Global Automatic Human Posture Recognition Sales Analysis Report: By Region, Players, Type, Application 2026
On Jan 23, Global Info Research released "Global Automatic Human Posture Recognition Market 2026 by Manufacturers, Regions, Type and Application, Forecast to 2032". This report includes an overview of the development of the Automatic Human Posture Recognition industry chain, the market status of Automatic Human Posture Recognition Market, and key enterprises in developed and developing market, and analysed the cutting-edge technology, patent, hot applications and market trends of Automatic Human Posture Recognition.
According to our (Global Info Research) latest study, the global Automatic Human Posture Recognition market size was valued at US$ 768 million in 2025 and is forecast to a readjusted size of US$ 1174 million by 2032 with a CAGR of 6.3% during review period.
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 is a detailed and comprehensive analysis for global Automatic Human Posture Recognition market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
According to our (Global Info Research) latest study, the global Automatic Human Posture Recognition market size was valued at US$ 768 million in 2025 and is forecast to a readjusted size of US$ 1174 million by 2032 with a CAGR of 6.3% during review period.
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 is a detailed and comprehensive analysis for global Automatic Human Posture Recognition market. Both quantitative and qualitative analyses are presented by company, by region & country, by Type and by Application. As the market is constantly changing, this report explores the competition, supply and demand trends, as well as key factors that contribute to its changing demands across many markets. Company profiles and product examples of selected competitors, along with market share estimates of some of the selected leaders for the year 2025, are provided.
Sample Report Request Automatic Human Posture Recognition
https://www.globalinforesearch.com/reports/3403675/automatic-human-posture-recognition
Market segment by Type: 2D、 3D
Market segment by Application: Personal、 Commercial
Major players covered: OpenPose、 MoveNet、 PoseNet、 ChivaCare、 Sensor Medica、 APECS、 DCpose、 Yugamiru Cloud、 Egoscue、 ErgoMaster - NexGen Ergonomics、 ProtoKinetics、 PhysicalTech、 Bodiometer Home、 PostureRay、 Tracy Dixon-Maynard、 DensePose、 HighHRNet、 AiphaPose
Market segment by region, regional analysis covers:
North America (United States, Canada and Mexico),
Europe (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe),
Asia-Pacific (China, Japan, Korea, India, Southeast Asia, and Australia),
South America (Brazil, Argentina, Colombia, and Rest of South America),
Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Rest of Middle East & Africa).
The content of the study subjects, includes a total of 15 chapters:
Chapter 1, to describe Automatic Human Posture Recognition product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top manufacturers of Automatic Human Posture Recognition, with price, sales, revenue and global market share of Automatic Human Posture Recognition from 2021 to 2025.
Chapter 3, the Automatic Human Posture Recognition competitive situation, sales quantity, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.
Chapter 4, the Automatic Human Posture Recognition breakdown data are shown at the regional level, to show the sales quantity, consumption value and growth by regions, from 2021 to 2032.
Chapter 5 and 6, to segment the sales by Type and application, with sales market share and growth rate by type, application, from 2021 to 2032.
Chapter 7, 8, 9, 10 and 11, to break the sales data at the country level, with sales quantity, consumption value and market share for key countries in the world, from 2021 to 2025.and Automatic Human Posture Recognition market forecast, by regions, type and application, with sales and revenue, from 2026 to 2032.
Chapter 12, market dynamics, drivers, restraints, trends and Porters Five Forces analysis.
Chapter 13, the key raw materials and key suppliers, and industry chain of Automatic Human Posture Recognition.
