ArticleSensors (Basel, Switzerland)2018
Pedestrian Dead Reckoning Based on Motion Mode Recognition Using a Smartphone.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bias Normalization for Sensors in Smart Devices.Sensors (Basel, Switzerland) · 2025Article
- Universal Vector Calibration for Orientation-Invariant 3D Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones.Sensors (Basel, Switzerland) · 2024Article
- A Context-Aware Smartphone-Based 3D Indoor Positioning Using Pedestrian Dead Reckoning.Sensors (Basel, Switzerland) · 2022Article
- A Novel Algorithm for Scenario Recognition Based on MEMS Sensors of Smartphone.Micromachines · 2022Article
- Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection.Sensors (Basel, Switzerland) · 2021Article
- Precise detection of early breast tumor using a novel EEMD-based feature extraction approach by UWB microwave.Medical & biological engineering & computing · 2021Article
- Deep Learning-Based Human Activity Real-Time Recognition for Pedestrian Navigation.Sensors (Basel, Switzerland) · 2020Article
- Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation.Sensors (Basel, Switzerland) · 2019Article
- A Multi-User Personal Indoor Localization System Employing Graph-Based Optimization.Sensors (Basel, Switzerland) · 2019Article
- Low-Cost Indoor Positioning Application Based on Map Assistance and Mobile Phone Sensors.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
5 authors.
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Abstract
This paper presents a pedestrian dead reckoning (PDR) approach based on motion mode recognition using a smartphone. The motion mode consists of pedestrian movement state and phone pose. With the support vector machine (SVM) and the decision tree (DT), the arbitrary combinations of movement state and phone pose can be recognized successfully. In the traditional principal component analysis based (PCA-based) method, the obtained horizontal accelerations in one stride time interval cannot be guaranteed to be horizontal and the pedestrian's direction vector will be influenced. To solve this problem, we propose a PCA-based method with global accelerations (PCA-GA) to infer pedestrian's headings. Besides, based on the further analysis of phone poses, an ambiguity elimination method is also developed to calibrate the obtained headings. The results indicate that the recognition accuracy of the combinations of movement states and phone poses can be 92.4%. The 50% and 75% absolute estimation errors of pedestrian's headings are 5.6° and 9.2°, respectively. This novel PCA-GA based method can achieve higher accuracy than traditional PCA-based method and heading offset method. The localization error can reduce to around 3.5 m in a trajectory of 164 m for different movement states and phone poses.
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