A 20/20 Look into Computer Vision
Often without realizing it, most humans are now regularly engaging with sophisticated computer vision technology. Things such as password authentication that used to require a password or fingerprint now need little more than a glance at your smartphone.
Forty percent of Americans are utilizing face biometrics and facial recognition technology with at least one app per day, and that adoption increases to 75% among 18 to 34-year-olds1. The global computer vision market is expected to grow at an exponential compounded annual growth rate of 19.6% by 2030 2, and other deep learning techniques have widened the scope of what’s possible with today’s computer vision technology.
From unlocking smartphones to walking through facial scanners at airports, computer vision technology is rapidly integrating into our everyday lives.
What is Computer Vision?
Computer vision is a subfield of computer science and artificial intelligence that utilizes computers and systems to gather meaningful information from images. Computer vision extrapolates data from images to make decisions. Its ultimate goal is correctly identifying objects and people to take appropriate action, such as avoiding a pedestrian on a walkway in a self-driving car or accurately identifying a smartphone user who can unlock their phone.
How Does Computer Vision Work?
Computer vision technology aims to mimic the human brain’s process of recognizing visual information. Utilizing pattern recognition, it absorbs inputs, labels them as objects, and finds patterns that produce familiar images. Computer vision works to derive meaning from images while cataloging visual data from the real world.
The History of Computer Vision
Like many fields of artificial intelligence, the first forays into computer vision occurred decades ago. In the 1960s, researchers used algorithms to process and analyze visual data. By the 1970s, this technology had become more accurate at image processing and pattern debt recognition.
Over the next several decades, scientists utilized machine learning algorithms to power most computer vision technology, culminating in one of the largest breakthroughs of the time, the Viola-Jones face detection algorithm. This algorithm is still used today as a core machine-learning object detection framework. As technology rapidly progressed in the 2000s, convolution neural networks enabled computers to detect objects and track movement with even greater accuracy.
Real-Life Applications of Computer Vision
Computer vision technology is revolutionizing many industries, from improved cancer detection to self-driving cars. Tools like facial recognition, object detection, and augmented reality offer multiple use cases for real-life applications.
Autonomous Vehicles
Tesla is a well-known example of self-driving vehicles, but Hyundai also recently invested in a deep-learning computer vision startup to apply the technology to its autonomous vehicles 3. Computer vision, a core functionality of autonomous vehicles, empowers self-driving cars to make sense of their surroundings; sensors and hardware gather billions of visual data points to create an image of what is happening outside the vehicle. From stop signs to hazardous objects on the road to pedestrians to other cars, computer vision algorithms improve the safety and efficiency of self-driving cars.
Cancer Detection
AI rapidly evolves in the healthcare industry, and cancer detection is no exception. X-ZELL is a company that uses sophisticated computer vision technology to enable same-day cancer diagnosis from imagery4. Computer vision uses advanced algorithms and machine learning to analyze medical images like X-rays, MRIs, and CT scans to identify potential signs of cancer with higher accuracy. Computer vision learns upon massive data sets, and it can accurately identify subtle patterns and features that might be difficult for humans to pick up on. In healthcare, this can improve patient outcomes, enhance treatments, and ultimately save lives.
Security in Schools and Public Areas
For increased school and public area security, Visio.ai combines edge computing with on-device machine learning and sophisticated vision systems5. From high-traffic walkways in airports to intrusion detection in universities and schools, deep learning intelligence can be merged with common surveillance cameras to use facial recognition analysis to measure emotions and detect suspicious activities. Computer vision technology offers the opportunity to improve safety throughout public areas like schools, airports, transportation systems, and more.
Manufacturing Settings
Manufacturing settings are full of opportunities for computer vision technology, from quality inspections to production monitoring to supply chain logistics. For example, in quality inspections, computer vision can automatically detect defects, scratches, and other anomalies. With radio frequency identification (RFID), computer vision technology can track products across supply lines, optimizing inventory, production schedules, and delivery. From improved supply chain logistics to ensuring consistent quality for semiconductors, computer vision supports better lighting, better product consistency, increased efficiencies, and more.
