Zero Velocity Detection without Motion Pre-classification: Uniform AI Model for All pedestrian Motions (UMAM)
Résumé
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%.
Mots clés
FEATURE EXTRACTION
FOOT
FOOTWEAR
FOOT-MOUNTED POSITIONING DEVICES
INERTIAL SENSORS
INERTIAL MEASUREMENT UNIT (IMU)
LEGGED LOCOMOTION
MACHINE LEARNING
OPEN AREA TEST SITES
PEDESTRIAN NAVIGATION
SENSORS
ZERO-VELOCITY DETECTION
ZERO VELOCITY UPDATE
EXTRACTION DE CARACTERISTIQUES
PIED
CHAUSSURES
DISPOSITIFS DE POSITIONNEMENT MONTES SUR LE PIED
CAPTEURS INERTIELS
UNITE DE MESURE INERTIELLE (IMU)
LOCOMOTION SUR PIED
APPRENTISSAGE AUTOMATIQUE
SITES D&apos
ESSAI EN MILIEU OUVERT
NAVIGATION PEDESTRE
CAPTEURS
DETECTION DE LA VITESSE NULLE
MISE A JOUR DE LA VITESSE NULLE