ArticleSensors (Basel, Switzerland)2022
Fusion Poser: 3D Human Pose Estimation Using Sparse IMUs and Head Trackers in Real Time.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An Adaptive IMU-Visual Multimodal Fusion System for Real-Time Exercise Recognition and Movement Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- Human-centered design-based lightweight wearable IMU human pose estimation.Scientific reports · 2026Article
- Visual-Inertial Fusion Framework for Isolating Seated Human-Body Vibration in Dynamic Vehicular Environments.Sensors (Basel, Switzerland) · 2026Article
- Three-Dimensional Human Posture Recognition by Extremity Angle Estimation with Minimal IMU Sensor.Sensors (Basel, Switzerland) · 2024Article
- Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment.Sensors (Basel, Switzerland) · 2024Article
- SSA Net: Small Scale-Aware Enhancement Network for Human Pose Estimation.Sensors (Basel, Switzerland) · 2023Article
- The Performance of Inertial Measurement Unit Sensors on Various Hardware Platforms for Binaural Head-Tracking Applications.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
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Abstract
The motion capture method using sparse inertial sensors is an approach for solving the occlusion and economic problems in vision-based methods, which is suitable for virtual reality applications and works in complex environments. However, VR applications need to track the location of the user in real-world space, which is hard to obtain using only inertial sensors. In this paper, we present Fusion Poser, which combines the deep learning-based pose estimation and location tracking method with six inertial measurement units and a head tracking sensor that provides head-mounted displays. To estimate human poses, we propose a bidirectional recurrent neural network with a convolutional long short-term memory layer that achieves higher accuracy and stability by preserving spatio-temporal properties. To locate a user with real-world coordinates, our method integrates the results of an estimated joint pose with the pose of the tracker. To train the model, we gathered public motion capture datasets of synthesized IMU measurement data, as well as creating a real-world dataset. In the evaluation, our method showed higher accuracy and a more robust estimation performance, especially when the user adopted lower poses, such as a squat or a bow.
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