Challenges of Computer Vision
Computer vision and endpoint intelligence offer seemingly limitless opportunities for advancement in critical sectors. While safer vehicles and faster cancer diagnosis don’t seem problematic, the intelligence behind computer vision has challenges.
Privacy Concerns
Privacy and security are top concerns, like with many artificial intelligence tools. The risk for data breaches is high, and with sensitive, confidential information stored in potentially unsecured platforms, cybercriminals are highly incentivized to attack. Consumers worry about giving too much personal data to technology companies, and with cybercrime on the rise, computer vision AI tools need to ensure they’ve shored up their defenses.
High Costs
Currently, computer vision technology is not cheap to implement, and especially in more sophisticated use cases, the cost of purchasing hardware and software and performing maintenance is high. Add in large data sets that need to be cleaned, stored, and maintained; computer vision technology is extra costly. Also, the maintenance of these systems is expensive, and predictive maintenance is necessary to fix potential equipment defects before they become a bigger issue.
Lack of Trained Experts
While computer vision is rapidly evolving, few companies or individuals have vast expertise. As with any newer technology, it will take time for education and training to catch up with real-life applications adequately. Companies struggle to maintain specialized tech talent, and computer vision is no exception. Organizations also need trained experts on the differences between artificial intelligence, machine learning, and deep learning to train systems adequately.
The Future of Computer Vision
Computer vision technology is still in its infancy, but society has already seen its vast impact across manufacturing, education, security, retail, healthcare, the automotive industry, and more. There is so much opportunity for consumer computer vision technology as the desire for the Internet of Things (IoT) devices accelerates—virtual reality headsets, augmented reality smart glasses, and more. As hardware becomes more sophisticated yet affordable, computer vision wearables and smart gadgets can trickle down to the average person. Also, as generative AI and deep learning accelerate, computer vision models will have more inputs from which to learn.
How Ambiq Contributes
Computer vision technology requires an embedded chip capable of processing machine learning inferencing. For this technology to be practical on endpoint devices, it needs to perform at low power and run at maximum efficiency. Ambiq’s ultra-low power System-on-Chips (SoCs) enable endpoint devices with optimal performance and energy efficiency that can perform locally on the device.
Our friends at Northern Mechatronics (NMI) recently performed digit recognition on their flagship NM180100, which was enabled by Ambiq’s Apollo3 SoC. Numbers were identified and returned in less than 2-seconds. See it for yourself:
Sources:
1 New CyberLink Report Finds Over 131 Million Americans Use Facial Recognition Daily and Nearly Half of Them to Access Three Applications or More Each Day | November 22, 2022
2 Computer Vision Market Size, Share & Trends Analysis Report By Component, By Product Type, By Application, By Vertical (Automotive, Healthcare, Retail), By Region, And Segment Forecasts, 2023 – 2030 | 2021
3 Hyundai Invests in Deep Learning Computer Vision Startup allegro.ai | May 11, 2018
4 X-Zell | 2023
5 Top 9 Applications of AI Vision in the Education Sector | 2023
How many hours do you spend in traffic a year? Probably more than you realize. While a few minutes every day might not seem like a lot at the moment, it adds up over time. According to transportation analytics firm INRIX, the average U.S. motorist spent 51 hours in traffic in 20221. That’s more than an entire workweek. The same study found that traffic jams cost U.S. drivers more than $81 billion during 2022. Long story short, traffic is a major drain on consumers’ time and resources.
The good news? Innovations in the Internet of Things (IoT) and artificial intelligence (AI) have paved the way for new solutions that would introduce smarter traffic mitigation and make the roads safer. From accident detection to traffic light management, AI offers the transportation industry a range of opportunities to beat the traffic. In this article, we’ll outline how the intersection of AI and IoT can optimize road operations and ultimately give people their time and money back.
