue, gross margin, product introductions/portfolios, recent developments, etc.
Chapter 12: Key findings and conclusions of the report.
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Table of Contents
1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Automatic Human Posture Recognition Market Size Growth Rate by Type: 2021 vs 2025 vs 2032
1.2.2 2D
1.2.3 3D
1.3 Market by Model
1.3.1 Global Automatic Human Posture Recognition Market Size Growth Rate by Model: 2021 vs 2025 vs 2032
1.3.2 Real-time Human Pose Estimation
1.3.3 Offline / High-precision Pose Estimation
1.4 Market by Quantity
1.4.1 Global Automatic Human Posture Recognition Market Size Growth Rate by Quantity: 2021 vs 2025 vs 2032
1.4.2 Single-person Pose Estimation
1.4.3 Multi-person Pose Estimation
1.5 Market by Application
1.5.1 Global Automatic Human Posture Recognition Market Growth by Application: 2021 vs 2025 vs 2032
1.5.2 Personal
1.5.3 Commercial
1.6 Assumptions and Limitations
1.7 Study Objectives
1.8 Years Considered
2 Global Growth Trends
2.1 Global Automatic Human Posture Recognition Market Perspective (2021–2032)
2.2 Global Automatic Human Posture Recognition Growth Trends by Region
2.2.1 Global Automatic Human Posture Recognition Market Size by Region: 2021 vs 2025 vs 2032
2.2.2 Automatic Human Posture Recognition Historic Market Size by Region (2021–2026)
2.2.3 Automatic Human Posture Recognition Forecasted Market Size by Region (2027–2032)
2.3 Automatic Human Posture Recognition Market Dynamics
2.3.1 Automatic Human Posture Recognition Industry Trends
2.3.2 Automatic Human Posture Recognition Market Drivers
2.3.3 Automatic Human Posture Recognition Market Challenges
2.3.4 Automatic Human Posture Recognition Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Automatic Human Posture Recognition Players by Revenue
3.1.1 Global Top Automatic Human Posture Recognition Players by Revenue (2021–2026)
3.1.2 Global Automatic Human Posture Recognition Revenue Market Share by Players (2021–2026)
3.2 Global Top Automatic Human Posture Recognition Players Market Share by Company Tier (Tier 1, Tier 2, Tier 3)
3.3 Global Key Players Ranking by Automatic Human Posture Recognition Revenue
3.4 Global Automatic Human Posture Recognition Market Concentration Ratio
3.4.1 Global Automatic Human Posture Recognition Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Automatic Human Posture Recognition Revenue in 2025
3.5 Global Key Players of Automatic Human Posture Recognition Head Offices and Areas Served
3.6 Global Key Players of Automatic Human Posture Recognition, Products and Applications
3.7 Global Key Players of Automatic Human Posture Recognition, Date of General Availability (GA)
3.8 Mergers and Acquisitions, Expansion Plans
4 Automatic Human Posture Recognition Breakdown Data by Type
4.1 Global Automatic Human Posture Recognition Historic Market Size by Type (2021–2026)
4.2 Global Automatic Human Posture Recognition Forecasted Market Size by Type (2027–2032)
5 Automatic Human Posture Recognition Breakdown Data by Application
5.1 Global Automatic Human Posture Recognition Historic Market Size by Application (2021–2026)
5.2 Global Automatic Human Posture Recognition Forecasted Market Size by Application (2027–2032)
6 North America
6.1 North America Automatic Human Posture Recognition Market Size (2021–2032)
6.2 North America Automatic Human Posture Recognition Market Growth Rate by Country: 2021 vs 2025 vs 2032
6.3 North America Automatic Human Posture Recognition Market Size by Country (2021–2026)
6.4 North America Automatic Human Posture Recognition Market Size by Country (2027–2032)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Automatic Human Posture Recognition Market Size (2021–2032)
7.2 Europe Automatic Human Posture Recognition Market Growth Rate by Country: 2021 vs 2025 vs 2032
7.3 Europe Automatic Human Posture Recognition Market Size by Country (2021–2026)
7.4 Europe Automatic Human Posture Recognition Market Size by Country (2027–2032)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Ireland
8 Asia-Pacific
8.1 Asia-Pacific Automatic Human Posture Recognition Market Size (2021–2032)
8.2 Asia-Pacific Automatic Human Posture Recognition Market Growth Rate by Region: 2021 vs 2025 vs 2032
8.3 Asia-Pacific Automatic Human Posture Recognition Market Size by Region (2021–2026)
8.4 Asia-Pacific Automatic Human Posture Recognition Market Size by Region (2027–2032)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia & New Zealand
9 Latin America
9.1 Latin America Automatic Human Posture Recognition Market Size (2021–2032)
9.2 Latin America Automatic Human Posture Recognition Market Growth Rate by Country: 2021 vs 2025 vs 2032
9.3 Latin America Automatic Human Posture Recognition Market Size by Country (2021–2026)
9.4 Latin America Automatic Human Posture Recognition Market Size by Country (2027–2032)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Automatic Human Posture Recognition Market Size (2021–2032)
10.2 Middle East & Africa Automatic Human Posture Recognition Market Growth Rate by Country: 2021 vs 2025 vs 2032
10.3 Middle East & Africa Automatic Human Posture Recognition Market Size by Country (2021–2026)
10.4 Middle East & Africa Automatic Human Posture Recognition Market Size by Country (2027–2032)
10.5 Israel
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 OpenPose
11.1.1 OpenPose Company Details
11.1.2 OpenPose Business Overview
11.1.3 OpenPose Automatic Human Posture Recognition Introduction
11.1.4 OpenPose Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.1.5 OpenPose Recent Development
11.2 MoveNet
11.2.1 MoveNet Company Details
11.2.2 MoveNet Business Overview
11.2.3 MoveNet Automatic Human Posture Recognition Introduction
