According to the GSMA, the IoT connections market is predicted to reach 25 billion by 2025, and the IoT market revenue is expected to reach US$ 1.1 trillion within the same period. The applications of devices are likely to trickle down to practically every end-user industry globally as IoT demand grows.
The number of connected wearable devices is also increasing. Sensors on these devices capture, monitor, and analyze vital biological signs like heart rate, pulse, and body temperature. According to Cisco Systems, the number of connected wearable devices globally is estimated to reach 1,105 million by 2022.
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The practice of detecting human emotions from facial expressions is known as facial emotion recognition. The human brain perceives emotions automatically and software that can recognize emotions has recently been developed. The technology is improving all the time and it will soon be able to sense emotions as accurately as our brains.
Emotions play a significant role in the conversation because they provide context. Text/word in conversation consists of linguistic and contextual meanings. In recent years, extracting emotions from text has become a fascinating project. Recognizing emotions from a text with machine learning gives promising and significant results due to the progress of machine learning techniques and hardware to enable the machine learning process.
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Emotion is a crucial factor in determining a student’s attention in-class lectures. The fastest approach to identifying emotion is to understand the emotion symptoms by facial expression, one of the many available methods. It is easy to watch the students’ reactions to a particular topic the teachers are teaching during an instructor-led course. However, with the development of new technologies, the trend is shifting away from instructor-led instruction and self-paced e-learning. The uncaptured emotion of the learner, which plays a crucial role in the teaching and learning process, was one of the major problems.
The Middle East & Africa emotion detection and recognition market is valued at US$ 657.47 million in 2020 and is estimated to reach US$ 1,583.93 million by 2028, growing at a CAGR of 11.9% during the forecast period.
The ability to recognize and detect emotions has increased. Face recognition is as common and unobtrusive as smartphones. You already have it if you can open your smartphone without entering a password. It’s only one of the many uses for this remarkable piece of technology.
New products are being released to assist clients in achieving their performance and environmental objectives. In September 2018, Affectiva introduced Mobile Lab, a mobile AI platform for the automotive industry. The mobile lab car features, part of Affectiva Automotive AI, the first multi-modal in-cabin sensing solution, show how AI can make driving safer in semi and fully autonomous vehicles.
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