Chapter 14 and 15, to describe Automatic Human Posture Recognition sales channel, distributors, customers, research findings and conclusion.
https://www.globalinforesearch.com/reports/3403675/automatic-human-posture-recognition
Market segment by Type: 2D、 3D
Market segment by Application: Personal、 Commercial
Major players covered: OpenPose、 MoveNet、 PoseNet、 ChivaCare、 Sensor Medica、 APECS、 DCpose、 Yugamiru Cloud、 Egoscue、 ErgoMaster - NexGen Ergonomics、 ProtoKinetics、 PhysicalTech、 Bodiometer Home、 PostureRay、 Tracy Dixon-Maynard、 DensePose、 HighHRNet、 AiphaPose
Market segment by region, regional analysis covers:
North America (United States, Canada and Mexico),
Europe (Germany, France, United Kingdom, Russia, Italy, and Rest of Europe),
Asia-Pacific (China, Japan, Korea, India, Southeast Asia, and Australia),
South America (Brazil, Argentina, Colombia, and Rest of South America),
Middle East & Africa (Saudi Arabia, UAE, Egypt, South Africa, and Rest of Middle East & Africa).
The content of the study subjects, includes a total of 15 chapters:
Chapter 1, to describe Automatic Human Posture Recognition product scope, market overview, market estimation caveats and base year.
Chapter 2, to profile the top manufacturers of Automatic Human Posture Recognition, with price, sales, revenue and global market share of Automatic Human Posture Recognition from 2021 to 2025.
Chapter 3, the Automatic Human Posture Recognition competitive situation, sales quantity, revenue and global market share of top manufacturers are analyzed emphatically by landscape contrast.
Chapter 4, the Automatic Human Posture Recognition breakdown data are shown at the regional level, to show the sales quantity, consumption value and growth by regions, from 2021 to 2032.
Chapter 5 and 6, to segment the sales by Type and application, with sales market share and growth rate by type, application, from 2021 to 2032.
Chapter 7, 8, 9, 10 and 11, to break the sales data at the country level, with sales quantity, consumption value and market share for key countries in the world, from 2021 to 2025.and Automatic Human Posture Recognition market forecast, by regions, type and application, with sales and revenue, from 2026 to 2032.
Chapter 12, market dynamics, drivers, restraints, trends and Porters Five Forces analysis.
Chapter 13, the key raw materials and key suppliers, and industry chain of Automatic Human Posture Recognition.
Chapter 14 and 15, to describe Automatic Human Posture Recognition sales channel, distributors, customers, research findings and conclusion.
Data Sources:
Via authorized organizations:customs statistics, industrial associations, relevant international societies, and academic publications etc.
Via trusted Internet sources.Such as industry news, publications on this industry, annual reports of public companies, Bloomberg Business, Wind Info, Hoovers, Factiva (Dow Jones & Company), Trading Economics, News Network, Statista, Federal Reserve Economic Data, BIS Statistics, ICIS, Companies House Documentsm, investor presentations, SEC filings of companies, etc.
Via interviews. Our interviewees includes manufacturers, related companies, industry experts, distributors, business (sales) staff, directors, CEO, marketing executives, executives from related industries/organizations, customers and raw material suppliers to obtain the latest information on the primary market;
Via data exchange. We have been consulting in this industry for 16 years and have collaborations with the players in this field. Thus, we get access to (part of) their unpublished data, by exchanging with them the data we have.
Via authorized organizations:customs statistics, industrial associations, relevant international societies, and academic publications etc.
Via trusted Internet sources.Such as industry news, publications on this industry, annual reports of public companies, Bloomberg Business, Wind Info, Hoovers, Factiva (Dow Jones & Company), Trading Economics, News Network, Statista, Federal Reserve Economic Data, BIS Statistics, ICIS, Companies House Documentsm, investor presentations, SEC filings of companies, etc.
Via interviews. Our interviewees includes manufacturers, related companies, industry experts, distributors, business (sales) staff, directors, CEO, marketing executives, executives from related industries/organizations, customers and raw material suppliers to obtain the latest information on the primary market;
Via data exchange. We have been consulting in this industry for 16 years and have collaborations with the players in this field. Thus, we get access to (part of) their unpublished data, by exchanging with them the data we have.
From our partners.We have information agencies as partners and they are located worldwide, thus we get (or purchase) the latest data from them.
Via our long-term tracking and gathering of data from this industry.We have a database that contains history data regarding the market.
Via our long-term tracking and gathering of data from this industry.We have a database that contains history data regarding the market.
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