Tech Innovations for Traffic Optimization
While the use of AI in traffic management is still in its early stages, a range of applications already allows for real-time monitoring and predictive analytics. Below, we outline key tech innovations that are making traffic innovation possible.
Flow Sensors
Traffic management has traditionally operated on a fixed schedule. Now, AI-powered sensors can be deployed in streets to monitor demand and adapt traffic signals based on shifts to optimize the flow. These sensors ultimately reduce congestion during peak times by prioritizing high-traffic roads.
Predictive Monitors
With mounds of historical and real-time data, traffic monitors can be trained to understand traffic patterns and predict traffic flow at a given time. This can be used to forecast future conditions so that traffic personnel can better allocate resources, optimize routes, and adjust traffic signals.
Incident Monitors
AI-powered monitors can be deployed to watch for and identify traffic incidents, including accidents, speeding, or blockages. If detected, this data can be sent to the correct parties to dispatch support and resolve hold-ups more quickly.
Players Leading AI-Powered Traffic Management
So, what companies are fueling this innovation…and how? Below, we examine several leaders in AI-powered traffic management and their real-world applications.
INRIX
INRIX offers instantaneous real-time traffic conditions, pinpointing traffic speeds in different lanes and delivering accurate ETAs for any road in the world, including interstates, country roads, and intersections 2. Additionally, they leverage vast datasets to provide insights into problem areas and trends in traffic conditions that can help users get back more of their time or allow the transportation aspect of businesses to run more profitably.
Canon
Canon, one of the top manufacturers of consumer cameras, is leading the trend in support of computer vision technology that can be used in traffic mitigation with its complementary metal oxide semiconductor (CMOS) sensors3. The Canon CMOS sensors are highly sensitive and power-accurate imaging for traffic management automation. They can provide clarity in different light conditions and with varying speeds of objects, which can help governments calibrate their traffic signal coordination for ramp meters and red-light cameras.
Miovision
Miovision collects multimodal traffic data and delivers actionable, real-time insights for urban grids and corridors4. This second-by-second response to dynamic conditions eliminates driver stops, reduces wait time, and improves travel speeds. The result is less congested roads and even reduced CO2 emissions as there won’t be as many automobiles on the road.
Challenges in AI-Powered Traffic Management
With any new technology, there are challenges to widespread adoption that can only be addressed with time. Let’s examine challenges in AI-powered traffic management.
Privacy
Privacy must remain at the forefront of all traffic management technology to ensure industry and consumer buy-in. Data and imaging collected on the roads must be anonymous and protected, including compliance with global privacy regulations.
Energy Efficiency
Like many battery-powered IoT devices, traffic monitors and sensors have limitations when it comes to energy efficiency. To process large amounts of data, these devices require significant processing power and, thus, battery power. Implementing these technologies shouldn’t require several battery changes throughout the week.
Accuracy
As with any device placed outdoors, there’s a high likelihood that external factors, such as earthquakes, storms, and extreme weather conditions, can impact device accuracy and, ultimately, the intelligence of those devices. Additionally, these devices are tracking moving objects, which presents an accuracy challenge within itself. Continued development and processing power could help offset some of these constraints.
The Future of AI-Powered Traffic Management
The intersection of AI and IoT is already facilitating easier and faster coordination across the range of systems contributing to traffic operations. As adoption increases, these enhanced sensors and monitors will ultimately be fully interoperable, allowing for greater optimization down the line.
How Ambiq is Contributing
Sophisticated sensors and monitors that can reduce the dread of driving in traffic is a win for motorists, granted these devices can overcome the energy challenges associated with them. Fortunately, Ambiq, specializes in creating ultra-low power system-on-chips (SoCs) that provide long-lasting battery life and processes complex data with greater energy efficiency. Continuous monitoring is possible, and these innovations will allow cities to become more effective in managing traffic conditions for drivers.
