The first automatic exercise tracker, using the power of artificial intelligence and wearables to automatically track your entire workout at the gym, hands free!

Train Fitness

Part II – Run Forrest Run Let me begin by breaking the suspense, I have never run a marathon or a half marathon till date. So sadly, no victorious selfies on Facebook. For that matter, I haven’t even run an organized 5k or 10k. However, my fitness journey did begin with running, a whole lot of running. My fitness train has had two major stations till date. It was a superfast express from 127 to 114kg. Then the gradual slide from 114kg to 103kg. Finally broke the 100kg barrier to my current form. Note that, I am intentionally calling it ‘current form’. Armed with very little foresight, I am more the ‘Find the tiger first and then look for a weapon’ kind. It doesn’t work for everyone but that is the only way I know how to make it work. Retrospective introspection of my fitness journey shows a mixed bag of both, good and bad decisions. But wisdom gleaned over time taught me that avoiding bad decisions is not the goal but continuously learning and fine tuning your approach is. I will try my best to convey all that I have learnt over time. Before I start, fitness, much like food, offers a wonderful array of options. Choose a cuisine you understand and, more importantly, one that you are comfortable with. You don’t want to end up with Pasta and a on the side. Take my word, though individually brilliant, it is not a good combination. What you are about to read is what worked for me and not a secret recipe. avial Lesson 1: A little exercise is better than no exercise The first mistake I made was budgeting an hour a day for exercise. For that meant, many a day when I woke up late I ended up using the ‘ I don’t have enough time’ card. I am a lazy person. An extremely lazy person. Each time before I had succumbed to my laziness, but this time around I was desperate and determined. I decided that I am not skipping my morning exercise for anything. Even if it is only a 15-minute walk. Soon I realized that it was only the initial inertia that I had to overcome. Most of the days when I had convinced myself that it will be only a 15 minute walk I would end up clocking close to an hour working out. All without compromising any other aspect of my life. I was still spending time at home. I was still reaching office at the same time. I only had to get started. Once it was routine, I made it work. Lesson 2: Log your progress Be it food or my workouts, I started logging them from day one. It has evolved from a scrappy notebook to a digital log but its purpose remains the same. For me, it acts as a motivator and a gauge of progress. Everyday religiously I used to log my meals, weight, the distance I ran and my timing. Any day that I didn’t work out was left blank. Another wonderful attribute of mine, along with laziness, is being obdurate. Usually my pig-headedness is channeled towards awesome things like which side of the sofa I get to sit on or where I leave my towel after bath. I decided to channel that into something a tad more useful. It took sometime but all those blank entries started bothering me. Similarly, with respect to food, the application used to budget for the amount of calories I can consume based on my goals. Any day I indulged was marked in red. I can’t lose to a machine now, can I? Lesson 3: As they say for everything these days, ‘Gamify’ the experience A little into the journey I realized that I needed to make it fun to ensure consistency. The first challenge that I set for myself was to better the distance I had covered in the previous week. Of course, I failed at times. But the feeling of failure spurs you on. It is no longer just a missed workout but a defeat. Soon I started competing with others. The ‘Nike run’ application has a feature where you can create groups. At the end of each week it shows who covered the most distance that week. The same was also aggregated for a month. That meant a failure in the first week usually ended up with me working extra hard for the rest of the weeks to make up. Fortunately, in my group, there was a marathon runner who used to cover 160–180 kilometers per month. That is nearly 25km of running per week. It became an obsession for me to beat him. It took some time coming but the feeling of elation, when I did best him, made it all worth it. And by that time running had become a habit. Lesson 4: Don’t fall for the ‘Oh, you have lost so much weight’ remark The one comment which often sidelined me from my fitness schedule. I feel it is the worst compliment to a person who has lost weight. I used to feel like Superman, like the one with a capital S on his tee, whenever someone observed this about me. This feeling accumulated overtime is dangerous . It always ended with reckless indulgence. Now I have learnt to keep reminding myself of the end goal. The one trick that has worked for me is wearing something that is one size too small for me. A day spent in a shirt which I have to scrape off myself at the end of the day negated any inclination towards recklessness. Don’t get me wrong here. My intent is not to stop feeling good about myself but to not lose track of the goal. Lesson 5: Invest emotionally in your workout regime I don’t know if this is something is applicable to all but it did wonders for me. The one thing I did right at the beginning was to dedicate this whole crusade to my dad. It ensured my intrinsic motivation stayed up. It gave me the push on days when I felt lazy. It allowed me to stay determined. Additionally, the fact that I haven’t done anything spectacular in my life helped. All this fat was my chance to inspire. It was a chance for me to make my dad proud and show my son that anything is possible. I am sure you can find ways to stay emotionally invested in your fitness. Probably for your children or parents or spouse(s). Alright, that was how the runner in me originated. But soon I realized that fitness goals change. It was not about running anymore. It was not even about losing weight anymore. What did it change to? More importantly why? I will end this post with an imaginary conversation between the runner version of myself, referred to as ‘Runner’ and the present day me – ‘Trainer’. Trainer: “Oh, you have lost so much weight!” Runner: “Yes. I run bro” Trainer: “Only running? What about resistance training? Do you have a schedule for your muscle. groups?” Runner: “I lift weights but no schedule, that is for body builders. I just want to lose weight.” Trainer: “Mmmm. Alright what about your diet?” Runner: “Of course modified my diet bro. I have given up white rice, sweets, chocolates, ice cream and in general anything that gives me joy in life” Trainer: “How do you manage to sustain it then?” Runner: “Cheat days bro. I kill it on cheat days.” Trainer: “Alright, do you account for your Macros?” Runner: “Say what?”

