Kick-scooters detection in sensor-based transportation mode classification methods

International audienceIn this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser's pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation

Inertial navigation, context awareness, online detection, indoor mapping, particle filtering, data fusion

La navigation inertielle grâce aux capteurs intégrés dans les smartphones permet d’assurer une géolocalisation continue même en absence de signal GNSS. Ces capteurs bas coût délivrent néanmoins des mesures bruitées qui engendrent une dérive de la trajectoire. La technique PDR qui est une technique de navigation inertielle par détection de pas souffre de deux limites principales. La première est l’estimation de la longueur de pas car cette dernière dépend des caractéristiques physiques de chaque utilisateur, et la seconde est le résultat d’une dérive angulaire combinée avec un biais lié au portage du capteur à la main. Dans le contexte du projet HAPPYHAND, ce travail s’intéresse à l’exploitation de la carte pour corriger ces différentes erreurs. Un réseau de navigation topologique est exploité pour corriger à la fois les erreurs angulaires et calibrer le modèle de longueur de pas. Ce modèle est ensuite augmenté par un processus de mise à jour de position par détection de points d’intérêt.Smartphone navigation using the low-cost embedded sensors in off the shelf smartphones can provide a continuous solution in GNSS-denied environments. The most widely adopted approach is Pedestrian Dead Reckoning (PDR) that uses acceleration and angular velocity to estimate the user’s position. Yet, consumer grade sensors deliver noisy measurements that may result into a drift in the estimated trajectory. One major challenge is to estimate accurately step length information since it depends on physiological features that are specific to each user. In addition, angular biases are more likely to be introduced in the orientation estimation process with handheld devices. This is mainly due to the high degree of freedom of hand motion. In the context of a national project called HAPPYHAND, the main goal of this work is to exploit map information as far as possible in order to mitigate the previous inherent limitations to the PDR approach. First, a topological network extracted from the map is proposed in order to correct the angular errors and calibrate the step length model. Second, context awareness is adopted in order to provide regular and frequent position updates thanks to a point of interest online detection scheme

Navigation des personnes aux moyens des technologies des smartphones et des données d’environnements cartographiés

Smartphone navigation using the low-cost embedded sensors in off the shelf smartphones can provide a continuous solution in GNSS-denied environments. The most widely adopted approach is Pedestrian Dead Reckoning (PDR) that uses acceleration and angular velocity to estimate the user’s position. Yet, consumer grade sensors deliver noisy measurements that may result into a drift in the estimated trajectory. One major challenge is to estimate accurately step length information since it depends on physiological features that are specific to each user. In addition, angular biases are more likely to be introduced in the orientation estimation process with handheld devices. This is mainly due to the high degree of freedom of hand motion. In the context of a national project called HAPPYHAND, the main goal of this work is to exploit map information as far as possible in order to mitigate the previous inherent limitations to the PDR approach. First, a topological network extracted from the map is proposed in order to correct the angular errors and calibrate the step length model. Second, context awareness is adopted in order to provide regular and frequent position updates thanks to a point of interest online detection scheme.La navigation inertielle grâce aux capteurs intégrés dans les smartphones permet d’assurer une géolocalisation continue même en absence de signal GNSS. Ces capteurs bas coût délivrent néanmoins des mesures bruitées qui engendrent une dérive de la trajectoire. La technique PDR qui est une technique de navigation inertielle par détection de pas souffre de deux limites principales. La première est l’estimation de la longueur de pas car cette dernière dépend des caractéristiques physiques de chaque utilisateur, et la seconde est le résultat d’une dérive angulaire combinée avec un biais lié au portage du capteur à la main. Dans le contexte du projet HAPPYHAND, ce travail s’intéresse à l’exploitation de la carte pour corriger ces différentes erreurs. Un réseau de navigation topologique est exploité pour corriger à la fois les erreurs angulaires et calibrer le modèle de longueur de pas. Ce modèle est ensuite augmenté par un processus de mise à jour de position par détection de points d’intérêt

Points of Interest Detection for Map-Aided PDR in Combined Outdoor-Indoor spaces

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), SAPPORO, JAPON, 18-/09/2017 - 21/09/2017Complementary data are necessary to bind the positioning error growth of Pedestrian Dead Reckoning (PDR). In this paper, absolute position updates are made possible with the online detection of different types of points of interest (POIs) located on the map. The POIs are selected depending on specific motion patterns which are associated to absolute locations on the map. To create the POIs database, the correlation between pedestrian motion and different map locations is first studied and the outcome is a typology of POIs. A K-NN (K nearest neighbors) algorithm is used to train different motion modes, which are further exploited for the detection of POIs in order to update the PDR algorithm with position data. Experimental assessment of the POI-based PDR calibration is conducted in both outdoor and indoor spaces with a focus on the transition between both environments. 90% of the time, motion is correctly classified and the PDR position is corrected with an accuracy that depends on POIs features (width of corridor/door, staircase size...). Therefore, the approach is found to be promising for enhancing PDR positioning using only map data

Kick-scooters identification in the context of transportation mode detection using inertial sensors: Methods and accuracy

International audienceThis work presents a novel transportation mode detection algorithm that handles the recognition of kick-scooters. In 2015, 10 minutes of data from a kick-scooter were considered in a transportation mode detection study, yielding a 56% F1-score. Since then, kick-scooters were not given much attention. Yet, kick-scooters are now very present in the urban transportation ecosystem, and their consideration in transportation studies has become a must. To fill this gap, 4 hours of kick-scooter signals were collected by 18 participants, with a set of 6 different kick-scooters, using 3 body-worn inertial measurement units. Obviously, kick-scooter patterns are classified in contrast with other modes of transportation. Two classification scenarios are considered in order to gradually increase the classification model complexity. The first scenario includes walking, biking, and kick-scooter, while the second considers public transport (tramway and bus) in addition to the former transportation modes. Results show that kick-scooters can be detected with an F1-score of 80% in the first scenario. Walking and public transport samples were still accurately classified in the second scenario, with an F1-score above 80% for both classes. However, bike and kick-scooter samples were both classified with lower F1-scores, equal to 59% and 64% respectively. Therefore, the main focus of future works should be directed toward the separability of kick-scooters and bikes when public transport is considered. The findings also suggest to place preferably the sensors in the trouser’s pocket, allowing for leg motion to be finely captured

Kick-scooters detection in sensor-based transportation mode classification methods

International audienceIn this work we present a novel classification model that can detect kick-scooters from inertial and pressure sensors. The detection is performed with kick-scooters being trained with other activities and transportation modes including still, walking, biking, taking bus and tramway. Results show that kick-scooters can be precisely detected up to 99% for three different sensor placements: on-foot, waist-attached and in the trouser's pocket. Thus, this paper provides a first contribution where kick-scooters can be classified and studied for further applications such as mobility behavior analysis and navigation