Sources:
1 Cities where motorists lose the most time and money | August 2023
2 Real Time Traffic Data [Powered by Artificial Intelligence] | INRIX | 2023
3 Why Choose Canon CMOS Sensors | Canon USA | 2023
4 Miovision TrafficLink – Smart cities start with smart signals | 2023
Hearing loss affects roughly 430 million people globally1. The anguish someone feels from not being able to hear well has proven to have a startling effect on their mental health. Losing your sense of sound is difficult to adjust to, and many people feel more distanced or anxious as they no longer feel like they can contribute to conversations in social settings.
Hearing aids were developed to help facilitate sound, but they may also help improve some of the mental health suffering that comes with hearing loss. This blog will address the connection between hearing loss and anxiety, and other potential mental health issues that develop. It will detail how hearing aids can help provide users relief and look at real-world examples of hearing aids and the outcomes of adopting them.
How Hearing Aids Offers Relief
Like wearables and fitness tracking, hearing aids do more than just the basics to improve patients’ health. Hearing aids work by amplifying sound through a unique three-part system. They help improve hearing and speech comprehension of people who struggle with disabled hearing. With a microphone, amplifier, and receiver, hearing aids can assist with hearing loss caused by disease, aging, traumatic events, viral infections, and more.
While the obvious benefit of hearing aids is to help give patients the ability to hear more clearly, it can also enhance their overall well-being by making patients feel more connected to the world and how they think about themselves.
Better Relationships
Hearing loss comes with a higher risk of loneliness and social isolation, most likely due to the difficulty in understanding speech or frustration with the inability to communicate2. Hearing aids offer amplified sound so patients can hear and communicate with friends and loved ones. Overall, quality of life can improve, and patients might not feel as disconnected from others when they are able to communicate effectively.
Improved Confidence
Along with healthier relationships, hearing aids can improve confidence in communication and speech recognition. It might feel easier to accept social invitations or socialize in noisy environments. Individuals with hearing aids can experience improved confidence by appropriately communicating with the world around them.
Better Cognitive Health
Hearing loss is sometimes linked to mental and cognitive decline, so treating hearing loss through supportive hearing aids might help address that potentially harmful effect3. The reason for a potential cognitive decline could be that there is less brain stimulation and an increase in the brain working overtime to try and hear. By engaging in conversations, the person with hearing loss can keep their mind sharp and prevent this degradation.
Real-world Applications
Commercial hearing aids have been around since the turn of the 20th century. Still, innovation powered by the Internet of Things (IoT) and artificial intelligence (AI) offers many advancements over its predecessors in accessibility, performance, sound quality, and comfortable designs. Today, these are some of the leading trends in improving hearing aids.
Longer Lasting Battery Life
It’s necessary to provide reliable sound to users so they can feel confident about enjoying their day without worrying that their hearing aid will run out of batteries. The duration of your hearing aid will depend on the individual’s level of use, environmental sounds, battery size, and more.
Oticon is a rechargeable hearing aid built to provide superb sound quality and a full day of hearing4. It comes with a portable smart charger for those on the go and rely on maintaining high-quality hearing levels all day. A 30-minute charge can provide up to six hours of battery life and a full day’s charge after three hours.
Sound Adjustments Through AI
Hearing loss comes from the brain losing its ability to understand sounds processed through the cerebral cortex. Genesis AI from Starkey provides a hearing aid that enlists the help of AI to make over 80 million adjustments per hour without the need for user interference 5. Their patented Neuro Sound Technology mimics the cerebral cortex, and processes sound like a functioning auditory system does, and fills in the gaps from years of hearing loss. They offer a comfortable, ergonomic design and wireless connectivity that results in clear, high-quality sound.
Non-Traditional Design
Some of the barriers keeping people from adopting hearing aids are the design and stigma that an earpiece will affect the way others think about the individual. Along with creating more discreet earpieces that are harder to see, some manufacturers are turning to unique designs like smart glasses that act as hearing aids.
