# Deep Activity Recognition Models with Triaxial Accelerometers

arXiv: https://arxiv.org/abs/1511.04664 · HTML: https://mlsanity.com/paper/1511.04664/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2015.

## Built on by (most cited first, 13 of 13)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation](https://mlsanity.com/paper/2209.09092/) (2022)
- [Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables](https://mlsanity.com/paper/2308.03805/) (2023)
- [Efficient Deep Clustering of Human Activities and How to Improve Evaluation](https://mlsanity.com/paper/2209.08335/) (2022)
- [Siamese Networks for Weakly Supervised Human Activity Recognition](https://mlsanity.com/paper/2307.08944/) (2023)
- [Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data](https://mlsanity.com/paper/2307.11796/) (2023)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)
- [IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications](https://mlsanity.com/paper/2209.00945/) (2022)
- [A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network](https://mlsanity.com/paper/2101.00702/) (2021)
- [Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables](https://mlsanity.com/paper/2110.05732/) (2021)
- [Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearables](https://mlsanity.com/paper/2408.07282/) (2024)
- [Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches](https://mlsanity.com/paper/2512.19713/) (2025)
- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)

## Abstract (arXiv)

Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. Moreover, a hybrid approach of deep learning and hidden Markov models (DL-HMM) is presented for sequential activity recognition. This hybrid approach integrates the hierarchical representations of deep activity recognition models with the stochastic modeling of temporal sequences in the hidden Markov models. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.