How I lost my X factor – My journey from XXL to L(Part II)

With every holiday season comes a glut of, well, gluttony as we strive to make merry amid calorie-laden food and drink for weeks on end.  Then there’s the predictable diet and fitness-focused New Year resolutions that, however genuinely and well-intended at the onset, can be easy to make—but even easier to break. With this understanding, throngs of tech and online companies are finding innovative ways to keep people engaged and motivated throughout the holiday season and beyond as they endeavor to get fit and healthy...and stay that way. However, with such an extensive and comprehensive field of modern-day health-tech options, it can be overwhelming to hone in on which are worthy of working into your daily lifestyle.  With that in mind, here are six savvy digital diet and fitness solutions that are sure to help you better survive the holiday revelry, shed those stubborn pounds, tone those muscles and increase your endurance easier, more effectively and with increased sustainability. Tonal Intelligent Home Gym Created by former Apple, Nest and GoPro engineers and designers, Tonal is the world’s first machine learning-powered strength training system. This intelligent home gym pairs on-demand workouts and personal coaching with a first-of-its-kind, at-home, wall-mounted fitness system that you really do need to see to fully understand and appreciate. So, hitting the website to watch the intro video is worthwhile. It’s like having a personal trainer plus an entire gym in the convenience of your home—but one that is super-sleek, taking up very little space and boasting a “wow” factor unlike any other all-in-one I’ve seen. It features digital weights to help you achieve your goal, whatever that may be: to lose weight, gain strength, build muscle, boost energy, improve your performance or maintain your fitness. As a virtual personal trainer, expert-led programs and full body workouts are availed on this device’s digital display. Virtual coaches will guide you step by step. Tonal supports hundreds of moves and 200 pounds of resistance so you can skip the gym without compromising your workout. Additionally, its artificial intelligence capabilities take the guesswork out of strength training. This device actually learns from your body and adapts guidance in real-time, so you see results faster. Service-wise, similar to Spotify music’s family plan, with Tonal, you can have unlimited users per household so everyone from mom and dad, to teens and even visiting friends and grandparents can strength train. BINGE Networks’ Fitness Channel BINGE is a broadcasting platform offering a convenient way to find all of a variety of fitness classes all in one place. They feature an array of fitness, health and life coaching shows allowing you to do yoga, pilates, weight training and even get your mind in shape. With BINGE Networks, you can work out where and when you want with expert-driven programs that you can do at home, and that also travel with you. Users can stream the content from a SmartTV, tablet, smartphone or laptop from over 90 premium platforms like Roku, Apple TV, Amazon Fire TV, Google Play, Sony, Samsung, Sharp, Tivo, Philips, Sanyo, Panasonic, JVC and Toshiba. The first week of access to the BINGE Fitness platform is free using code “FIT” and then access to the service and its content thereafter costs $24.95 per year. Amazfit GTS SmartWatch by Huami Huami recently announced the launch of its newest smartwatch, the Amazfit GTS. This device is powered by Huami's Bio Tracker optical sensor, which enables 24 hours of uninterrupted, high-precision heart rate monitoring with detailed data and abnormal value detection warnings. In both the watch and the app, the data is clearly displayed for an easy understanding of the heart rate data patterns. With the app, users can also customize the classification of their heart rate and receive an alarm when the bpm values are out of range. All recordings are saved and can be seen in the app and can be shared. The device also integrates multiple health management features such as sedentary reminder, calorie consumption, sleep analysis and more. All these features, if used as an integral way to manage the user's health, can surely help people to maintain a healthier lifestyle and improve their overall well-being. With the six-axis acceleration sensor, the Amazfit GTS becomes an ideal companion for sports and workout. It comes with 12 different sports modes including outdoor running, treadmill, walking, outdoor/indoor cycling, elliptical trainer, pool/open water swimming, mountaineering, trail running, skiing and exercising. Once the activity is finished, the smartwatch displays all the data collected like pace, calories, BPM range, altitude, time and much more. This new model comes in six different colors and with a 50 meter depth water resistance. Its techy and modern looking style comes with a vivid silicon strap, a thin 9.4 millimeter body for maximum comfort and the battery can last up to roughly 14 days. It also allows users to stay connected thanks to the mobile smart notifications, find-my-phone function, music control via Bluetooth and weather forecast. Methodology Healthy Food Delivery Methodology is a web-based, wellness-focused subscription service that offers ready-to-eat meals, with menus crafted by a team of chefs and nutritionists dedicated to maximizing health benefits while also making the food delicious. So, this is a great Internet-driven solution for those who find themselves too busy to make good, wholesome meals at home—whether every day or intermittently, as needed. Methodology distinguishes itself from other meal services on the market based on five parameters for using food to improve beauty, strength and well-being: Eat food you love; maximize nutrient density; avoid refined and processed ingredients; control your portions; and be consistent. Methodology addresses all five of these concerns and endeavors to make clear that it’s “no ordinary meal delivery service.” It offers clean, ultra-nourishing, personalized meals for people who both love food and want to achieve optimal wellness. Of course, pricing reflects the premium nature of this food service option with meals starting at $16.99 each for lunch and dinner and $7.99 for breakfast. A small price to pay for truly delicious, real, whole, ultra-nourishing superfood meals that are ready-to-eat and can be reheated in a microwave or on a stove top in less than five minutes—all prepared and also refined sugar, gluten and processed ingredient-free. Grass-fed ghee is the only forms of dairy they currently use. In fact, the company can aptly accommodate specialized diets like Keto, Paleo, Mediterranean, Pescatarian or Vegan. You can visit the company’s website to review online and also kick-start the process by specifying your food preferences. sample menus HealthyWage App Weight-Loss Cash Incentives No matter what kind of fitness or diet regime—or technology facilitators—you choose, the HealthyWage app actually PAYS you for losing weight! With the HealthyWage app, you determine how much weight you want to lose, how long you want to take to lose it and how much you want to wager each month. If you successfully reach your goal by the end date of your challenge, you win your prize. The average HealthyWager prize is over $1,200! You can also join team, jackpot and step challenges through the app. And, this gamification approach is well-proven. Multiple studies show that monetary incentives serve to enhance the effectiveness of, and duly complement, weight-loss programs of any and all sorts—especially when paid out quickly like HealthyWage’s various programs. In fact, according to a company spokesperson, the average HealthyWager participant “more than doubles their investment if they are successful at achieving their goal,” so the financial upside potential is impressive. In 2018, alone, program participants collectively lost over 1,000,000 pounds and gained $13,000,000 in kind. Since the company’s inception, over $20,000,000 has been paid to HealthyWager winners. H20N Smart Scale by InBody InBody has developed the H20N Smart Scale that measures your whole body, providing accurate and personalized results in the comfort of your home. The product provides essential health metrics including body fat percentage, BMI, skeletal muscle mass, and weight. Test data automatically uploads to a mobile app that allows you to record test history and track trends. Smart scales only have electrodes on the footplate, so they can only measure your bottom half and have to estimate the upper half. The InBody H20N comes with electrodes as well as electrodes, so your entire body composition is measured, not estimated. handlebar footplate Testing your body composition on the InBody H20N is quick and easy. Just step on the scale, pick up the handle and get your results in 10 seconds. All your data is automatically recorded and synched to an app installed directly on your smartphone. After every test, your results are recorded and organized. See your test history, spot trends and share your results with your personal trainer or doctor. Make adjustments based on accurate data, so that you are consistently trending toward the right direction, "During our R&D, we found that the current smart scale market focused heavily on the number features offered rather than the quality of the outputs,” said Joseph Han, Director of Business Development. “We made the conscious decision to scale back on the number of features and focus on delivering the most accurate body composition test you can take at home.” As the Executive Editor and Producer of “The Luxe List,” Merilee Kern is an internationally-regarded brand analyst, strategist and futurist. As a wellness industry veteran and health advocate, she’s also author of the award-winning, illustrated fictional children’s book, “Making Healthy Choices – A Story to Inspire Fit, Weight-Wise Kids” (Amazon). Merilee spotlights noteworthy industry innovators, change makers, movers and shakers: experts, brands, products, services, destinations and events. Connect with her at www.TheLuxeList.com Instagram www.Instagram.com/LuxeListReviews Twitter www.Twitter.com/LuxeListEditor Facebook www.Facebook.com/TheLuxeList LinkedIN www.LinkedIn.com/in/MerileeKern Some or all of the accommodations(s), experience(s), item(s) and/or service(s) detailed above may have been provided or arranged at no cost to accommodate this review, but all opinions expressed are entirely those of Merilee Kern and have not been influenced in any way. Photo courtesy of www.healthywage.com

