Measuring body dimensions using only your phone is not only exciting but also paves the way for innovative solutions across multiple industries like retail, healthcare, and fitness. The rise of AR and AI amplifies this potential, enhancing the accuracy and accessibility of 3D body measurements.
If you are on the path of adopting this technology in your product, this guide will help you make progress. Here, we explain all the nuances of the body measurement app development and consider multiple use cases, step-by-step instructions, challenges, and best practices.
Cases of Body Measurement Applications
A body measurement application is a digital tool that relies on advanced technologies, such as 3D scanning, ML, and smartphone sensors, to capture and analyze user physical dimensions. These apps are designed to provide accurate measurements of the human body, often without the need for traditional measuring tools like tape measures.
Types of body measurement apps include:
- 3D scanning apps: These apps use a device’s camera and sensors to scan the user’s body, capturing a 3D model. The software processes the data to create an accurate digital representation of the user’s physical shape, which can be used for precise clothing size recommendations.
- Fitting rooms: Virtual fitting rooms with body measurement features allow users to virtually try on clothing in real-time. By overlaying digital garments onto the user’s live image, the app gives an interactive experience of how the clothing will fit their body.
- Fitness and health tracking apps: Some body measurement apps are used in the fitness and health industries to track body metrics such as weight, muscle mass, and body fat percentage. These apps can also provide recommendations for workout routines or health goals based on the user’s body data.
Let’s take a look at some typical use cases of body measurement apps:
- Online shopping: Retailers integrate these apps into their platforms to offer customers accurate size recommendations, reducing the guesswork and improving the likelihood of a perfect fit. This helps minimize returns due to sizing issues, benefiting both the customer and the retailer.
- Tailoring services are increasingly using body measurement apps to collect customer measurements digitally. This allows for precise, made-to-order garments without requiring customers to visit a tailor in person, making custom clothing accessible and convenient.
- Fitness apps use body measurement features to track changes in the user’s body over time. Whether the goal is to gain muscle, lose weight, or maintain a healthy physique, these apps provide detailed insights into body composition, helping users monitor their progress and adjust their fitness routines accordingly.
- In the healthcare industry, body measurement apps are being used to create custom-fitted medical garments, such as compression wear, surgical attire, and prosthetics. These apps ensure that patients receive clothing or medical devices tailored to their exact body shape, improving comfort and functionality.
In the following section, we’ll take a look at the key technologies and approaches to building body measurement applications.
3D Body Measurement Technology Overview
The development of body measurement applications relies on a combination of cutting-edge technologies that enable precise, real-time capturing and analysis of human body dimensions. These apps use advanced techniques such as 3D scanning, Artificial Intelligence, Machine Learning, Computer Vision, and Augmented Reality to create highly accurate digital models of a user’s body.
Computer vision
Computer vision is a fundamental technology in body measurement applications. It uses the device’s camera to capture images or videos of a user’s body and processes this visual data to extract relevant features. Advanced computer vision algorithms can identify and analyze the human form from photos taken in different environments, enabling the software to detect the user’s body shape and dimensions.
Machine learning
Machine learning enhances the accuracy and adaptability of body measurement apps by training algorithms to recognize patterns in body shapes and dimensions. Neural networks are employed to detect key points on the body, such as the shoulders, hips, and knees, by analyzing large datasets of pre-labeled images. These key points are crucial for calculating precise body measurements.
Over time, the algorithms become more accurate as they learn from each interaction, trained on even more diverse images, improving the system’s ability to generate reliable and personalized size recommendations. Moreover, ML algorithms can help to predict missing body measurements.
3D Reconstruction and Geometry Algorithms
3D body measurement apps use 3D reconstruction techniques to transform 2D images or videos into detailed 3D models. Statistical modeling and 3D geometry algorithms are applied to the key points detected by computer vision and machine learning. This allows the app to build an accurate representation of the user’s body in three dimensions. The generated 3D model is essential for visualizing the user’s body and providing measurements like waist circumference, height, and arm length. These models also support virtual try-ons, allowing users to see how clothing would fit on their unique body shape.
Pose Estimation and Keypoint Detection
Human pose estimation is used to identify the exact positioning of a person’s body and detect key points such as joints and other landmarks. By analyzing the relationship between these key points, pose estimation allows the app to calculate accurate measurements like arm length, chest circumference, or hip width without the need for physical measuring tools.
Keypoint detection works in tandem with pose estimation, ensuring that body measurements are consistent and reliable.
Augmented Reality
Augmented reality enhances the user experience by providing real-time feedback during body scans. AR helps guide users in positioning their devices and themselves to ensure that the body scan captures accurate measurements. Additionally, AR can convert the pixel-based measurements taken by the camera into real-world metric measurements, further increasing accuracy. This technology provides users with a more interactive and engaging experience, making the process of body measurement more intuitive.
Why is ARKit’s LiDAR Alone Not Enough for Body Measurement Apps?
The advancement of AR and AI technologies has led to the enhancement of frameworks that simplify virtual measurements, such as ARKit, which utilizes LiDAR capabilities. This approach works well for measuring stationary objects like rooms and furniture. However, measuring the human body presents more challenges due to its complex and dynamic shapes. LiDAR struggles to accurately capture these contours, particularly when the body is in motion or in different postures.
To effectively measure the human body, AI models are needed that can identify the position of the body within the frame, determine its outlines and body parts, and measure them accurately. By analyzing visual data from various angles, these models can generate a detailed 3D representation of the body, accommodating changes in posture and movement.
