Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU
This paper presents a behavioral-level activity recognition method using head-mounted IMU, going beyond basic motion primitives. The authors define five behavioral categories, construct a 160K-sample dataset from Ego4D with a four-tier quality assurance framework, and propose HiT-HAR, a 703K-parameter hierarchical model that outperforms prior models on action and scenario recognition. Observability analysis reveals locomotion is reliably observable, while object transfer and task operation benefit from temporal context; scenario-dependent signal overlap remains a challenge. Results show that architectural choices exploiting temporal context and scenario structure outperform simply scaling model size.
Article intelligence
Key points
- Proposes HiT-HAR, a hierarchical model for behavioral activity recognition from head-mounted IMU, going beyond motion primitives
- Constructs a 160K-sample Ego4D dataset with 8 scenarios and 5 behavioral categories, using a four-tier quality assurance framework
- Maps observability of head-mounted IMU: locomotion reliable, object transfer and task operation need temporal context
- Demonstrates that leveraging temporal context and scenario structure is more effective than scaling model size
Why it matters
This matters because proposes HiT-HAR, a hierarchical model for behavioral activity recognition from head-mounted IMU, going beyond motion primitives.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27464] Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU
[Submitted on 26 May 2026]
Title:Beyond Motion Primitives: Behavioral Activity Recognition from Head-Mounted IMU
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Abstract: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 this https URL.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27464 [cs.CV]
(or arXiv:2605.27464v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.27464
arXiv-issued DOI via DataCite
Submission history
From: Chung-Ta Huang [view email] [v1] Tue, 26 May 2026 04:01:43 UTC (9,372 KB)
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