# Deep Learning-Based Gait Recognition Using Smartphones in the Wild

arXiv: https://arxiv.org/abs/1811.00338 · HTML: https://mlsanity.com/paper/1811.00338/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 19 of 19)

- [Gait Recognition Based on Deep Learning: A Survey](https://mlsanity.com/paper/2201.03323/) (2022)
- [Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition](https://mlsanity.com/paper/2206.01441/) (2022)
- [GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning](https://mlsanity.com/paper/2110.03967/) (2021)
- [ModSelect: Automatic Modality Selection for Synthetic-to-Real Domain Generalization](https://mlsanity.com/paper/2208.09414/) (2022)
- [A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities](https://mlsanity.com/paper/2505.00525/) (2025)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)
- [Deep Heterogeneous Contrastive Hyper-Graph Learning for In-the-Wild Context-Aware Human Activity Recognition](https://mlsanity.com/paper/2409.18481/) (2024)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Physical Self-Supervised Learning: IMU Sensing without Manual Labels](https://mlsanity.com/paper/2607.18361/) (2026)
- [WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors](https://mlsanity.com/paper/2105.01753/) (2021)
- [Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition](https://mlsanity.com/paper/2105.03958/) (2021)
- [Towards Complete-View and High-Level Pose-based Gait Recognition](https://mlsanity.com/paper/2209.11577/) (2022)
- [Realtime Person Identification via Gait Analysis](https://mlsanity.com/paper/2404.15312/) (2024)
- [Decoding Children's Gait Behavior](https://mlsanity.com/paper/2608.00371/) (2026)
- [Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers](https://mlsanity.com/paper/2108.02400/) (2021)
- [Explainable AI and Machine Learning Towards Human Gait Deterioration Analysis](https://mlsanity.com/paper/2306.07165/) (2023)
- [Contrastive Learning with Auxiliary User Detection for Identifying Activities](https://mlsanity.com/paper/2410.21300/) (2024)
- [Gait Recognition Based on Tiny ML and IMU Sensors](https://mlsanity.com/paper/2507.18627/) (2025)
- [Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification](https://mlsanity.com/paper/2607.19122/) (2026)

## Abstract (arXiv)

Compared to other biometrics, gait is difficult to conceal and has the advantage of being unobtrusive. Inertial sensors, such as accelerometers and gyroscopes, are often used to capture gait dynamics. These inertial sensors are commonly integrated into smartphones and are widely used by the average person, which makes gait data convenient and inexpensive to collect. In this paper, we study gait recognition using smartphones in the wild. In contrast to traditional methods, which often require a person to walk along a specified road and/or at a normal walking speed, the proposed method collects inertial gait data under unconstrained conditions without knowing when, where, and how the user walks. To obtain good person identification and authentication performance, deep-learning techniques are presented to learn and model the gait biometrics based on walking data. Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network. In the experiments, two datasets collected by smartphones for a total of 118 subjects are used for evaluations. The experiments show that the proposed method achieves higher than 93.5% and 93.7% accuracies in person identification and authentication, respectively.