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 Robotics in Healthcare: Emotion sensing facial recognition systems. Buy the report here.
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, microfluidic devices, static computer-aided implant surgery (s-CAIS), and digital pathology guided robotic surgery are disruptive technologies that are in the early stages of application and should be tracked closely. Interactive exercise system, computer-aided dental prostheses, and automated genetic screening are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas is the automated drug dispensing systems, which is now well established in the industry.
Innovation S-curve for robotics in the healthcare industry
Emotion sensing facial recognition systems is a key innovation area in robotics
Facial emotion recognition (FER) is the technology that scans 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. Integration of FER into robotics would facilitate better human-machine interactions.
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 the second and third spots, respectively.
Facial expression emotion recognition will help in recognising people’s emotions from their facial expressions. FER would help in multiple applications, such as the diagnosis of psychological diseases, social marketing campaigns, physiological interaction identification, and the healthcare domain. 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 the key themes and technologies disrupting the healthcare industry, access GlobalData’s latest thematic research report on Healthcare.