EssilorLuxottica, the parent company of popular glasses like Ray-Ban and Oakley, is developing hearing aids in the form of smart glasses to treat mild to moderate hearing loss6. These glasses feature built-in microphones and speakers that use algorithms to detect the presence of the person you are standing in front of and amplify their voice as they talk to you. Their goal is to provide a reliable and over-the-counter solution to give people back their hearing in a way that works for them.
The Future of Hearing Aids for Mental Health
As hearing loss becomes increasingly prevalent, there are promising developments in improving hearing aids to protect patients’ mental health. Advanced hearing aids could offer endpoint intelligence the potential for biometric feedback, sending real-time updates to care providers to alert them of patient activity and well-being for remote patient monitoring. Augmented reality and virtual reality technology can also be integrated for more immersive experiences, and artificial intelligence-powered personalized soundscapes could help reduce anxiety and depression.
How Ambiq Contributes
Ambiq helps next-generation endpoint devices powered by emerging technology such as AI become a reality. With Ambiq’s ultra-low power System-on-Chips (SoCs), smart battery-powered devices become more energy-efficient and operate at lower levels of power with higher performance. Visit Ambiq’s hearables applications page to learn more.
Sources:
1 Deafness and hearing loss | February 2, 2024
2 Hearing Loss, Loneliness, and Social Isolation: A Systematic Review | March 10, 2020
3 The Hidden Risks of Hearing Loss |2024
4 Oticon | 2024
5 Genesis AI | 2024
6 Essilorluxottica Nuanced Audio | 2023
Most people don’t consider operating a building a major contributor to climate change. However, according to many recent studies1, buildings account for 30% of global final energy consumption and 26% of global energy-related emissions. Buildings consume an absorbent amount of energy for standard operations, such as maintenance, heating, cooling, lighting, security, and appliances. Worse, they all contribute significantly to greenhouse gas emissions, even if no one is in the building.
Advancements in AI Technologies have made tremendous advances in optimizing energy and reducing waste in building operations. In the same way that AI can help us decide which products to buy with data-driven recommendations, it can also help us make better, real-time decisions about how we consume energy for the better. The potential to tackle global issues such as climate change with smarter building optimization is monumental, and innovative companies are taking notice.
How AI Can Help Build Sustainable, Smart Buildings
A United Nations report stated that today’s innovations could result in 40% embodied carbon reduction in the building sector by 20302 – and we’ve since come even further in advances. At a high level, AI can analyze energy usage patterns in a given building and identify optimization opportunities based on several factors that we’ll outline below. With the right technology, AI can actually maximize occupants’ comfort while minimizing the building’s overall carbon footprint.
Occupancy
With AI-powered sensors embedded into floors, walls, and ceilings, we can accurately determine the occupancy of a given floor or room and adjust the energy usage appropriately in real time. This may look like turning off the heat on a company’s floor because the company gave employees a week off.
Weather Patterns
Trained AI models can analyze weather patterns and forecast energy demand based on outside conditions. With this data, building systems can proactively adjust energy consumption based on predictions (e.g., reduce heating if temperatures rise), creating more efficient processes and reducing energy waste.
Daily Activities
The beauty of office buildings is that they have peak activity hours during the day. There’s an opportunity for AI to learn those hours and operate energy usage accordingly (e.g., automatically turning off the lights after traditional work hours).
Maintenance
Equipment defects can be costly in energy and spending. Predictive maintenance, powered by AI, provides the chance to identify operational anomalies and get ahead of them before they waste significant energy. This also offers the opportunity to minimize future outages and maintenance needs.
Innovations in AI Powering Smart Buildings
More and more companies, from start-ups to big names, are innovating on the ways AI can optimize energy use. Here are some of the technologies helping buildings go green:
Turntide
Turntide3 for Buildings produces electric motors with small computers that detect heating and cooling patterns to optimize usage. Its Smart Motor System™ maximizes a building’s operational efficiency and energy performance to reduce carbon contributions and costs.
