AI Based Gesture Recognition Solution – Overview
- Unvoiced, body-only conversation between humans and robots is made possible via gesture recognition.
- Gestures are a simple and straightforward way to interact with people and your environment. Therefore, using hand gestures to communicate with computers makes complete sense.
- However, there are other challenges, starting with the requirement to wave your hands in front of the little screen on your smartphone and finishing with the sophisticated machine learning algorithms needed to recognise motions other than the standard thumbs-up. Is the work worth the juice? Let’s look at it, starting with vocabulary and going on to technology specifics.
AI gesture recognition: How does it function?
- Gesture recognition provides a computer with real-time data so it can execute the user’s instructions. Motion sensors in a gadget can monitor and decipher gestures, making them the primary source of data input.
- The majority of gesture recognition solutions integrate machine learning methods with infrared and 3D depth-sensing cameras. Since they were trained using labelled depth pictures of hands, machine learning algorithms can now discern between the positions of hands and fingers.
There are three fundamental layers of gesture recognition:
Detection – After a camera detects hand or body motions, a machine learning approach separates the image to determine hand edges and positions.
Tracking – A gadget records each movement and provides exact data for data processing by tracking motions frame by frame.
Recognition –The system tries to find trends using the data that has been collected. When it finds a match and recognises the gesture, the system performs the action associated with that motion. The following approach uses feature extraction and classification to accomplish the recognition functionality.
Source: Research Gare
- For hand tracking, several solutions use vision-based systems, although this method has many drawbacks. These systems struggle when hands overlap or aren’t clearly visible since users must move their hands inside a constrained space. However, Gesture recognition systems can recognise both static and dynamic motions in real time when using sensor-based motion tracking.
- Depth sensors are employed in sensor-based systems to line up computer-generated images with actual ones. The number and three-dimensional positions of fingers, the palm’s centre, and the direction of the hand are all detected by leap motion sensors as part of hand tracking.
Processed data provides details on a range of subjects, including fingertip angles, distance from the palm’s centre, elevation, and three-dimensional coordinates. The hand gesture recognition system for image processing trains its algorithms using data from the depth and leap motion sensors.
- The system can distinguish a hand from its surroundings using colour and depth data. From the hand sample, the arm, wrist, palm, and fingers are further divided. The algorithm ignores the arm and wrist because they don’t contain gesture data.
- Then, the system collects information on the dimensions of the palm, the placement of the fingers, the height of the fingertips, the separation between the fingertips and the centre of the palm, among other things.
- In order to represent a gesture, the system then gathers all of the characteristics it has retrieved into a feature vector. To detect the user’s gesture, AI-based hand gesture identification software compares the feature vector to a database of various motions.
Depth sensors are crucial for hand tracking systems because they allow users to dispense with specialty wearables like gloves and make HCI more natural.
How We Developed an AI Based Gesture Recognition Solution for A Start-up Firm.
- OptiSol helped a Startup company in building an AI/ML-based solution approach that helps non-technical users to record sign translations of bible verses.
- This application allows sign language experts to login into the web portal and uses gesture recognition.
- This platform is meant to be used by non-technical folks to record sign language translation of Bible Verses.
- Then use the data to train the gesture recognition model to interpret and translated the Bible version from Sign Language gestures.
- This platform focuses on the need to reach sign-language users and give them an opportunity to read and interpret bible verses.
- Ability to translate sign-language gestures of Bible Verses to many international languages easily.
Market size: Gesture recognition
The gesture recognition market size was valued at USD 14.08 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 19.1% from 2022 to 2030.