5th Floor, Rajapushpa Summit
Nanakramguda Rd, Financial District, Gachibowli
Hyderabad, Telangana - 500008
The Gesture Recognition Market is Segmented by Technology (Touch-based Gesture Recognition, Touchless Gesture Recognition), End-user Industry (Automotive, Healthcare, Consumer Electronics, Gaming, Aerospace and Defense, and Other End-user Industries), and Region.
2018 - 2026
Fastest Growing Market:
The gesture recognition market is expected to register a CAGR of over 27.9% over the forecast period (2021 - 2026). The development of artificial intelligence (AI) has given rise to gesture-recognition-based devices. Douwe Egberts has come up with an innovative machine, which was placed at the Tambo International Airport, which can detect travelers who yawned or looked sleepy and dispense free cups of coffee to them. The company was able to take benefit of face recognition technology to advertise and market its brand innovatively.
In the past, the interaction between humans and electronic devices was quite different. For instance, human interaction with TVs required a remote. However, today, gesture recognition technology is being increasingly implemented for human-device interaction, due to an increased acceptance of gesture-enabled electronic devices, across various industry verticals; for example, switching through television channels or radio stations.
The evolution of GUI technology from the use of texts as inputs to the use of gestures as inputs has paved the way for the emergence of gesture recognition technology. The use of gesture recognition is increasing in various sectors. One recent development in this area is the interaction of humans with machines, by using hand gesture recognition. Another development is the use of hand gesture recognition to control computer applications.
With continuous technological developments, the companies in the market studied have been manufacturing products incorporated with new and innovative features. Omron Corporation has developed the gesture recognition technology, by simultaneously recognizing the position, shape, and motion of a person's hand or finger, by referencing a camera-recorded image.
A gesture recognition application system comprises several key hardware and software components, all of which must be tightly integrated to provide a compelling user experience. A camera is the first component, which captures the raw data that represent the user’s actions. Generally, this raw data is then processed to reduce the noise in the signal, for example, or (in the case of 3D cameras) to compute the depth map.
Moreover, specialized algorithms subsequently interpret the processed data, translating the movements into actionable commands that a computer can understand. Subsequently, an application integrates these actionable commands with user feedback, which must be natural, as well as engaging. Adding to the overall complexity of the solution, the algorithms and applications are increasingly being implemented on embedded systems, with limited processing, storage, and other resources.
Adequate integration of these components, in order to deliver a compelling gesture control experience, is not a simple task. The complexity is further magnified by the demands of gesture control applications. In particular, gesture control systems must be highly interactive and be able to process significant volumes of data, with imperceptible latency.
Gesture recognition is the conversion of human movements or signals to commands using a mathematical algorithm. It enables people to interrelate with machines in the absence of physical devices, as an input mechanism to perform desired actions in a system. The technology interprets human gestures and movements, such as movement of hands, fingers, arms, head, or the entire body. It allows users to operate and control devices merely with their gestures.
|Touch-based Gesture Recognition|
|Touchless Gesture Recognition|
|By End-user Industry|
|Aerospace and Defense|
|Other End-user Industries|
Report scope can be customized per your requirements. Click here.
Touch-based gesture recognition consists of single- and multi-touch screens, which are widely used in consumer electronics. A single touch-based function can be used in many devices, such as smartphones. For instance, a single-swipe touch can be used to access the menu bar in any smartphone.
Multi-touch-based gesture recognition is used in functions, such as zoom-in, zoom-out, and three-finger screenshot in smartphones. Functions, such as desktop swap and access to the menu in Windows 10, can be found on the trackpads of laptops. Currently, the touch-based gesture recognition segment dominates the market studied, due to the high market penetration of laptops and smartphones that have the aforementioned basic functionalities. The segment is expected to remain the same over the forecast period as well.
Smartphones are expected to witness continuous growth over the next six years, as companies are shifting their focus to the Asia-Pacific region, especially India, by launching low-cost and feature-rich smartphones. This is expected to have a positive impact on the growth of the market studied.
Currently, smartphone manufacturers are launching phones that incorporate touch-based gesture recognition features, such as double tap to sleep and wake. In addition, laptop manufacturers are launching low-cost products that use touch-based gesture recognition, thereby, augmenting the availability of the technology.