JLL
JLL, an innovator in real estate, acquired technology by Envio Systems that tracks a building’s occupancy and other external factors to adjust lighting, cooling, and similar energy-related activities via sensors and applications across systems and functions4. By integrating data from building systems and providing predictive analytics, JLL offers critical insights into utilities and operations, health and comfort, and sustainability regardless of existing equipment or building size.
IBM Envizi
IBM’s Envizi Software Suite monitors energy efficiency, including how renewable energy stacks up against conventional forms of energy, HVAC performance, and overall sustainability5. The Suite then offers advanced reporting into comprehensive ESG findings that help businesses accelerate their decarbonization efforts.
BrainBox AI
BrainBox AI’s cloud-based HVAC optimization solution connects to existing HVAC systems and autonomously sends real-time optimized commands to reduce emissions, energy costs, and operational inefficiencies6. Commands are based on AI predictions of each building zone’s thermal behavior and ensure the HVAC equipment only runs when needed.
A Greener Future with AI
The Internet of Things (IoT) has opened up opportunities to leverage AI technology to optimize and streamline energy usage in buildings, reduce costs, and improve sustainability efforts for the long term. Today’s innovations have made it easier than ever to understand better where buildings stand today and how they can be outfitted to become smart buildings. Taking this next step not only contributes to a greener future but also saves costs for building stakeholders.
How Ambiq Is Contributing
Sensors and other battery-powered IoT devices used to optimize energy usage require significant energy. They require computing power at lower energy levels to facilitate AI applications and optimization that last. Ambiq’s ultra-low power system-on-chip (SoC) solutions built on the patented Subthreshold Power Optimization Technology (SPOT ®) platform dramatically reduce the power consumption of devices, facilitating energy efficiency in smart buildings. A greener future is more accessible than ever before with Ambiq.
Sources
1 Buildings – Energy System – IEA I 2023
2 2020 Global Status Report For Buildings and Construction | 2020
3 Turntide Technologies | 2023
4 JLL Smart building solutions | 2023
5 Decarbonization Software – IBM Envizi | 2023
6 Decarbonize and optimize your buildings with autonomous AI (brainboxai.com) | 2023
Augmented reality (AR) and Virtual reality (VR) technologies have always held a foothold in entertainment as they create immersive environments for users to get lost in. The healthcare industry has noted AR and VR applications and is now using the same approach to diagnose and treat mental health illnesses.
Roughly one in four American adults suffer from a mental illness, making mental health one of the fastest-growing illnesses plaguing our society1. From talk therapy to cognitive behavioral therapy, options to treat and support the symptoms of anxiety, depression, stress, and sleep disorders are abundant, with innovations constantly being released.
While healthcare organizations are already using artificial intelligence (AI), such as AI voice analysis for disease detection and remote patient monitoring, AR and VR are two AI-based tools that are increasingly prominent regarding therapeutic experiences.
In this article, we will detail how AR and VR treat symptoms of mental illness, along with real-life applications.
How AR and VR Treat Symptoms of Mental Illness
Mental illness symptoms vary widely from physical pain like headaches to mental fatigue, brain fog, disordered sleeping, and more. AR and VR work to reduce these symptoms and provide effective therapeutic treatments for the overall illness.
Exposure therapy
In cases of post-traumatic stress disorder (PTSD), virtual reality can create simulated environments to help patients increase exposure to phobias or traumatic environments. Exposure therapy works to gradually introduce and expose an individual to the things they are afraid of with the eventual goal of reducing that fear. Through this immersive exposure, VR and AR projects visual and auditory environments to encourage patients that they’re in a safe place while exposing them to their fear. Research showed a 66% and 90% success rate when virtual reality therapy was combined with cognitive behavioral therapy2.
Mindfulness
For social anxiety or panic disorders, VR can provide mindful and meditative stress reduction. Through guided visualization, calming music, or guided breathwork, patients experiencing an anxiety attack or panic attack can slow their breathing, calm their heart rate, and work themselves down.