6 Smart Digital Health & Fitness Solutions

The Costs and Considerations for Making a Fitness App

Washboard abs, buff biceps, or a curvy figure — a body shape that most of us probably desire. However, let's be honest: We're too lazy to get it. Hitting the gym is a great choice to getting ourselves in shape, but paying attention to what we eat and how much we eat requires not only great persistence, but also knowledge about what goes in food. The food recognition function can be integrated into fitness apps, letting users use their phone's camera to capture food and displaying on-screen details about the calories, nutrients, and other bits and pieces of the food in question. This helps health fanatics keep track of what they eat on a meal-by-meal basis. The GIF below shows the food recognition function in action. Technical Principles This fitness assistant is made possible thanks to the image classification technology which is a widely-adopted basic branch of the field. Traditionally, image classification works by initially pre-processing images, extracting their features, and developing a classifier. The second part of the process entails a huge amount of manual labor, meaning such a process can merely classify images with limited information. Forget about the images having lists of details. AI Luckily, in recent years, image classification has developed considerably with the help of deep learning. This method adopts a specific inference framework and the neural network to classify and tag elements in images, to better determine the image themes and scenarios. Image classification from is one service that adopts such a method. It works by: detecting the input image in static image mode or camera stream mode → analyzing the image by using the on-device or on-cloud algorithm model → returning the image category (for example, plant, furniture, or mobile phone) and its corresponding confidence. HMS Core ML Kit The figure below illustrates the whole procedure. Advantages of ML Kit's Image Classification This service is built upon deep learning. It recognizes image content (such as objects, scenes, behavior, and more) and returns their corresponding tag information. It is able to provide accuracy, speed, and more by utilizing: : The service is equipped with a higher-performance image-tagging model and a better knowledge transfer capability, as well as a regularly refined deep neural network topology, to boost accuracy by 38%. Transfer learning algorithm : The service optimizes the semantic analysis model and analyzes images semantically. It can automatically deduce the image concepts and tags, and supports up to 23,000 tags. Semantic network WordNet : Huawei GPU cloud services increase the cache bandwidth by 2 times and the bit width by 8 times, which are vastly superior to the predecessor. These improvements mean that image classification requires only 100 milliseconds to recognize an image. Acceleration based on Huawei GPU cloud services Sound tempting, right? Here's something even better if you want to use the image classification service from ML Kit for your fitness app: You can either directly use the classification categories offered by the service, or customize your image classification model. You can then train your model with the images collected for different foods, and import their tag data into your app to build up a huge database of food calorie details. When your user uses the app, the depth of field (DoF) camera on their device (a Huawei phone, for example) measures the distance between the device and food to estimate the size and weight of the food. Your app then matches the estimation with the information in its database, to break down the food's calories. In addition to fitness management, ML Kit's image classification can also be used in a range of other scenarios, for example, image gallery management, product image classification for an e-commerce app, and more. All these can be realized with the image classification categories of the mentioned image classification service. I have integrated it into my app, so what are you waiting for?

