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IMUPoser: Body Pose Tracking with Consumer Devices

IMUPoser estimates body pose using everyday devices like smartphones and smartwatches.

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Table of Contents

IMUPoser is a system that estimates the pose of a person's body using sensors inside everyday devices like smartphones, smartwatches, and earbuds. This technology can be useful in various areas such as Fitness Tracking, mobile Gaming, virtual assistants, and Rehabilitation. The main idea is to use the sensors already in devices people own, rather than needing special suits or extra sensors that most people are unlikely to use.

Challenges in Body Pose Estimation

Estimating body pose with consumer devices presents unique challenges. The sensors in these devices can produce noisy data. Each device might have different levels of accuracy and reliability. Moreover, the number of devices a person uses can change frequently; someone might use a phone one day, but only wear a smartwatch another day. IMUPoser needs to work with what it has, even if there’s only a single device available.

IMUPoser Dataset

To assess how well IMUPoser works, a new dataset was created. This dataset includes information from ten participants using standard consumer devices in various activities. The goal was to collect enough data to understand how well the system can track body movements under different conditions.

How IMUPoser Works

IMUPoser works by taking data from the sensors in the devices a user has with them. When a user has multiple devices, the system can combine information from all of them to get a clearer picture of the user's body pose. Even if just one device is present, the system can still estimate the body pose to some extent.

Importance of Body Pose Tracking

Body pose tracking can be very beneficial in many areas. It can help athletes improve their performance, assist in physical therapy, and support other health and fitness applications. For instance, a digital coach could provide feedback on someone's exercise form or track their progress over time.

Existing Systems and Their Limitations

Other systems for body pose tracking often require expensive and complex setup that involves multiple sensors and external cameras. While the technology for tracking body pose is improving, most of these solutions are not practical for everyday consumers. IMUPoser aims to fill this gap by using devices that many people already carry with them.

Body Motion Capture in Consumer Devices

While motion capture is well-known in the film and gaming industries, it is starting to find its way into consumer products. The systems usually rely on a set of high-quality sensors which most consumers do not have. IMUPoser seeks to provide motion capture capabilities through devices that people already own.

Use Cases Across Different Areas

  1. Fitness and Sports: IMUPoser could help users track their workouts, providing real-time feedback on form and technique. This can motivate users to improve.

  2. Rehabilitation: For those recovering from injuries, the system can monitor movements during physical therapy sessions, helping therapists adjust recovery plans accordingly.

  3. Gaming: In mobile gaming, body tracking can make games more interactive by allowing players to use their bodies as controllers.

  4. Health Monitoring: Long-term monitoring of body movements can help users track changes in their health and fitness levels over time.

Mobile Device Ecosystem

Smartphones, smartwatches, and earbuds all contain Inertial Measurement Units (IMUs), which are essential for body tracking. By using the data from these sensors, IMUPoser can estimate body pose without needing any extra equipment. This makes it much easier and more accessible for everyday users.

Device Combinations and Limitations

IMUPoser can work with various combinations of devices. For example, a user could have a phone and a smartwatch, or a phone and earbuds. The system observes which devices are used and where they are on the body to provide the best possible estimate of body pose.

However, the accuracy of the system can vary based on the number of active devices. More devices typically lead to better accuracy, but even with just one device, IMUPoser can still offer useful estimates.

Data Collection and Evaluation

Gathering data for IMUPoser involved having participants wear different devices while performing various activities. The goal was to create a robust dataset that could help evaluate the accuracy of the pose estimation system. Researchers used high-quality motion capture systems alongside consumer devices to ensure the results could be compared fairly.

System Architecture

IMUPoser has a specific architecture for processing data and making predictions. When data is collected from devices, it is sent to a model that processes the information and estimates the user's pose. This model can adapt depending on the data it receives, allowing it to provide reasonable estimates even when some sensors are missing.

Training the Model

To train the IMUPoser model, researchers used a large set of motion capture data. This helped the model learn how to estimate poses based on the input from the IMUs in the consumer devices. The model was trained to minimize errors in its predictions, allowing it to improve over time.