Pain Reduction
VR also immerses patients in different environments to distract them from chronic pain. This reduction in physical pain can be morale-boosting for patients suffering from serious mental illnesses and allow them to continue other treatments. For patients dealing with pain, traveling to smart buildings equipped with VR tools might not be possible, so at-home technology is best.
Real-Life Applications
AI-enhanced wearables like fitness trackers have gained significant, steady adoption for the last few years, and VR and AR technology could be the next quickly adopted health tool. Take a look at how companies are developing and launching revolutionary products.
Apple’s Vision Pro Headset
As one of the forefront pioneers in VR, Apple has long been testing and developing different virtual reality headsets. One of their rumored mental health headsets, the Vision Pro, is thought to detect different levels of anxiety, depression, PTSD, and other types of stress 3.
XR Virtual Reality Therapy
In the comfort of your own home, XR Health promises drug-free, personalized mental health therapy through VR. Leveraging a cutting-edge headset, therapists using video and talk therapy will deliver through confronting experiences using different modalities such as cognitive behavioral therapy, acceptance and commitment, therapy, and psychodynamic therapies 4.
PsyTech VR
Ideal for mindfulness practices, meditations, and an overall reduction in anxiety and stress, psyTech VR also uses cutting-edge AR and VR technology to build therapeutic immersive experiences. It’s designed to provide interventions for anxiety disorders, phobias, and PTSD 5.
Challenges of AR and VR for Mental Health
The future applications of AR and VR in healthcare and mental health are exciting, but like many new AI technologies, there are concerns around ethical considerations, safety, costs, and privacy.
Privacy
As consumers increasingly care about data, privacy, and security, it’s important that AR and VR technologies for mental health protect sensitive healthcare data. Also, as data is aggregated at scale to identify trends and patterns, anonymity should be considered to protect the identity of individuals participating in these newer technologies.
Safety
Like any new therapy treatment, thorough research must be done before releasing AR and VR tools to the public for mental health treatment. Mental health patients often experience up and down episodes; AR and VR mustn’t be more harmful than helpful. All exposure should be thoroughly evaluated and done under the care of trained professionals.
Costs
AR and VR technology for mental health will undoubtedly be expensive, such as Apple’s estimated $3,500 headset. This significant barrier to entry could create socioeconomic disparities and biased data sets, leaving some patients without critical treatments in the future. However, as more AR and VR technologies are released, prices will go down, and technology will be more affordable for the average consumer.
The Future of AR/VR for Mental Health
Before a mental health issue can even start to be treated, it has to be formally diagnosed, yet mental health issues are often called the “silent disease.” The vast majority of mental illnesses go untreated, as proper diagnosis can be difficult to get. In the future, research is hopeful that AR and VR can also be used for faster, more accurate patient diagnosis before treatment starts. AI is already showing its effectiveness in healthcare diagnosis.
Also, as more companies develop AR and VR technology, cost barriers will reduce, and practitioners will feel more comfortable recommending these innovative technologies in conjunction with other treatments. New enhancements, such as voice commands or wearable solutions, will make it even easier for all patients to use AR and VR technology in a way that makes sense for them.
How Ambiq Contributes
As more and more people suffer from depression, sleep disorders, and anxiety, innovative technologies will create a better quality of life, reduce pain and suffering, and offer new hope for patients. Ambiq’s ultra-low power System-on-Chips (SoCs) allow battery-powered, energy-efficient IoT devices to operate at optimal battery life and performance, extending their usage. Already, Ambiq has enabled over 230 million endpoint devices capable of giving users active control over their health. These battery-powered endpoint devices could last days, weeks, and even months on a single charge. Learn more about different applications Ambiq can help here.
Sources
1 Mental Health Disorder Statistics | 2023
2 Virtual reality exposure therapy for post-traumatic stress disorder (PTSD): a meta-analysis | August 19, 2019
3 Apple Is Considering Treating Mental Health With $3,500 Vision Pro Augmented Reality Headset, Report Says | October 25, 2023
4 XRHealth | 2023
5 PsyTech | 2023