Zero Velocity Detection Without Motion Pre-Classification: Uniform AI Model for All Pedestrian Motions (UMAM)
| Autor: | Yacouba Kone, Ni Zhu, Valérie Renaudin |
|---|---|
| Přispěvatelé: | Géolocalisation (AME-GEOLOC), Université Gustave Eiffel |
| Rok vydání: | 2022 |
| Předmět: |
Computer science
ZERO-VELOCITY DETECTION LEGGED LOCOMOTION 01 natural sciences Field (computer science) EXTRACTION DE CARACTERISTIQUES [SPI]Engineering Sciences [physics] Velocity Moments ZERO VELOCITY UPDATE SENSORS UNITE DE MESURE INERTIELLE (IMU) Instrumentation CHAUSSURES ESSAI EN MILIEU OUVERT Detector Zero (complex analysis) LOCOMOTION SUR PIED UNITE DE MESURE PEDESTRIAN NAVIGATION SITES D&apos NAVIGATION MACHINE LEARNING Algorithm OPEN AREA TEST SITES FEATURE EXTRACTION FOOT CAPTEURS ESSAI Computation AUTOMATIQUE DETECTION DE LA VITESSE NULLE INERTIAL SENSORS APPRENTISSAGE FOOTWEAR DISPOSITIFS DE POSITIONNEMENT MONTES SUR LE PIED PIETON Set (abstract data type) MISE A JOUR DE LA VITESSE NULLE Inertial measurement unit NAVIGATION PEDESTRE PIED CAPTEUR Electrical and Electronic Engineering CAPTEURS INERTIELS APPRENTISSAGE AUTOMATIQUE Propagation of uncertainty FOOT-MOUNTED POSITIONING DEVICES NAVIGATION INERTIELLE 010401 analytical chemistry CAPTEUR INERTIEL 0104 chemical sciences INERTIAL MEASUREMENT UNIT (IMU) |
| Zdroj: | IEEE Sensors Journal IEEE Sensors Journal, Institute of Electrical and Electronics Engineers, 2021, 9 p. ⟨10.1109/JSEN.2021.3099860⟩ |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2021.3099860 |
| Popis: | Foot-mounted positioning devices are becoming more and more popular in the different application field. For example, inertial sensors are now embedded in safety shoes to monitor security. They allow positioning with zero velocity update to bound the error growth of foot-mounted inertial sensors. High positioning accuracy depends on robust zero velocity detector (ZVD). Existing Artificial Intelligent (AI)-based methods classify the pedestrian dynamics to adjust ZVD at the cost of high computation costs and error propagation from miss-classification. We propose a machine learning model to detect zero velocity moments without any pre-classification step, named Uniform AI Model for All pedestrian Motions (UMAM). Performance is evaluated by benchmarking on two new subjects of opposite gender and different size, not included in the training data set, over complex indoor/outdoor paths of 2 km for subject 1 and 2.1 km for subject 2. We obtain an average 2D loop closure error of less than 0.37%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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