How to Build an AI Body Measurement App
The workflow of the body measurement app development may vary depending on the type of product and the range of features you wish to include in your app. Let’s use the development of a virtual try-on clothing app with body measurement functionality as an example.
This app allows users to virtually try on clothes and provide accurate body measurements to help them choose the correct size. Here’s a brief overview of how they work:
- A user takes a photo or video of themselves, following a prearranged A-pose, and provides details like their height.
- Using the photo, the app detects key body points to determine the correct size.
- Generative models help match the clothing to the user’s image, including generating body parts that may be obscured by the clothes.
However, let’s dive into the specifics of how we handle the body measurement aspect of the app. Based on our expertise, we recommend using a combination of two machine learning models for optimal accuracy.
First, a Pose Estimation model is employed to identify key body points. Then, an Image Segmentation model, specifically Body Segmentation, creates pixel masks of the human body, which can be translated into measurements in centimeters or meters.
From our experience, it’s important to note that the accuracy of body measurements can vary significantly between different body parts.
The table below illustrates the complexity of measuring various body parts.
POSE ESTIMATION MODELS
To analyze human posture, it’s essential to apply pose estimation methods. There are two main approaches for this: 2D and 3D pose estimation models. Based on practical experience, 2D pose estimation models tend to be less accurate than 3D models. The advantage of 3D pose estimation models is that they detect body key points in (x, y, z) coordinates, which allows for a volumetric evaluation of human poses — an important aspect of virtual fitting room solutions.
However, detecting key points alone is not sufficient for obtaining accurate measurements. At this stage, it becomes necessary to integrate a Body Segmentation model.
BODY SEGMENTATION
A body segmentation model helps to identify the body’s mask in the photos provided by users. This model determines which pixels in an image correspond to the ”body” object. This is a well-established task, and many models can generate reliable results. Since the results are pixel-based, we must convert these pixel measurements into real-world units (centimeters or meters). The challenge lies in adjusting the pixel data to a proper scale, which can vary depending on how users take photos, including camera distance. Therefore, using the user’s height as a reference is an easy way to calculate an accurate scale.
BODY MEASUREMENTS CALCULATIONS
The final step involves developing a process to calculate body measurements based on the outputs from the pose estimation and body segmentation models. This step comes with several challenges:
- Camera angle: Proper key point detection is dependent on the camera angle. If the user’s position differs from the reference image, inaccuracies may occur. Body parts overlapping in the photo can also cause key points to be detected incorrectly.
- Background interference: If the background color is too similar to the user’s clothing, this can lead to poor body segmentation, resulting in incorrect body measurements.
Manual measurements also present challenges. For example, users may take three photos and manually identify key points to get measurements following instructions. Risks involved in manual measurements include:
- Users need to wear tight-fitting clothing or just underwear for accurate measurements. Thick clothing, even if tight, can cause inaccuracies.
- Proper lighting is required.
- The user must stand out from the background (humans can identify bodies better than AI in challenging situations).
- Camera quality on phones may affect the accuracy of measurements.
- Different users may interpret measurement instructions differently.
- Manual measurements typically involve calculating the distance between two points, such as from point A to point B.
To enhance accuracy, a hybrid approach can be employed — using AI to measure certain dimensions while leaving the rest for manual measurements. Additionally, users could be allowed to adjust AI-generated measurements as needed.
With AI development, many uncertainties can arise. Consulting with AI experts can help mitigate these uncertainties, guide the development process, and boost confidence in making informed strategic decisions.
Interested in AI consulting services?
Learn moreChallenges and Best Practices for Building a Body Measurement App for Clothing Size
Accurate body measurements using pose estimation models face several challenges, from ensuring precision to dealing with environmental factors and real-time processing demands.
Below, we specify key challenges and best practices to overcome them for reliable and efficient measurement solutions.
Measurement accuracy
Ensuring precise body measurements can be challenging due to varying body shapes and sizes.
Best practices to solve this challenge:
- Use advanced pose estimation algorithms to improve accuracy
- Implement calibration features that allow users to adjust measurements based on their body shape or past experiences
- Include a feedback system for users to report inaccuracies, helping to refine algorithms for future measurements
Environmental factors
Lighting and background conditions can affect the performance of pose estimation, leading to errors.
Best practices to solve this challenge:
- Test the system in various environments and provide users with guidelines for optimal lighting and background conditions
- Use augmented reality (AR) to guide users in positioning themselves and their devices correctly
- Apply computer vision techniques to reduce background noise and focus on the user’s body
Real-Time Processing
Achieving fast, accurate pose estimation in real time requires significant computational resources and optimization.
Best practices to solve this challenge:
- Optimize algorithms for both speed and efficiency. Consider using cloud computing to handle heavy processing tasks.
- Utilize the device’s processing power for local calculations, reducing latency and improving response times. Alternatively, combine edge computing with cloud processing.
- Use data streaming techniques to focus on relevant frames or measurements, reducing the amount of data processed in real-time.
How MobiDev Can Help You With AI Body Measurement App Development
MobiDev has been operating in the software development industry since 2009. During this time, we have gained significant expertise, particularly in AI and AR application development. We offer both AI consulting and AI development services to assist you in assessing the feasibility of your ideas, refining your product concept, and transforming it into a market-ready solution.
Reach out to us to explore how we can help you achieve your business objectives.
