ArticleSensors (Basel, Switzerland)2020
RNN-Aided Human Velocity Estimation from a Single IMU.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Learning-based 3D human kinematics estimation using behavioral constraints from activity classification.Nature communications · 2025Article
- A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor.Sensors (Basel, Switzerland) · 2023Article
- Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.Sensors (Basel, Switzerland) · 2022Article
- Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data.Sensors (Basel, Switzerland) · 2021Article
- Smartphone-Based Inertial Odometry for Blind Walkers.Sensors (Basel, Switzerland) · 2021Article
- Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.Sensors (Basel, Switzerland) · 2021Article
- Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.
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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.