# Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition

arXiv: https://arxiv.org/abs/2406.16886 · HTML: https://mlsanity.com/paper/2406.16886/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Builds on (most cited first, 7 of 7)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Gimme Signals: Discriminative signal encoding for multimodal activity recognition](https://mlsanity.com/paper/2003.06156/) (2020)
- [A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors](https://mlsanity.com/paper/2003.01874/) (2020)
- [Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition](https://mlsanity.com/paper/2110.12163/) (2021)
- [Worker Activity Recognition in Manufacturing Line Using Near-body Electric Field](https://mlsanity.com/paper/2308.03514/) (2023)
- [Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2306.05285/) (2023)
- [BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition](https://mlsanity.com/paper/2208.03647/) (2022)

## Built on by (most cited first, 8 of 8)

- [A Comprehensive Methodological Survey of Human Activity Recognition Across Divers Data Modalities](https://mlsanity.com/paper/2409.09678/) (2024)
- [Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation](https://mlsanity.com/paper/2505.24415/) (2025)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [Detecting Informative Channels: ActionFormer](https://mlsanity.com/paper/2505.20739/) (2025)
- [Diffusion Model-based Activity Completion for AI Motion Capture from Videos](https://mlsanity.com/paper/2505.21566/) (2025)
- [Self-supervised Learning Method Using Transformer for Multi-dimensional Sensor Data Processing](https://mlsanity.com/paper/2505.21918/) (2025)
- [Reinforcement Learning Driven Generalizable Feature Representation for Cross-User Activity Recognition](https://mlsanity.com/paper/2509.01031/) (2025)
- [Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction](https://mlsanity.com/paper/2607.15400/) (2026)

## Abstract (arXiv)

The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, which offer self-labeled activity data from users, the volume of labeled data remains insufficient compared to domains where deep learning has achieved remarkable success. Addressing this gap, in this paper, we propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model that generates sensor data directly from 3D skeleton pose sequences. our method simultaneously trains the pose-to-sensor network and a human activity classifier, optimizing both data reconstruction and activity recognition. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset. Experimental results demonstrate the superiority of our framework with significant performance improvements over baseline methods.