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Virtual Dressing Room to Increase Sales During COVID-19

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COVID-19 has changed the way people worldwide behave daily, and nowhere is this felt more keenly than in bricks-and-mortar retail. For decades, retailers have been trying various customer engagement strategies to bring people in and let them browse samples and linger to their heart’s content. But the pandemic has made nearly all those experiences risky and undesirable. Personal safety wins out of the desire to try on clothing, jewelry, makeup, and other wearable products.

Increasingly, with COVID-19, retail businesses are turning to virtual dressing rooms as an alternative for their customers.

A virtual dressing room allows a user to upload a video of themselves and then renders an Augmented Reality image of the person modeling their perspective items.

Certain sectors of the retail industry have already been embracing Augmented Reality dressing rooms, most notably the cosmetics and jewelry industries. But many more retail sectors are adopting virtual dressing rooms, and the pandemic has accelerated the process greatly.

Artificial Intelligence in Virtual Dressing Rooms

Like many digital business applications, virtual dressing rooms are driven by recent advances in technology, from AR and VR to Artificial Intelligence. In fact, technology is advancing so quickly that one of the barriers to adoption is simply that many people don’t realize how good virtual dressing rooms can be if they’ve never tried one before.

But more and more consumers are trying them and generally like their experiences. As a result, the virtual dressing room market looks to have staying power even after COVID-19 becomes less of a clear-and-present threat. By 2027, the virtual dressing market is projected to be a $10 billion industry.

Virtual Dressing Rooms Becoming Mainstream

For several years, certain retailers have toyed with virtual dressing room solutions and related apps, although frequently, these have been limited in scope and with mixed results. However, the combination of technology-driven increases in quality and the pandemic driving customers away from physical store locations has led to a significant uptick in virtual dressing room adoption.

Global retail giants like Macy’s and Adidas have led the way in virtual dressing room implementation, with many smaller retailers following suit.

Amazon is also involved in virtual dressing room development, as its online retail model continues to gobble up market share.

For many retailers, embracing virtual dressing rooms is a necessity right now. They’ve blocked off their physical dressing rooms and forbade customers to handle merchandise like they once did out of fears that these behaviors will spread COVID-19. And many customers simply aren’t coming out to stores regardless.

In today’s retail landscape, the virtual dressing room represents an opportunity to recapture some of the lost business that’s crushing most retailers’ profit margins.

How Virtual Dressing Rooms Work

From a technical standpoint, the two broad technologies pivotal to the virtual dressing room are Augmented Reality and Artificial Intelligence. These are huge domains that stretch far beyond the scope of a virtual dressing room solution, and it’s useful to understand just how these technologies apply here.

The virtual dressing room process begins with video capture of the person who will be trying on the virtual item. Often, the recording device is a mobile smartphone. A smartphone is an ideal vehicle because it contains both the camera to capture video and a screen to display the AR image of the person/body part with the wearable item modeled.

The video is parsed by human pose estimation algorithms that identify a range of key points or locators on the human body, which allow the application to understand the contours, size, and spatial location of the person. Often, AI deep learning routines are used to make these determinations. The accuracy of these AI-driven processes can be far superior to a human programmed process, allowing for far greater fidelity in virtual dressing room development.

Once the body’s dimension and location are fixed, the application then appends the item of clothing or accessory to the image on the screen, allowing the user to model that item virtually in a 3-D, photorealistic display.

Pros and Cons of Virtual Dressing Rooms

Like any business or technological innovation, virtual dressing rooms have their advantages and disadvantages when compared to the traditional model.

It’s important to grasp that virtual dressing room technology continues to develop and evolve, and as the process continues the industry will change. Former drawbacks may be mitigated, and advantages may heighten as supporting technology improves.

But even as some problems may fade in relevance, others may develop. The following pros and cons represent a snapshot of the short — and medium-term projections for the virtual dressing room landscape.

Benefits of Virtual Dressing Rooms

The most obvious benefit to a virtual dressing room is giving the customer the ability to sample and model products remotely. But for this to be worthwhile, the AR rendering has to be realistic enough to be useful. If a user doesn’t feel comfortable with the image they’re seeing, a virtual dressing room is a failure.

Fortunately, the science of capturing the human body and rendering it in a virtual environment is one that engineers and developers are devoting massive amounts of time and resources to. While virtual dressing rooms aren’t the most important or lucrative application of these processes, we reap the rewards of that development and innovation.

The real game-changer is the implementation of artificial intelligence in the video capture and rendering process. Deep learning algorithms can estimate and display the user’s full body, face, head, hands, feet, or any other specific body area with rapidly increasing clarity and accuracy.

This is taking us toward the point where the average shopper regards a virtual dressing room as roughly equivalent in quality to the physical experience. Once we’ve achieved that benchmark, the traditional dressing room is nearly entirely obsolete.

Potential Drawbacks to Virtual Dressing Rooms

Many of the current drawbacks to virtual dressing rooms are temporary issues likely to be addressed in the coming years.

People are excellent judges of the human form, especially their own. If a virtual dressing room image has minor imperfections, this can detract from the immersive experience and leave a customer uncertain about whether they can trust what they’ve seen.

