# WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition

arXiv: https://arxiv.org/abs/2304.05088 · HTML: https://mlsanity.com/paper/2304.05088/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 89 references that are in the map.
- First posted on arXiv 11 Apr 2023.

## Builds on (most cited first, 30 of 49)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Attention Bottlenecks for Multimodal Fusion](https://mlsanity.com/paper/2107.00135/) (2021)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019)
- [Anticipative Video Transformer](https://mlsanity.com/paper/2106.02036/) (2021)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Learning Salient Boundary Feature for Anchor-free Temporal Action Localization](https://mlsanity.com/paper/2103.13137/) (2021)
- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://mlsanity.com/paper/2303.07347/) (2023)
- [Gaussian Temporal Awareness Networks for Action Localization](https://mlsanity.com/paper/1909.03877/) (2019)
- [Relaxed Transformer Decoders for Direct Action Proposal Generation](https://mlsanity.com/paper/2102.01894/) (2021)
- [Temporal Context Aggregation Network for Temporal Action Proposal Refinement](https://mlsanity.com/paper/2103.13141/) (2021)
- [Video Self-Stitching Graph Network for Temporal Action Localization](https://mlsanity.com/paper/2011.14598/) (2020)
- [Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables](https://mlsanity.com/paper/1604.08880/) (2016)
- [Fast Learning of Temporal Action Proposal via Dense Boundary Generator](https://mlsanity.com/paper/1911.04127/) (2019)
- [Boundary Content Graph Neural Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2008.01432/) (2020)
- [TALLFormer: Temporal Action Localization with a Long-memory Transformer](https://mlsanity.com/paper/2204.01680/) (2022)
- [Enriching Local and Global Contexts for Temporal Action Localization](https://mlsanity.com/paper/2107.12960/) (2021)

## Built on by (most cited first, 30 of 32)

- [EgoLife: Towards Egocentric Life Assistant](https://mlsanity.com/paper/2503.03803/) (2025)
- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [Temporal Action Localization for Inertial-based Human Activity Recognition](https://mlsanity.com/paper/2311.15831/) (2023)
- [An Egocentric Vision-Language Model based Portable Real-time Smart Assistant](https://mlsanity.com/paper/2503.04250/) (2025)
- [Towards Generalizable Human Activity Recognition: A Survey](https://mlsanity.com/paper/2508.12213/) (2025)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning](https://mlsanity.com/paper/2511.18242/) (2025)
- [Weak-Annotation of HAR Datasets using Vision Foundation Models](https://mlsanity.com/paper/2408.05169/) (2024)
- [SlotFM: A Motion Foundation Model with Slot Attention for Diverse Downstream Tasks](https://mlsanity.com/paper/2509.21673/) (2025)
- [γ-Quant: Towards Learnable Quantization for Low-bit Pattern Recognition](https://mlsanity.com/paper/2509.22448/) (2025)
- [Left-Right Swapping and Upper-Lower Limb Pairing for Robust Multi-Wearable Workout Activity Detection](https://mlsanity.com/paper/2408.03947/) (2024)
- [PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2503.17978/) (2025)
- [HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound](https://mlsanity.com/paper/2605.02596/) (2026)
- [Rethinking RAG in Long Videos: What to Retrieve and How to Use It?](https://mlsanity.com/paper/2606.13141/) (2026)
- [TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition](https://mlsanity.com/paper/2608.15861/) (2026)
- [Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker](https://mlsanity.com/paper/2410.01966/) (2024)
- [Detecting Informative Channels: ActionFormer](https://mlsanity.com/paper/2505.20739/) (2025)
- [DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition](https://mlsanity.com/paper/2505.20894/) (2025)
- [Label Leakage in Federated Inertial-based Human Activity Recognition](https://mlsanity.com/paper/2505.20924/) (2025)
- [FedFitTech: A Baseline in Federated Learning for Fitness Tracking](https://mlsanity.com/paper/2506.16840/) (2025)
- [MobiDiary: Autoregressive Action Captioning with Wearable Devices and Wireless Signals](https://mlsanity.com/paper/2601.08204/) (2026)
- [WS-IMUBench: Can Weakly Supervised Methods from Audio, Image, and Video Be Adapted for IMU-based Temporal Action Localization?](https://mlsanity.com/paper/2602.01850/) (2026)
- [ActivityNarrated: An Open-Ended Narrative Paradigm for Wearable Human Activity Understanding](https://mlsanity.com/paper/2604.00767/) (2026)
- [BayaHAR: Lightweight Bayesian Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition](https://mlsanity.com/paper/2606.04798/) (2026)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)
- [EgoInertia-MI: A Multimodal Egocentric Vision and IMU Benchmark for Motor Impairment Assessment](https://mlsanity.com/paper/2607.03934/) (2026)
- [ProcAgent: An Agentic Framework for Procedural Task Guidance on Edge with Human-in-the-Loop](https://mlsanity.com/paper/2607.24770/) (2026)
- [Dynamic Influence-Weighted Distillation for Single-IMU Activity Recognition](https://mlsanity.com/paper/2608.24904/) (2026)
- [RevalExo: A Functional Daily-Activity Benchmark for Inertial and Visual Locomotion Mode Recognition in Older Adults and Clinical Cohorts](https://mlsanity.com/paper/2609.08090/) (2026)
- [PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization](https://mlsanity.com/paper/2609.21462/) (2026)

## Abstract (arXiv)

Research has shown the complementarity of camera- and inertial-based data for modeling human activities, yet datasets with both egocentric video and inertial-based sensor data remain scarce. In this paper, we introduce WEAR, an outdoor sports dataset for both vision- and inertial-based human activity recognition (HAR). Data from 22 participants performing a total of 18 different workout activities was collected with synchronized inertial (acceleration) and camera (egocentric video) data recorded at 11 different outside locations. WEAR provides a challenging prediction scenario in changing outdoor environments using a sensor placement, in line with recent trends in real-world applications. Benchmark results show that through our sensor placement, each modality interestingly offers complementary strengths and weaknesses in their prediction performance. Further, in light of the recent success of single-stage Temporal Action Localization (TAL) models, we demonstrate their versatility of not only being trained using visual data, but also using raw inertial data and being capable to fuse both modalities by means of simple concatenation. The dataset and code to reproduce experiments is publicly available via: mariusbock.github.io/wear/.