# On Attention Models for Human Activity Recognition

arXiv: https://arxiv.org/abs/1805.07648 · HTML: https://mlsanity.com/paper/1805.07648/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2018.

## Built on by (most cited first, 30 of 34)

- [SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition](https://mlsanity.com/paper/2410.10624/) (2024)
- [WEAR: An Outdoor Sports Dataset for Wearable and Egocentric Activity Recognition](https://mlsanity.com/paper/2304.05088/) (2023)
- [Improving Deep Learning for HAR with shallow LSTMs](https://mlsanity.com/paper/2108.00702/) (2021)
- [Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition](https://mlsanity.com/paper/2407.06628/) (2024)
- [Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task](https://mlsanity.com/paper/2411.14452/) (2024)
- [Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport](https://mlsanity.com/paper/2310.09114/) (2023)
- [Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition](https://mlsanity.com/paper/2202.07260/) (2022)
- [Temporal Action Localization for Inertial-based Human Activity Recognition](https://mlsanity.com/paper/2311.15831/) (2023)
- [Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2109.02054/) (2021)
- [Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition](https://mlsanity.com/paper/2203.12230/) (2022)
- [P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer](https://mlsanity.com/paper/2403.08214/) (2024)
- [Regional Attention Network (RAN) for Head Pose and Fine-grained Gesture Recognition](https://mlsanity.com/paper/2101.06634/) (2021)
- [Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition](https://mlsanity.com/paper/2103.04279/) (2021)
- [Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals](https://mlsanity.com/paper/2112.11224/) (2021)
- [ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition](https://mlsanity.com/paper/2305.13541/) (2023)
- [Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data](https://mlsanity.com/paper/2402.09434/) (2024)
- [MAC-Gaze: Motion-Aware Continual Calibration for Mobile Gaze Tracking](https://mlsanity.com/paper/2505.22769/) (2025)
- [Transformer Networks for Data Augmentation of Human Physical Activity Recognition](https://mlsanity.com/paper/2109.01081/) (2021)
- [Multi-Stage Based Feature Fusion of Multi-Modal Data for Human Activity Recognition](https://mlsanity.com/paper/2211.04331/) (2022)
- [FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2311.07765/) (2023)
- [Beyond Isolated Frames: Enhancing Sensor-Based Human Activity Recognition through Intra- and Inter-Frame Attention](https://mlsanity.com/paper/2405.19349/) (2024)
- [Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition](https://mlsanity.com/paper/2501.10917/) (2025)
- [PhysiQ: Off-site Quality Assessment of Exercise in Physical Therapy](https://mlsanity.com/paper/2211.08245/) (2022)
- [Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2212.13918/) (2022)
- [Hi-OSCAR: Hierarchical Open-set Classifier for Human Activity Recognition](https://mlsanity.com/paper/2510.08635/) (2025)
- [Tutorial on Deep Learning for Human Activity Recognition](https://mlsanity.com/paper/2110.06663/) (2021)
- [Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition](https://mlsanity.com/paper/2206.04855/) (2022)
- [Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition](https://mlsanity.com/paper/2305.03810/) (2023)
- [DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity Recognition](https://mlsanity.com/paper/2505.20894/) (2025)
- [MicroBi-ConvLSTM: An Ultra-Lightweight Efficient Model for Human Activity Recognition on Resource Constrained Devices](https://mlsanity.com/paper/2602.06523/) (2026)

## Abstract (arXiv)

Most approaches that model time-series data in human activity recognition based on body-worn sensing (HAR) use a fixed size temporal context to represent different activities. This might, however, not be apt for sets of activities with individ- ually varying durations. We introduce attention models into HAR research as a data driven approach for exploring relevant temporal context. Attention models learn a set of weights over input data, which we leverage to weight the temporal context being considered to model each sensor reading. We construct attention models for HAR by adding attention layers to a state- of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on benchmark datasets achieving sig- nificant increase in performance. Finally, we visualize the learned weights to better understand what constitutes relevant temporal context.