Using Image Classification for Fitness and Dieting Apps

Kristy Cheung is a fencer from Hong Kong. She competes with the I-Fencing Alliance Club. During training, Kristy wears a special fencing suit that is equipped with lightweight sensors recording her movements. All the data is aggregated and analyzed to generate insights into her strength, flexibility, stamina, and balance. Kristy believes that using IoT in sports opens new opportunities for improvement. Here is what she says: “It’s interesting to quickly see where I am going right or wrong. I can immediately address weaknesses I didn’t even know that I had. I’m feeling more confident about performing my best.” Having this type of knowledge is rather helpful for every athlete, competing and recovering. Consequently, the sports sector leverages to give athletes innovative ways to analyze and enhance their performance. But this is not the only contribution of IoT to fitness. IoT development services Keep reading this article if you want to discover other applications of IoT in sports and fitness and prepare for implementing your project. What can the Internet of Things do for sports? Since the COVID-19 pandemic hit, everyone was forced to stay inside and couldn’t visit the gym anymore. At-home workouts gained popularity as people still wanted to stay in shape. But this new gym culture wasn’t limited to simply exercising at home. Professional athletes, as well as fitness lovers, turned to science and technology to gather data on their performance, analyze it, and receive improvement suggestions, as they would during individual face-to-face coaching sessions. As a result, the global sports analytics market started growing and is expected to , up from 2.2 billion back in 2020. And the global sports tech industry is . Also, the number of wearable IoT devices worldwide is growing rapidly, as more people come to appreciate the technology’s benefits, safety, and convenience. According to Statista, this number is expected to reach 1,105 million units by the end of 2022. surpass 10 billion by 2028 forecast to reach $17.9 billion by 2026 So, how do people use the Internet of Things in health and fitness? In more basic cases, users wear IoT fitness trackers that capture different parameters, such as the user’s pulse, to estimate the number of calories burned and monitor the intensity of their training. Professional athletes opt for more complicated measurements like motion tracking when someone wears numerous sensors that capture and transmit data about the user’s body position to build their skeleton model. This helps understand the person’s body positioning and make improvements in sports, such as javelin throwing and fencing. In addition to body positioning and motion, sensors can measure intensity. For example, sensors mounted on training equipment can measure how much weight an athlete can lift. Professionals can also benefit from analyzing their vital signs, which will indicate how fast they recover and how to achieve their peak performance. But wearables are not the only way people can benefit from IoT in fitness. Sensors can also be positioned inside gyms and sports stadiums or attached to exercise equipment to improve customer experience. The Internet of Things brings other benefits to sports as well: Allows people to detect minor health deteriorations as they monitor vital signs in real-time Enables workout customization for better training and faster rehabilitation Facilitates preventive maintenance as gym owners can track equipment usage Empowers sports organizations to improve fan engagement IoT in sports applications and trends Exercising in groups Deploying IoT technology in sports enables people to participate in group workouts without leaving their houses. Gyms use such solutions to facilitate remote group training and retain customers who can’t physically attend. Several participants exercise at home simultaneously and use wireless sensors to measure and share their data with the trainer, who monitors individuals’ progress to better serve the needs of each member and the group as a whole. One example of such virtual group sessions comes from Peloton, an American company that manufactures gym equipment. They built smart stationary bikes and treadmills that allow users to take part in virtual training sessions with other cyclers and runners from the same city, while physically remaining at home. Immersive training Connected IoT fitness devices, together with virtual reality (VR) technology, help users immerse themselves in a simulated environment while remaining at home. For example, VR headsets, such as , allow users to run virtual marathons from their treadmills and ride infamous cycling competitions from their stationary bikes. Oculus Rift With the help of connected sensors, people can monitor their heart rate and compare it to others involved in the same activity. They also enable the environment to appropriately respond to users’ movements and stimulate them. Smart sports venues Smart stadiums connect different IoT fitness devices, such as cameras, sensors, and digital signage, to create a remarkable fan experience. They can direct users to their seats, and offer information on parking spots, bathroom availability, and promotions, offering a personalized experience. The technology also allows stadium staff to respond quickly to any incidents. One example of IoT sports technology comes from Levi’s Stadium in Santa Clara, California. This venue includes over 2,000 battery-powered beacons that help fans find seats and facilitate placing food orders. In 2021, the Tour de France, one of the largest cycling events that last three weeks, teamed up with a tech partner to create a digital twin of the entire race with the help of real-time data. This gave organizers constant visibility over-riders and key locations, making the competition easier to manage. Digitizing such an enormous event was a challenge that paid off, according to Peter Gray, Senior Vice President, Advanced Technology Group, Sport at NTT. Here is , “It’s a highly dynamic and changing environment that requires immediate access to information to ensure continuous and smooth operations, resulting in more informed and engaged fans.” what he said Equipment maintenance and facility management Sometimes it is hard to tell which fitness machines are more used than others just by looking at the inventory. The IoT in sports sensors allows gym owners to collect and analyze real-time data on their equipment to understand which machines are at risk of breaking down during a workout session. Also, sensors help detect which machines are in high demand and cause the most waiting time for customers. When strategically placed, such devices can also detect infrastructural damage, such as pipe leakage and system failure. Fitness IoT devices planted at different locations at the gym will help the owner manage the whole facility. For example, sensors can notify gym staff when a trash bin is full and when paper towels are finished, and sanitizers are empty. IoT sports technology can also monitor the environment at the gym like temperature, humidity, and lighting, and adjust these parameters accordingly. This will help reduce expenses by optimizing energy usage and prevent revenue losses, as customers might switch to another gym if they are unhappy with the current situation. One that a gym with poor ventilation can lose up to $38,500 of its annual revenue due to people not being able to handle foul sweat smells. study estimates Another IoT project in sports comes from Finland. The city of Helsinki used Kaltiot’s IoT technology to of its 160 outdoor gyms. These are very expensive to maintain traditionally using scheduled maintenance checks. Kaltiot developed tracking solutions that can work in Finland’s harsh climate and gather data on equipment utilization. Harri Uusimäki, Helsinki’s lead maintenance manager for outdoor recreation areas described the benefits of the gathered data as follows: track