To understand key trends, Download Sample Report
The North American market for gesture recognition is led by the United States, due to the presence of major tech firms and startups in the country. R&D investment in the United States is very high. The country produces the most advanced degrees in science and engineering and high-impact scientific publications. It is the largest provider of information services, globally.
Deep learning forms a base for gesture recognition. In 2017, the deep-learning software market in the region was estimated to be USD 80 million and is expected to reach USD 130 million in 2019.
Also, in terms of artificial intelligence (taxonomy includes gesture-recognition-based products and services providers), the United States occupies the leading position with 415 companies, followed by the United Kingdom with 67 companies, and Canada with 29 companies. The average funding raised by the companies, particularly in the field of gesture control, is USD 7.8 million.
Canada-based Thalmic Labs manufactured a gesture recognition device that can be worn on the forearm, called Myo. This armband can be integrated with various applications, such as presentations and gaming or as a controller for drones. In terms of demand, the United States is helping in setting the stage for record sales of the latest consumer electronics. Disposable personal income increased by 1.8% in 2017 and is likely to increase by more than 2.0% in 2018. As a result, revenue in the consumer electronics industry is expected to amount to USD 72,443 million in 2018, in the United States.
To understand geography trends, Download Sample Report.
The gesture recognition market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. These major players with a prominent share in the market are focusing on expanding their customer bases across foreign countries. These companies are leveraging on strategic collaborative initiatives to increase their market shares and profitability.
The companies operating in the market are also acquiring start-ups working on autonomous delivery robot technologies to strengthen their product capabilities. In July 2018, in Beijing, Intel shared a series of collaborations with Baidu on artificial intelligence (AI), including powering Baidu’s Xeye, a new artificial intelligence retail camera with Intel Movidius vision processing units (VPUs).
1.1 Study Assumptions
1.2 Scope of the Study
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
4.1 Market Overview
4.2 Market Drivers
4.2.1 Technological Advancements for Efficient HMI and Demand for Cost-effective Features
4.2.2 Evolution of Artificial Intelligence and Machine Learning Technology Augmented with Fall in Sensor Prices
4.2.3 Increasing Use of Devices Supporting Gesture Recognition across End-user Industries
4.3 Market Restraints
4.3.1 Algorithms, Mathematical, and Other Complexities Associated with the Use of Gesture Recognition Technology
4.3.2 High Battery Power Consumption by Gesture Sensors, due to the ‘Always-on’ User Interface
4.4 Value Chain / Supply Chain Analysis
4.5 Industry Attractiveness - Porter's Five Forces Analysis
4.5.1 Threat of New Entrants
4.5.2 Bargaining Power of Buyers/Consumers
4.5.3 Bargaining Power of Suppliers
4.5.4 Threat of Substitute Products
4.5.5 Intensity of Competitive Rivalry
5. MARKET SEGMENTATION
5.1 By Technology
5.1.1 Touch-based Gesture Recognition
5.1.2 Touchless Gesture Recognition
5.2 By End-user Industry
5.2.1 Aerospace and Defense
5.2.3 Consumer Electronics
5.2.6 Other End-user Industries
5.3.1 North America
18.104.22.168 United States
22.214.171.124 United Kingdom
126.96.36.199 Rest of Europe
188.8.131.52 Rest of Asia-Pacific
5.3.4 Rest of the World
184.108.40.206 Latin America
220.127.116.11 Middle-East & Africa
6. COMPETITIVE LANDSCAPE
6.1 Company Profiles
6.1.1 Intel Corporation
6.1.2 Jabil Inc.
6.1.3 Leap Motion Inc.
6.1.4 Microchip Technology Inc.
6.1.5 Sony Corporation
6.1.6 Elliptic Laboratories AS
6.1.7 Thalmic Labs Inc.
6.1.8 Sony Corporation
6.1.9 Pyreos Limited
6.1.10 GestureTek Inc.
6.1.11 Fibaro Group SA
6.1.12 Eyesight Technologies Ltd
7. INVESTMENT ANALYSIS
8. MARKET OPPORTUNITIES AND FUTURE TRENDS
** Subject to Availability