ArticleSensors (Basel, Switzerland)2019
Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A Large Language Model-Guided IMU-PDR Method for Detection and Error Correction.Sensors (Basel, Switzerland) · 2026Article
- Indoor Pedestrian Location via Factor Graph Optimization Based on Sliding Windows.Sensors (Basel, Switzerland) · 2025Article
- Lateral walking gait phase recognition for hip exoskeleton by denoising autoencoder-LSTM.Computational and structural biotechnology journal · 2025Article
- Step Length Estimation for Blind Walkers.Computers helping people with special needs : ... International Conference, ICCHP ... : proceedings. International Conference on Computers Helping People with Special Needs · 2024Article
- Data Fusion of Dual Foot-Mounted INS Based on Human Step Length Model.Sensors (Basel, Switzerland) · 2024Article
- Deep Neural Network-Based Fusion Localization Using Smartphones.Sensors (Basel, Switzerland) · 2023Article
- A Fusion Positioning Method for Indoor Geomagnetic/Light Intensity/Pedestrian Dead Reckoning Based on Dual-Layer Tent-Atom Search Optimization-Back Propagation.Sensors (Basel, Switzerland) · 2023Article
- Step Length Is a More Reliable Measurement Than Walking Speed for Pedestrian Dead-Reckoning.International Conference on Indoor Positioning and Indoor Navigation : [proceedings]. International Conference on Indoor Positioning and Indoor Navigation · 2023Article
- Accurate Stride-Length Estimation Based on LT-StrideNet for Pedestrian Dead Reckoning Using a Shank-Mounted Sensor.Micromachines · 2023Article
- A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning.Sensors (Basel, Switzerland) · 2022Article
- Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios.Sensors (Basel, Switzerland) · 2022Article
- A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
- INIM: Inertial Images Construction with Applications to Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Smartphone-Based Inertial Odometry for Blind Walkers.Sensors (Basel, Switzerland) · 2021Article
- A Pedestrian Dead Reckoning Method for Head-Mounted Sensors.Sensors (Basel, Switzerland) · 2020Article
- RNN-Aided Human Velocity Estimation from a Single IMU.Sensors (Basel, Switzerland) · 2020Article
- Differentially Private Mobile Crowd Sensing Considering Sensing Errors.Sensors (Basel, Switzerland) · 2020Article
- Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation.Sensors (Basel, Switzerland) · 2020Article
- Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian's stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.