the usage “The data obtained with Kaltiot’s tracking solution is an untouched area in fitness research. We are getting a huge amount of data which enables us to install the right number of the right kinds of outdoor fitness devices in the right places. With this data, we can purchase the devices that best serves and most interests users.” Shareable health data generation and performance tracking The Internet of Things fitness tracking sensors collects various user parameters, у. This information can be used to create a custom exercise routine tailored to the needs and health condition of this particular person. And for professional athletes, their coaches can collect and analyze performance data that will enable them to make adjustments in dieting and exercising. For example, , a sports tech company, introduced sports IoT HX50 shin pads that are made of lightweight carbon fiber. Football players can wear those pads during training or in official matches. Enhanced with sensors, this product gathers player data and shares it with their coach, who can analyze the performance and come up with improvement ideas. HUMANoX When AI meets the Internet of Things in sports: a use case from ITRex One way to enhance the performance of IoT-based fitness solutions is to enhance them with artificial intelligence (AI) capabilities. In our portfolio, we have an exciting example of how AI and IoT in sports can work together to empower people to train at home with the efficiency of the gym. ITRex was hired by a North American tech startup to . build a fitness mirror with a personal AI-powered coach Project description Our team built a full-length wall mirror with a custom operating system, hardware drivers, cameras, a trained custom machine learning (ML) model for user performance analysis and personalized recommendations, computer vision for pose estimation and body tracking, and numerous sensors. We also that verifies every aspect of the system, including the ML performance. supplied an automation testing framework When users exercise in front of the mirror, sensors gather different types of data, such as weight load, body positioning, etc. All this information is either passed to the mirror and processed locally or transferred to the cloud for further analysis and storage. Based on the received information, the mirror’s virtual coach counts the number of correct reps and gives users advice on how to improve their results. For example, it can point out that the angle of weight holding is wrong or that the user squats too low. The more sensors are used, the more comprehensive the advice. After processing user data, this IoT sports solution creates dashboards to visualize the results. Users can share their information with their real-life coaches and peers. IoT components The fitness mirror operates with three types of sensors: Removable internet of things fitness sensors attached to equipment, such as weights, to track total load, explosive strength, and time under tension Three camera sensors on the mirror itself. They measure different dimensions, such as the user’s proximity to the mirror, and create a 3D model of the person exercising to determine whether the movements are done correctly with the right form Users can optionally connect their wearable devices, and the data they gather will be processed by the system as well Implementation considerations During the product development process, our team performed intensive research and discovery to identify and resolve potential issues. We also conducted a proof of concept (POC) to test our assumptions. It is beneficial for IoT projects to go through the initial research and discovery phase to foresee potential issues and plan for success. Here are some of the prominent concerns that our team identified and successfully resolved for this use case: How to make sure that AI can keep its focus on the exercising person when other people enter the room How to adapt AI to work with people at different levels of fitness, so that it can recognize a correct form not only in professional athletes but also in beginners who are out of shape How to make the system adjust to room dimensions after installation How to enable support for all the commonly-used wearable devices that users might want to connect How to ensure the required accuracy of measured signals and values of different workouts How to properly balance devices costs, reliability, and performance How to implement a user-friendly UI/UX so that every user can find what they are looking for How to make this device attractive, so that users would want to spend more time with the mirror IoT in sports and fitness: implementation tips If your startup is planning to build an IoT fitness solution, we have some practical tips for you! Our R&D engineer and IoT expert, , recommends going through the following steps when implementing IoT in sports. Yahor Paloika For some sports, viewing heart rate and electrocardiography (ECG)/photoplethysmography (PPG) is important. In other sports, athlete body positioning is essential. Some parameters are rather easy to deal with. For example, there is available technology that can capture human pulse rather accurately. On the other hand, measuring blood pressure is still a challenge. Determine which type of data you want to gather and analyze to generate value. Think of the sensors and other hardware devices you will need to build or purchase. If you want to measure body mass index, you will most likely need a device that a person will step on, while if you want to create a 3D skeleton model, motion trackers or a powerful camera system will be incorporated in a suit garment. Decide how you will capture your data. Also, think about how to aggregate data, and where to store and process it. If the fitness Internet of Things device is powerful enough, it can process the data locally. Otherwise, you will need to ensure its safe and timely transition to the cloud or local storage units. Raw data is not valuable on its own. You need to come up with a way to derive useful insights that people can leverage to enhance their performance. So, any IoT fitness solution needs to rely on scientifically sound mathematical models, which can analyze data and give athletes ideas on how to improve or even just help them understand their current condition and performance trends. For example, someone can use sensors to measure their pulse during an exercise session. This information by itself might not be that valuable, but applying a mathematical model to determine how many calories the person burned is already more interesting. Prepare for data analysis. IoT sports devices are prone to security threats. Kaspersky reports that were breached during the first half of 2021, while there were only 639 million similar incidents throughout 2020. Keep in mind that not only connected devices themselves are vulnerable, but also IoT gateways, embedded systems, and data storage centers can be targeted. Think about security. over 1.5 billion IoT devices And of course, don’t forget about users’ physical safety. Wearable sensors are not supposed to cause harm or discomfort. Relevant resources on our blog As IoT goes hand in hand with data analytics, you might already want to think about your . Also, to generate useful insights, you might consider incorporating into your IoT fitness solution. Depending on the product you are building, you might look into the (to monitor the human body biometrics) or into (to understand body positioning). Finally, will help you estimate the total budget required for your IoT in sports solution. data management strategy artificial intelligence and its subtypes Internet of Bodies human pose estimation this article Are you considering deploying IoT in sports? If so, Our team will work with you to build suitable hardware devices and software applications. If needed, our AI experts will develop and train ML models to support your solution’s business logic. drop ITRex a line! Originally published here.