In some cases, virtual dressing room solutions are close but to quite up to the highest standard, meaning that customers would prefer traditional ones if given a choice.

During this pandemic, virtual dressing rooms receive a bump simply by being the only realistic option for people looking to minimize their COVID-19 risk. However, at some point in the next year or so, countries will begin to get the pandemic under control via the release of vaccines.

At this point, the question is whether virtual dressing rooms will offer a seamless and accurate experience, one good enough to keep people using them when life can return more to normal. This is where the industry will be continuing to focus.

One final potential drawback worth mentioning is that virtual dressing rooms can pose a data security issue. The process captures users’ face and body data and background images from wherever the user is filming.

It would be possible for a developer to engineer a virtual dressing room solution that pulls biometrical data and geolocation data from its users. That data could then be used to create profiles of those users, allowing third parties to use this info in a variety of ways.

This particular concern is a universal one in our increasingly digital world, far from unique to virtual dressing rooms. But the video data captured here is particularly intimate, and users may have special concerns.

In 2020, the advent of COVID-19 has reshaped the retail landscape in a seismic way. Consumers avoid stores and businesses find the physical process of trying on and sampling wearable items riskier. In this environment, virtual dressing rooms are being adopted more and more.

But the virtual dressing room concept is more than just a quick-fix workaround for the pandemic. Hand-in-hand with the rise of online retail, virtual dressing rooms have the potential to supplant the traditional dressing room. As AI technology matures, a larger group of consumers will likely find themselves using virtual dressing rooms even afterlife returns more to normal.

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How Artificial Intelligence Innovations are Used in the Fitness Industry

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It’s hard to find an industry that isn’t in the process of being transformed by digital technologies, and the world of fitness is no exception. While people have been working out or exercising for decades, if not centuries, how they’re doing so is rapidly evolving. Here is how artificial intelligence innovations are used in the fitness industry.

Technologies like digital wearables enhanced with artificial intelligence are a natural fit for the fitness industry. Some of these products and solutions have already seen increasingly widespread adoption, while others are only beginning to penetrate consumer consciousness.

Finally, coming full circle into the field of fitness-related is AI. Advances in AI allow for more responsive, customizable, and immersive digital fitness assistants and helpers with each successive year.

AI-Based Personal Trainers

One aspect of fitness that has consistently brought people out to their local gym has been a lack of expertise. If you want a sustainable and effective workout routine, you need to consult with someone who knows what works and what doesn’t. And until recently, that means working with a trainer or other fitness professional.

Neural Networks and Trainers

But by harnessing the power of neural networks and AI, digital personal trainers can fill in for that role, tailoring and overseeing workouts from the comfort of users’ homes. The AI-based personal trainers use various AI algorithms to simulate or even surpass the know-how of a human trainer.

The AI-based personal trainers see a noticeable surge in popularity due to the global COVID-19 pandemic. As people continue to deal with local lockdowns and are reluctant to spend more time away from home than needed, digital personal trainers fill a void when it comes to fitness guidance.

AI-based personal trainer offerings attack the concept in a variety of ways. Still, it’s worth taking a look at a couple in particular as representatives of some of the field’s possibilities.

Apps Leveraging AI Processes

The first we’ll look at is the Freeletics app, which leverages a series of AI processes to create a customized workout and then maintains and modifies it to optimize user preferences and development.

The Freeletics app begins by collecting a small amount of personal data. It then cross-references with a massive database of other users and workouts to create a suggested starting routine.

The app will then track the user’s progress and accept feedback to continue to sculpt their workout to their satisfaction. Whether it be general fitness, targeting individual muscle groups or areas of the body, weight loss, or other fitness goals, Freeletics uses machine learning.

With machine learning, the user gives feedback that will zero in on a routine a user can stick with over the long term.

Other apps use human pose estimation that detects and analyzes human posture during physical activities.

Data Gathered Through Apps

The Zenia app gathers data through the mobile camera, which is angled to capture the user’s workout. Under the hood, Zenia uses a sequence of neural networks and a database of hundreds of thousands of captured yoga poses and images.

The database allows the app to identify correct and incorrect postures, giving feedback on users’ form and progress.

AI  — Personal Coach

Creating AI personal coach apps, developers consult with fitness experts in creating the tools. This ensures that these apps have a foundation rooted in expertise within the fitness world and the power of machine learning.

The fusion of the two allows AI-based personal trainers to offer a compelling alternative to the gym, which an increasing number of fitness seekers prefer.

Smart Clothes and Wearables

An even more intimate application of AI tech in the realm of fitness is smart clothes and wearable devices. These products can monitor exercise or athletic endeavors in real-time through readings of biometric measures.

The motion-capture gathers data and offers feedback and guidance either after the fact or when the user is still engaged in their activity.

Smart Wearables

Asensei is a smart apparel developer offering a suite of shirts and pants capable of tracking users’ movements engaged in body movement exercises like lunges, squats, and similar routines.

The Asensei smart clothes use motion capture and AI technology to compare the user’s angles and range of motion to acceptable norms of exercise form and can correct users in real-time to create good exercise habits.