Real-Time Performance

One key aspect of IMUPoser is its ability to provide real-time feedback. As users move, the system updates the pose estimates almost instantly. This is crucial for applications like gaming and fitness tracking where immediate feedback could enhance user experience.

Tracking Device Locations

For IMUPoser to work effectively, it must know which devices the user has and where they are located on the body. The system uses user input during setup to determine where devices are typically kept or worn. As the user moves, the system continuously updates its understanding of device locations.

Benefits of Consumer Device Integration

By using everyday devices, IMUPoser can be much more accessible than traditional motion capture systems. It allows users to track their body movements without needing to buy expensive equipment. This accessibility could lead to broader adoption of body tracking technologies.

Evaluation of IMUPoser's Efficacy

To understand how well IMUPoser performs, the system was evaluated under various conditions. Researchers measured accuracy based on different device combinations and tested the system with actual users performing a range of motions. Results showed that even with limited devices, IMUPoser could provide reasonable estimates of body pose.

Key Findings

  1. Effectiveness in Various Conditions: IMUPoser's ability to estimate body pose was found to be effective across different device configurations. More devices generally led to better results.

  2. Error Rates: The accuracy of pose estimation was measured using several metrics. These included errors in joint position and overall body mesh, allowing researchers to compare their results with other systems.

  3. User Experience: Participants found the system easy to use. Because it integrates with devices they already own, it does not require extensive setup or training.

Comparison with Previous Systems

While many existing systems rely on professional-grade sensors, IMUPoser can work with consumer-grade devices, which makes it more practical for everyday use. Although it may not achieve the same level of accuracy as high-end systems with numerous sensors, it still provides valuable insights at a much lower cost, making it a viable option for consumers.

Future Directions

Looking ahead, IMUPoser has the potential for further development. Some ideas for future work might include:

  1. Integrating More Devices: Adding support for other consumer devices could enhance the tracking capabilities of IMUPoser. For example, smart shoes or rings could provide additional data points for better accuracy.

  2. Improving Device Tracking: Refining the system for more reliable device tracking would help increase accuracy, especially when devices are used in different ways.

  3. Using Additional Data: Incorporating data from other sources, like cameras or GPS, could further improve the accuracy of pose estimation.

  4. Wider Application: Expanding the use cases for IMUPoser beyond fitness and gaming could help bring the technology to new fields, such as rehabilitation or healthcare.

  5. User Feedback Integration: Involving users in future developments could help ensure that the features and capabilities meet real-world needs.

Conclusion

IMUPoser represents a significant step toward making body pose estimation widely accessible. By utilizing the sensors already in consumer devices, it offers a practical solution for tracking movements in real-time without requiring additional equipment. As technology continues to evolve, IMUPoser could set the stage for new applications in health, fitness, and beyond.

By simplifying access to body tracking technology, IMUPoser has the potential to impact how people engage in exercise, monitor their health, and enjoy interactive experiences with technology. The road ahead is filled with possibilities for innovation and improvement, making this an exciting field to watch as it develops.

Original Source

Title: IMUPoser: Full-Body Pose Estimation using IMUs in Phones, Watches, and Earbuds

Abstract: Tracking body pose on-the-go could have powerful uses in fitness, mobile gaming, context-aware virtual assistants, and rehabilitation. However, users are unlikely to buy and wear special suits or sensor arrays to achieve this end. Instead, in this work, we explore the feasibility of estimating body pose using IMUs already in devices that many users own -- namely smartphones, smartwatches, and earbuds. This approach has several challenges, including noisy data from low-cost commodity IMUs, and the fact that the number of instrumentation points on a users body is both sparse and in flux. Our pipeline receives whatever subset of IMU data is available, potentially from just a single device, and produces a best-guess pose. To evaluate our model, we created the IMUPoser Dataset, collected from 10 participants wearing or holding off-the-shelf consumer devices and across a variety of activity contexts. We provide a comprehensive evaluation of our system, benchmarking it on both our own and existing IMU datasets.

Authors: Vimal Mollyn, Riku Arakawa, Mayank Goel, Chris Harrison, Karan Ahuja

Last Update: 2023-04-24 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2304.12518

Source PDF: https://arxiv.org/pdf/2304.12518

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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