5 Reasons to Incorporate IoT in Fitness and Professional Sports

How to Train Self-Parking Car Using Genetic Algorithm (<500 Lines of Code)

If you have darker skin, shuffle when you walk, or regularly push a stroller, it can throw off the data In a 2019 , 18 senior citizens took a stroll on some treadmills while armed to the hilt with fitness trackers. They had devices strapped to their wrists and ankles, fastened to their belts, and wrapped around their chests. But even with all these trackers, the seniors couldn’t get an accurate step count because their movements were too slow to trigger the sensors in the devices. study Commercial fitness trackers are being used for all kinds of things other than tracking steps. They measure heart rate, track sleep patterns, and calculate basal metabolic rate and calories burned. They’re used in clinical trials, research labs, and by insurance companies and corporate wellness programs. But are they really reliable enough? There are various ways your fitness tracker could go wrong, especially if you don’t fit into a fairly narrow demographic: light skin tone, in your 20s or 30s, with an average fitness level and “purposeful” gait. (We’ll get to that in a minute.) Step Counters: It’s Not What They Know, It’s What They Assume Devices don’t specifically count each step; they approximate using what’s called an “accelerometer.” Accelerometers use electromagnetic sensors to pick up on motion, and the fitness trackers interpret that information using an algorithm that trains the devices to recognize what counts as a step. Users can further personalize those calculations by programming the device with their height, weight, and age. However, the algorithms used are usually based on data from studies that enroll college-age men, said Lynne Feehan, an associate professor in the Department of Physical Therapy at the University of British Columbia who is a . “They do detect steps well if it’s normal paced steps, normal cadence,” Feehan said. “They were designed to measure purposeful walking.” But, she said, if you’re shuffling around in the kitchen taking small steps, or if you’re pushing a stroller or a walker, the accelerometer isn’t as reliable. co-author of a study on Fitbit accuracy “There’s a definite bias in there,” she said. “How a child moves and how someone who’s 90 moves is very different.” In an emailed statement Shelten Yuen, vice president of research at Fitbit, said the company works continuously to improve its algorithms. “Fitbit uses AI and machine learning, coupled with insights from its large database of biometric information to develop and continually improve its offerings,” he wrote. If you take a step, for instance, and thrust your arm forward, the accelerometer will sense the force of that movement and record a step. But if you shuffle, limp, don’t swing your arms while walking, or just plain move slowly, the step may not register on your fitness tracker. 50: Percent of the time that fitness trackers undercounted steps in one laboratory study. The same is true for older adults who move more slowly, those who , or those who have , a symptom of which is reduced arm motion while walking. Feehan and her co-authors found that Fitbits underestimated steps in older adults by 25 percent compared to other, research-grade devices. walk with a limp Parkinson’s disease Feehan’s study also found that the devices counted steps with an acceptable accuracy overall only about a third to one half the time. In the lab, when subjects jogged or walked normally, the devices undercounted steps 50 percent of the time, compared with observed steps. Outside of lab conditions, however, the Fitbits would steps by as much as 35 percent compared to research-grade pedometers and accelerometers. overestimate Fitbit declined to comment on individual studies. The devices are also fairly easy to fool, should you want to, say, beef up your step count for workplace-sponsored fitness campaigns. Before joining The Markup, investigative reporter Surya Mattu collaborated with engineer and artist Tega Brain to devise a for tricking these trackers, from attaching them to metronomes to spinning them around on drills or bicycle wheels. series of strategies So, if you want to compare your activity levels day by day, a step counter can be a good tool. If you’re aiming for those mythical 10,000 steps a day, your count might be off. If you’re an employer wanting to promote step-counting as fitness, know that comparisons among individuals may not be fair, as all people’s steps are not counted equally. Heart Rate: Skin Tone and Exercise Intensity May Matter To measure heart rate, most trackers use a technique called photoplethysmography, which measures blood volume by shining a beam of green LED light into the wrist. When the heart beats, more blood flows into the blood vessels and more of the green light is absorbed by that blood. Between beats, the blood ebbs away, absorbing less light. From these measurements, the device then calculates heart rate. But the green LED sensors that track heart rate can be unreliable. Green light has to penetrate the skin in order to measure blood volume, but suggest that by more melanated skin. several studies green light is more likely to be absorbed The science on this is still being debated. between skin tone and accuracy, though it did report that the error rate during activity was 30 percent higher than at rest. Another study found that heart rate monitors on wrist-worn fitness trackers, especially the Apple Watch, . One study didn’t find a correlation perform pretty well in controlled environments