The healthcare industry continues to be a hotbed of innovation, with activity driven by telemedicine, real-time diagnostics, smart hospitals and access to digital therapies, as well as the growing importance of technologies such as artificial intelligence (AI), the Internet of Things (IoT), augmented reality (AR), robotics and data management practices. In the last three years alone, there have been over 106,000 patents filed and granted in the healthcare industry, according to GlobalData’s report on Internet of Things in Healthcare: Emotion sensing facial recognition systems.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
200+ innovations will shape the healthcare industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the healthcare industry using innovation intensity models built on over 443,000 patents, there are 200+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, smart helmets, body temperature sensors, and software as a medical device (SaMD) are disruptive technologies that are in the early stages of application and should be tracked closely. Smart balloon catheters, point-of-care molecular diagnostics, and automated immunoassay analysers are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are smart contact lenses and GPS integrated fitness monitors, which are now well established in the industry.
Innovation S-curve for Internet of Things in the healthcare industry
Emotion sensing facial recognition systems is a key innovation area in Internet of Things
Internet of Things (IoT) refers to the overall network of interconnected devices and the technology that enables communication between them as well as with the cloud server. Integration of facial emotion recognition (FER) into the IoT would facilitate better human-machine interactions. FER is the technology that automatically identifies facial expressions from both videos and static images and helps recognise the emotional state of a person. FER uses the pattern recognition algorithm to identify the emotions of a person and information regarding how they feel.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 140+ companies, spanning technology vendors, established healthcare companies, and up-and-coming start-ups engaged in the development and application of emotion sensing facial recognition systems.
Key players in emotion sensing facial recognition systems – a disruptive innovation in the healthcare industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to emotion sensing facial recognition systems
Source: GlobalData Patent Analytics
Koninklijke Philips is one of the leading patent filers in the market for emotion sensing facial recognition systems. Some other key patent filers in the field include Samsung Group and Microsoft. In terms of application diversity, InfoMotion Sports Technologies leads the pack, followed by ASTR and Microsoft. By means of geographic reach, NIKE held the top position, followed by Amer Sports and ATSR in second and third spots, respectively.
Facial expression emotion recognition will help to recognise the emotions from human facial expressions. FER will help in several applications, such as the diagnosis of psychological diseases, social marketing campaigns, physiological interaction identification, and the healthcare domain. The addition of facial emotion recognition to IoT will be helpful to achieve better communication between the devices and human beings. IoT-FER can be used in the diagnosis of cognitive disorders by detecting the early sign of neurological disorders. In addition, FER can be used in the automotive industry to identify fatigue from drivers’ facial expressions and improve overall safety while driving the vehicle. FER can be successfully utilised by market research firms.
To further understand how Internet of Things is disrupting the healthcare industry, access GlobalData’s latest thematic research report on Internet of Things (IoT) in Healthcare.
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData’s Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
