SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909769064775680 |
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| author | DeVrio, Nathan Mollyn, Vimal Harrison, Chris |
| author_facet | DeVrio, Nathan Mollyn, Vimal Harrison, Chris |
| contents | The ability to track a user's arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving beyond prior work, we take advantage of more recent ultra-wideband (UWB) functionality on these devices to capture absolute distance between the two devices. This measurement is the perfect complement to inertial data, which is relative and suffers from drift. We quantify the performance of our software-only approach using off-the-shelf devices, showing it can estimate the wrist and elbow joints with a \hl{median positional error of 11.0~cm}, without the user having to provide training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data DeVrio, Nathan Mollyn, Vimal Harrison, Chris Human-Computer Interaction Computer Vision and Pattern Recognition Graphics Robotics The ability to track a user's arm pose could be valuable in a wide range of applications, including fitness, rehabilitation, augmented reality input, life logging, and context-aware assistants. Unfortunately, this capability is not readily available to consumers. Systems either require cameras, which carry privacy issues, or utilize multiple worn IMUs or markers. In this work, we describe how an off-the-shelf smartphone and smartwatch can work together to accurately estimate arm pose. Moving beyond prior work, we take advantage of more recent ultra-wideband (UWB) functionality on these devices to capture absolute distance between the two devices. This measurement is the perfect complement to inertial data, which is relative and suffers from drift. We quantify the performance of our software-only approach using off-the-shelf devices, showing it can estimate the wrist and elbow joints with a \hl{median positional error of 11.0~cm}, without the user having to provide training data. |
| title | SmartPoser: Arm Pose Estimation with a Smartphone and Smartwatch Using UWB and IMU Data |
| topic | Human-Computer Interaction Computer Vision and Pattern Recognition Graphics Robotics |
| url | https://arxiv.org/abs/2509.03451 |