in studies Fitbit says the company has worked hard to calibrate the sensors in their devices to work for everyone. “To achieve the optimum, most consistent performance for users of all skin tones, we designed our optical system to emit green light at sufficient strength to sense through darker skin and our detector to be sensitive enough to accurately detect the heart rate signal,” Yuen said in an emailed statement. But users with dark skin who have tried Fitbits and other trackers that the or don’t work at all. Smartwatch maker Polar even as factors that can limit the accuracy of wrist-worn monitors. have complained devices give strange readings lists dark skin and tattoos Mikael Mattsson, a senior researcher at Karolinska Institutet, a medical university in Sweden, said that in research settings, scientists usually calibrate seven different wavelengths of light to yield the most accurate readings, “but you can’t fit everything in a small watch,” he explains. The Apple Watch uses in addition to green LEDs to measure resting heart rate. Red light is more accurate and reliable than green, said Mattsson, but it’s also easily thrown off by movements, so many devices still rely on green light for heart rate measurements during exercise. The Apple Watch Series 4 also that measure the electrical current from the heart directly, instead of relying on proxy measures like blood levels. infrared sensors features electrodes The accuracy of wrist-worn sensors varies a lot depending on the type of activity. Even so, the accuracy of wrist-worn sensors varies a lot depending on the type of activity. While the devices are pretty good during repetitive, stable, moderately intense activities like riding a stationary bike, studies show they can get heart rate wrong even during other relatively controlled activities, like using an with arm levers, and that . elliptical machine no wrist-worn sensors are as accurate as chest-strap monitors “The more variation within our exercise, the bigger the difference,” said Mattsson. He hasn’t even attempted to test the devices outdoors yet because, in his view, “If they’re not good enough indoors, they won’t be good enough outdoors.” All in all, some heart rate monitors can work pretty well under specific conditions and are fine if you’re tracking heart rate for fun or to casually compare the intensity of your workouts—just know that they’re not perfect. And if you’re a competitive athlete or need to monitor your heart rate for health reasons, you may be better off with a more sophisticated device like a chest strap. Calories: A Most Elusive Metric Here’s where things get really murky. From those potentially inaccurate measurements about movement and heart rate, most devices then use proprietary algorithms to calculate energy expenditure. Some trackers allow you to about your height, weight, age, and sex, which the device uses to calculate basal metabolic rate—basically how many calories your body burns each day in its normal functions. add information But how many calories you burn during an activity is the most unreliable metric that fitness trackers calculate. Mattsson is a co-author of of different ages, weights, heights, skin tones, and fitness levels. His work found that, across the board, no devices had an error rate less than 20 percent for calculating calories burned. “The engineers create algorithms for machines. But humans are not machines,” he said. one of the few studies that look at how well fitness trackers work on a diverse group of people Another tested four trackers—the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa—and concluded that while the Apple Watch and the Polar Vantage V did pretty well at measuring heart rate, none of the four should be used to monitor energy expenditure at the levels tested, which ranged from sitting to sprinting. recent study As with step counting, Mattsson said the problem lies in the algorithm. “Since they are using an algorithm and proxy measures, it’s never going to be perfect,” he said. “The biggest problem is that they’ve done the algorithms for a subset of people. In most studies you talk about white males in their 30s at average fitness level. The farther away you get, the bigger the risk of a problem.” Mattsson said the situation lands fitness trackers in an awkward Catch-22. To improve, companies need a more diverse group of people to buy and use their product. That way, companies would have a more diverse set of data to train the algorithms on and and get algorithms that take into account the full diversity of bodies, fitness levels, and ages. “But then you need a lot of people to use them even if they’re not perfect,” he said. Bottom line? If you’re looking to calorie count, don’t rely on your fitness tracker. Overall, trackers can be fun and useful, but the data they provide has to be taken in context. “It’s more important for everybody to recognize that there’s fallibility to every technology and take that into account when you use the technology,” said Helena Mentis, director of the Bodies in Motion Lab at the University of Maryland, Baltimore County. Fitness trackers can help users learn about themselves, recognize patterns, and reflect on their behavior, but the data these devices provide shouldn’t be taken as gospel truth. And considering the devices’ uncertain accuracy across demographics, anyone considering using commercial fitness trackers to monitor health or compare individuals’ performances should think twice. Originally published as " How Accurate Is Your Commercial FitnessTracker? " with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

