Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918525959929856 |
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| author | Huang, Chung-Ta Das, Leopold Zhou, Jeffrey Siddique, Faizaan Baek, Julia Seungjoo Liu, Serena Rusli, Andrew Zhou, Todd Y. Yu, Freddy Hansen, Sinclair Hu, Ziling Sharma, Arnav Wang, Mengyu |
| author_facet | Huang, Chung-Ta Das, Leopold Zhou, Jeffrey Siddique, Faizaan Baek, Julia Seungjoo Liu, Serena Rusli, Andrew Zhou, Todd Y. Yu, Freddy Hansen, Sinclair Hu, Ziling Sharma, Arnav Wang, Mengyu |
| contents | AR smart glasses need continuous behavioral context to offer proactive assistance, yet their most practical always-on sensor, the head-mounted Inertial Measurement Unit (IMU), detects only motion primitives such as walking or standing. We push beyond motion primitives to behavioral-level recognition, defining five categories that balance AR application need with sensor observability. To this end, we construct a 160K-sample Ego4D dataset with a four-tier quality assurance framework spanning 8 activity scenarios, and propose HiT-HAR, a 703K-parameter hierarchical model that outperforms prior head-mounted IMU models on five-class action and eight-class scenario recognition. We further map the observability frontier of head-mounted IMU through per-class separability analysis, identifying which behavioral categories are reliably observable (Locomotion), which benefit from temporal context (Object Transfer, Task Operation), and where scenario-dependent signal overlap poses remaining challenges. Our results indicate that architectural choices exploiting temporal context and scenario structure outperform simply scaling model size. The code and dataset are publicly available at https://github.com/Harvard-AI-and-Robotics-Lab/HiT-HAR. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27464 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU Huang, Chung-Ta Das, Leopold Zhou, Jeffrey Siddique, Faizaan Baek, Julia Seungjoo Liu, Serena Rusli, Andrew Zhou, Todd Y. Yu, Freddy Hansen, Sinclair Hu, Ziling Sharma, Arnav Wang, Mengyu Computer Vision and Pattern Recognition Artificial Intelligence AR smart glasses need continuous behavioral context to offer proactive assistance, yet their most practical always-on sensor, the head-mounted Inertial Measurement Unit (IMU), detects only motion primitives such as walking or standing. We push beyond motion primitives to behavioral-level recognition, defining five categories that balance AR application need with sensor observability. To this end, we construct a 160K-sample Ego4D dataset with a four-tier quality assurance framework spanning 8 activity scenarios, and propose HiT-HAR, a 703K-parameter hierarchical model that outperforms prior head-mounted IMU models on five-class action and eight-class scenario recognition. We further map the observability frontier of head-mounted IMU through per-class separability analysis, identifying which behavioral categories are reliably observable (Locomotion), which benefit from temporal context (Object Transfer, Task Operation), and where scenario-dependent signal overlap poses remaining challenges. Our results indicate that architectural choices exploiting temporal context and scenario structure outperform simply scaling model size. The code and dataset are publicly available at https://github.com/Harvard-AI-and-Robotics-Lab/HiT-HAR. |
| title | Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.27464 |