11.2.4 MoveNet Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.2.5 MoveNet Recent Development
11.3 PoseNet
11.3.1 PoseNet Company Details
11.3.2 PoseNet Business Overview
11.3.3 PoseNet Automatic Human Posture Recognition Introduction
11.3.4 PoseNet Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.3.5 PoseNet Recent Development
11.4 ChivaCare
11.4.1 ChivaCare Company Details
11.4.2 ChivaCare Business Overview
11.4.3 ChivaCare Automatic Human Posture Recognition Introduction
11.4.4 ChivaCare Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.4.5 ChivaCare Recent Development
11.5 Sensor Medica
11.5.1 Sensor Medica Company Details
11.5.2 Sensor Medica Business Overview
11.5.3 Sensor Medica Automatic Human Posture Recognition Introduction
11.5.4 Sensor Medica Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.5.5 Sensor Medica Recent Development
11.6 APECS
11.6.1 APECS Company Details
11.6.2 APECS Business Overview
11.6.3 APECS Automatic Human Posture Recognition Introduction
11.6.4 APECS Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.6.5 APECS Recent Development
11.7 DCpose
11.7.1 DCpose Company Details
11.7.2 DCpose Business Overview
11.7.3 DCpose Automatic Human Posture Recognition Introduction
11.7.4 DCpose Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.7.5 DCpose Recent Development
11.8 Yugamiru Cloud
11.8.1 Yugamiru Cloud Company Details
11.8.2 Yugamiru Cloud Business Overview
11.8.3 Yugamiru Cloud Automatic Human Posture Recognition Introduction
11.8.4 Yugamiru Cloud Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.8.5 Yugamiru Cloud Recent Development
11.9 Egoscue
11.9.1 Egoscue Company Details
11.9.2 Egoscue Business Overview
11.9.3 Egoscue Automatic Human Posture Recognition Introduction
11.9.4 Egoscue Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.9.5 Egoscue Recent Development
11.10 ErgoMaster - NexGen Ergonomics
11.10.1 ErgoMaster - NexGen Ergonomics Company Details
11.10.2 ErgoMaster - NexGen Ergonomics Business Overview
11.10.3 ErgoMaster - NexGen Ergonomics Automatic Human Posture Recognition Introduction
11.10.4 ErgoMaster - NexGen Ergonomics Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.10.5 ErgoMaster - NexGen Ergonomics Recent Development
11.11 ProtoKinetics
11.11.1 ProtoKinetics Company Details
11.11.2 ProtoKinetics Business Overview
11.11.3 ProtoKinetics Automatic Human Posture Recognition Introduction
11.11.4 ProtoKinetics Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.11.5 ProtoKinetics Recent Development
11.12 PhysicalTech
11.12.1 PhysicalTech Company Details
11.12.2 PhysicalTech Business Overview
11.12.3 PhysicalTech Automatic Human Posture Recognition Introduction
11.12.4 PhysicalTech Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.12.5 PhysicalTech Recent Development
11.13 Bodiometer Home
11.13.1 Bodiometer Home Company Details
11.13.2 Bodiometer Home Business Overview
11.13.3 Bodiometer Home Automatic Human Posture Recognition Introduction
11.13.4 Bodiometer Home Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.13.5 Bodiometer Home Recent Development
11.14 PostureRay
11.14.1 PostureRay Company Details
11.14.2 PostureRay Business Overview
11.14.3 PostureRay Automatic Human Posture Recognition Introduction
11.14.4 PostureRay Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.14.5 PostureRay Recent Development
11.15 Tracy Dixon-Maynard
11.15.1 Tracy Dixon-Maynard Company Details
11.15.2 Tracy Dixon-Maynard Business Overview
11.15.3 Tracy Dixon-Maynard Automatic Human Posture Recognition Introduction
11.15.4 Tracy Dixon-Maynard Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.15.5 Tracy Dixon-Maynard Recent Development
11.16 DensePose
11.16.1 DensePose Company Details
11.16.2 DensePose Business Overview
11.16.3 DensePose Automatic Human Posture Recognition Introduction
11.16.4 DensePose Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.16.5 DensePose Recent Development
11.17 HighHRNet
11.17.1 HighHRNet Company Details
11.17.2 HighHRNet Business Overview
11.17.3 HighHRNet Automatic Human Posture Recognition Introduction
11.17.4 HighHRNet Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.17.5 HighHRNet Recent Development
11.18 AiphaPose
11.18.1 AiphaPose Company Details
11.18.2 AiphaPose Business Overview
11.18.3 AiphaPose Automatic Human Posture Recognition Introduction
11.18.4 AiphaPose Revenue in Automatic Human Posture Recognition Business (2021–2026)
11.18.5 AiphaPose Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.1.1 Research Programs/Design
13.1.1.2 Market Size Estimation
13.1.1.3 Market Breakdown and Data Triangulation
13.1.2 Data Source
13.1.2.1 Secondary Sources
13.1.2.2 Primary Sources
13.2 Author Details
13.3 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 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.
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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 Date: 2025-01-28
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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.
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Published Date: 2026-03-11
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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 Date: 2026-03-11
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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
Pages: 127
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
Pages: 97
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 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.
Published: 2026-03-08
Pages: 130
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 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
Pages: 148
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