The Biases in Fitness Trackers

“Is it possible for a technology solution to replace fitness coaches? Well, someone still has to motivate you saying “Come On, even my grandma can do better!” But from a technology point of view, this high-level requirement led us to 3D human pose estimation technology. In this article, I will describe our own experience of how 3D human pose estimation can be developed and implemented for the AI fitness coach solution. What is Human Pose Estimation? Human pose estimation is a computer vision-based technology that detects and analyzes human posture. The main component of human pose estimation is the modeling of the human body. There are three of the most used types of human body models: skeleton-based model, contour-based, and volume-based. consists of a set of joints (keypoints) like ankles, knees, shoulders, elbows, wrists, and limb orientations comprising the skeletal structure of a human body. This model is used both in 2D and 3D human pose estimation techniques because of its flexibility. Skeleton-based model consists of the contour and rough width of the body torso and limbs, where body parts are presented with boundaries and rectangles of a person’s silhouette. Contour-based model consists of 3D human body shapes and poses represented by volume-based models with geometric meshes and shapes, normally captured with 3D scans. Volume-based model Here, I am talking about , which may be detected from a 2D or 3D perspective. skeleton-based models is based on the detection and analysis of X, Y coordinates of human body joints from an RGB image. 2D pose estimation is based on the detection and analysis of X, Y, Z coordinates of human body joints from an RGB image. 3D pose estimation When speaking about fitness applications involving human pose estimation, it’s better to use 3D estimation, since it analyzes human poses during physical activities more accurately. Talking about AI fitness coach apps, the common flow looks as follows: Capture user’s movements while doing an exercise Analyze the correctness of an exercise performance Display mistakes to the user interface How 3D Human Pose Estimation Works The visual image of how 3D human pose estimation technology detects keypoints on a human body looks like as follows: The process usually involves the extraction of joints on a human body, and then analysis of a human pose by deep learning algorithms. If the human pose estimation system uses video records as a data source, keypoints (joints locations) are detected from a sequence of frames, not a single picture. It allows us to achieve more accuracy as the system analyzes an actual movement of a person, not a steady position. There are several ways to develop the 3D human pose estimation system for fitness. The most optimal ways are training of a deep learning model to extract 3D or 2D key points from the given images/frames For sure, using video streams from several cameras with different views on the same person doing exercises – it will grant us better accuracy. But multi-cameras are often not available. Also, analyzing video from several video streams will take more computer power to process. For our research, we used a single video source for the analysis. And applied convolutional neural networks (CNNs) with dilated temporal convolutions (see the video below). Source We made the analysis of existing models and figured out that is the most optimal choice for fitness app purposes.  In the input, it should have a set of 2D keypoints detected, where the COCO 2017 dataset is applied as a pre-trained 2D detector. For the accurate prediction of a current joint’s position, it processes visual data from several frames captured at various periods of time. VideoPose3D How 3D Human Pose Estimation System Can Be Implemented in AI Fitness Coach App Digitalization has not spared the fitness industry. According to the Research and Markets , the digital fitness market size is expected to reach $27.4 billion by 2022. report The 3D human pose estimation is a relatively new but rapidly evolving technology in digital fitness. After analysis and practical experience of working with 3D human pose estimation systems, we have come to our own vision of how it can be implemented. Let’s review the flow of how this system may be built so that it can analyze movements in an automatic manner by utilizing videos of users performing physical exercises. Assuming that the goal of the given system is to inspect the input video for common exercise mistakes and compare it with the reference video, where the professional athlete is performing the same exercise, the flow will look like as follows: 1. Cutting of the input video depending on the exercise start & end For the start and the end points indication, we can automatically detect the position of body control points by using arbitrary thresholds. For example, when squatting, it is possible to detect the angle of arms and position of hands by height, and then, by using arbitrary thresholds, we can detect the start and the end points of an exercise. Video source One more way is to ask the user to indicate the start and the end of the exercise performance manually. 2. Detecting 2D and 3D keypoints on the user’s body 3. Decomposing of the exercise phases When having the positions of keypoints (joints) extracted, they should be compared with the reference video’s positions. However, we cannot make a direct comparison because the exercise performance speed and the total number of repetitions on the input and reference videos may differ. These discrepancies can be resolved by decomposition of an exercise into phases. We can see how it is illustrated in the image below, where the squatting exercise is decomposed into two primary phases: squatting down and squatting up. Photo source: stronglifts.com The decomposition can be done through the analysis of keypoints detected from the input video frame by frame, and then comparing them by certain criteria with the keypoints from the reference video. 4. Searching for common mistakes When 3D keypoints and certain phases of an exercise are detected, it’s time to detect common mistakes in an exercise technique in the input video. For example,  in squatting, we can detect moments when the legs are bent (not straight) and the knees are closer to the center torso than feet. Video source 5. Comparing the input video frames with the reference ones Here we should take a reference video, where the exercise is performed correctly, split it into phases, and detect keypoints in each frame. When the keypoints are detected and exercise phases defined in both input and reference videos, we can compare each phase of an exercise performed by a user and professional athlete. The step-by-step flow looks as follows: a. Slow down/accelerate the reference video in order to match the speed of the input one. b. Align both skeleton models of the user and a professional athlete so that their rotation angle and origins match. c. Normalize the size of both skeletons since reference and input videos can be captured from different distances. d. Compare keypoints frame by frame and detect motion inconsistencies. e. Repeat the flow separately for different groups of joints (e. g. feet position, knee position, hands and elbows position, etc.). 6. Display results and generate recommendations for a user When the whole analysis cycle is completed, the user will get results displayed in different formats. For example, the output may include interactive 3D reconstructions with mistake hints, so that the user can zoom in/out, go back, forward, or pause at a specific moment. It is also possible to collect and display movement statistics such as the number of repetitions, average speed and duration of one repetition, and others. Visually the 3D human pose estimation system based on videos looks like as follows: Photo sources: stronglifts.com, Men’s Health channel In this article, I described how a 3D human pose estimation system works from the perspective of AI fitness coach app development because it illustrates well how it might work by example. But please note that the flow might be changed depending on business requirements or other factors. Highlights: 3D human pose estimation can be used to detect movement errors in fitness exercises. The selection of a proper 2D keypoint detector is critical in getting high-quality 3D keypoints. Occluded or fast-moving joints can be challenging to detect for 2D keypoint models and lead to incorrect/random predictions. When using pre-trained models, it is important to keep in mind that they will most likely not work well for unusual moves and body positions. You will probably need to fine-tune or re-train at least refine a model on domain-specific or purposefully augmented data. Written by Maksym Tatariants, Data Science Engineer, MobiDev . This article is based on our technology research and experience providing software development services. Previously published at https://mobidev.biz/blog/human-pose-estimation-ai-personal-fitness-coach

TRAIN FITNESS

trainfitness.ai

-- employees

Since n.d.

COMPANY RANKING

TRAIN FITNESS

EVERGREEN INDEX #10201

Representing this company? Click here to claim and customize this page!

Train Fitness WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

TRAIN FITNESS

trainfitness.ai

-- employees

Since n.d.

COMPANY RANKING

TRAIN FITNESS

EVERGREEN INDEX #10201

RELATED COMPANIESRANK

STORIES ABOUT TRAIN FITNESS

Representing this company? Click here to claim and customize this page!

Train Fitness WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!