Sensoria offers a similar AI-based wearable system, this one specially tailored to jogging and running. The Sensoria platform gathers data from smart garments (whether Sensoria’s own or other IoT-enabled garments).

The data measures a range of motions and biometrics. This includes heart rate, rate of foot landing and cadence, and impact forces while running.

The Sensoria analysis not only offers suggestions for optimization and improvements for a workout routine but can monitor and spot potential injuries-in-waiting and identify weak points in the kinetic chain.

The Sensoria system is designed with an eye on both fitness and wellness for users engaged in an active lifestyle.

Wearables for Exercise Tech

Nadi is a smart clothes creator-focused specifically on yoga, using many of the same AI and body capture technologies to produce smart yoga pants.

These leggings connect wirelessly to an app for mobile devices, and the mobile app offers a yoga tutorial working through a predetermined routine.

Meanwhile, the leggings themselves utilize a series of gentle vibrations to offer guidance on which parts of the body should be focused on at each step of the routine.

Real-Time Data

These and other smart clothing and wearable manufacturers are starting to scratch the surface of the power AI has when it can be fed real-time data on granular body movements.

Analyzing and offering instruction at that level of precision allows for a greater level of control of a workout than we’ve ever seen before.

AI-Driven Diet Planning

Long before AI was a viable commercial technology, digital diet planning was a massive part of the fitness market.

Tracking calories and meals online and on mobile devices has been popular for more than a decade. With a profusion of competing apps and websites offering services of highly variable quality.

Limitations of the Users

The recurring problem or limitation of many of these solutions is the amount of work they ask the user to do and the expertise they expect from their users. AI has the potential to fix many of these issues.

AI Helps in the Specifics

One area where AI can help is in the easy identification of foods for meal logging.

Asking users to painstakingly enter each of their meals and snacks is far more time consuming than asking them to snap a simple photo.

And apps like Calorie Mama offer the ability to calculate the calories within a dish or meal through a picture.

If users can log their calories with a minimum effort, motivation to stick with a diet plan increases significantly.


Meanwhile, apps like FitGenie and Neutrino leverage AI-based analytics to tailor meal plans and caloric targets for individual users’ requirements.

By gathering user info up-front and as time goes on, these apps can tap into huge databases of foods, metabolic and physiognomic ranges.

All of the data measurements can chart fitness goals and create recommendations customized to an exacting standard.

Wellness and Fitness

As time goes on, we realize that wellness and fitness are often an intensely personal journey and that one size very much doesn’t fit all.

AI has the potential to discover what works for any given person, even if that diet or routine is significantly different than what works for anyone else.

The Diet Industry

For decades, the diet planning industry has been stunted by untapped promise, often delivering results to a small subset of people while failing the majority of users.

AI-based solutions may be the key that unlocks a wider-spread success rate.

Artificial Intelligence innovations are transforming nearly every industry, and market in existence to one extent or another, or else will be in the near future. It’s already starting to change the fitness industry and the way people exercise and take care of their bodies.


As we grapple with a world forever changed by the COVID-19 pandemic, the possibilities for major changes in consumer behavior are as great as they’ve ever been.

Whether it be smart apparel, AI personal trainers, or AI-driven diet planners, AI technology is making fast inroads in the fitness industry.

Integrating and expanding on the current offerings is the surest route to becoming a leader in the near future.

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The Value of AI-Based Visual Inspection in 2020

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For over a decade, manufacturers have turned to automated solutions to improve their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is especially true when it comes to visual inspection. The use of AI-based visual inspection technology is transforming manufacturing’s ability to improve business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is built with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a range of environments, making it useful across a multitude of industries. It has unlimited potential and can be developed rapidly to meet a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do the same — but with greater efficiency. Like a human eye, AI-based vision systems capture an image and send it to a central “brainâ€� for processing.

Like a human brain, an AI “brain� makes detailed meaning from the image by contrasting it against its existing knowledge.

AI-based vision systems are made of two integrated components. A sensing device acts as an “eye,� while a deep learning algorithm acts as a “brain.� The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain� stores greater amounts of information.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex visual perception tasks.

AI-based vision systems can search images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (mobidev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation

Decades-old automated systems depend on defect libraries, lists of exceptions and complicated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning do not require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems might be constructed by several quality engineers and a dataset of training images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and Quality Control

Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics that can be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location

In addition, inspection images and results can also be tracked and documented. These initiatives prevent future failure, which saves time and additional production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction

AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only able to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is an issue. For these reasons, AI-based vision inspections are more cost-effective than manual labor.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to meet healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and can provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral pneumonia and images of coronavirus.

Fujitsu Laboratories implemented an Image Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across the entirety of the company’s production sites.

Airbus introduced an automated, drone-based aircraft inspection system in 2018. The system has improved the quality of inspections and reduced aircraft downtime.

GlobalFoundries is a leader in semiconductor manufacturing. The company designed a visual inspection system that detects defects in a scanning electron microscope (SEM) images. The system detects defects in a wafer map which then helps to determine the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider how to use this technology to get advantages.

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