# Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1609.03323 · HTML: https://mlsanity.com/paper/1609.03323/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2016.

## Built on by (most cited first, 10 of 10)

- [TransPose: Real-time 3D Human Translation and Pose Estimation with Six Inertial Sensors](https://mlsanity.com/paper/2105.04605/) (2021)
- [Millimeter Wave Radar-based Human Activity Recognition for Healthcare Monitoring Robot](https://mlsanity.com/paper/2405.01882/) (2024)
- [Machine-learning parameter tracking with partial state observation](https://mlsanity.com/paper/2311.09142/) (2023)
- [Octave Mix: Data augmentation using frequency decomposition for activity recognition](https://mlsanity.com/paper/2101.02882/) (2021)
- [Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor](https://mlsanity.com/paper/2203.00503/) (2022)
- [Classification of anomalous gait using Machine Learning techniques and embedded sensors](https://mlsanity.com/paper/2110.06139/) (2021)
- [IMU Based Deep Stride Length Estimation With Self-Supervised Learning](https://mlsanity.com/paper/2205.02977/) (2022)
- [Deep Gait Tracking With Inertial Measurement Unit](https://mlsanity.com/paper/2205.04666/) (2022)
- [Real-Time Wearable Gait Phase Segmentation For Running And Walking](https://mlsanity.com/paper/2205.04668/) (2022)
- [Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data](https://mlsanity.com/paper/2506.12561/) (2025)

## Abstract (arXiv)

Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of 8 spatio-temporal stride parameters. To this end, two modelling approaches are compared: A combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modelling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15±6.09 cm, -0.09±4.22 cm and 0.13 ± 3.78^circ respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ± 0.07, ±0.05, ± 0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining state-of-the-art. Our results further indicate that the proposed change in methodology could substitute assumption-driven double-integration methods and enable mobile assessment of spatio-temporal stride parameters in clinically critical situations as e.g. in the case of spastic gait impairments.