Human data collection helps capture granular behavioral data like physical, geographical, cultural, and more…

As the world around us becomes more digitally focused, the way we interact with machines, data and each other is becoming more prominent through touchscreens, gestures, facial recognition, voice commands, and beyond. This revolution is driven by artificial intelligence (AI) and machine learning (ML) algorithms that rely on accurate ground truth data to produce effective recognition of the real world.

ML and AI programs need high-quality data at scale to realistically improve efficiency. Computer vision is closing the gap between real-world images and perceived images captured by cameras. It is equally important to have collection initiatives for verbal speech, as well as human gestures and inanimate objects and spaces.

Human Data Collection helps capture the granular behavioral data (physical, geographical, cultural, and more) that can be used to tune the user experience for specific markets, cultures, and demanding applications.

• Emotion Recognition. This technology allows the software to “read” the emotions on a human face using advanced image processing or audio data processing. We can now capture “micro-expressions,” or subtle body language cues, and vocal intonation that portrays a person’s feelings. Users may include law enforcers, who want to detect more information about someone during an interrogation. It also has a wide range of applications for marketers.
• Image Recognition. Image recognition is the process of identifying and detecting an object or feature in a digital image or video, and AI is increasingly being stacked on top of this technology to great effect. AI can search social media platforms for photos and compare them to a wide range of data sets to decide which ones are most relevant during image searches. Image recognition technology can also be used to detect license plates, diagnose diseases, analyze clients and their opinions and verify users based on their faces.
• Biometrics. This technology can identify, measure and analyze human behavior and physical aspects of the body’s structure and form to allow for more natural interactions between humans and machines.

Body language can be more powerful than the spoken word. How people react to and behave with products can reveal richer information than from that which is spoken. The spectrum of behavior, e.g. gestures or facial expressions, or eye movement, can express the user experience with tremendous depth and more importantly, contradict what an individual may be said to reveal a fuller truth. Properly recognizing human behaviors such as gestures and facial expressions is especially critical in understanding someone with limited verbal communication skills, such as young children or people with speech disabilities.

• How much to do and how much is enough: It’s important to determine how much data is enough data to ensure that the algorithms work correctly. Human data, with different body language, hand gestures, and facial expressions, can be confusing and difficult to learn for computers. Determining the right amount of data to collect is difficult.
• Human data capture is difficult: The most critical step is to fully understand what kind of human data is needed and all the associated parameters, before executing. Once you have decided how many participants are needed for an application, it requires special planning and research to determine how and where to find those people that exhibit varying hand gestures, body language, facial expressions, and more. Failing to work through this process in a methodical way will only lead to extra cycles, time, and money to get the right data.
• There is no standard: Another misconception is that there is, or should be, a standard for data capture. But each project is unique, based on the product and the scenarios needed for optimizing it. One can standardize the execution aspect, but only after carefully when planning and designing the data capture process.