Usability results suggest that X-Torp represents a usable EE for healthy subjects and persons with MCI and AD. However, in order to reach moderate or high intensity of aerobic activity, X-Torp control modes should be adapted to become more physically stimulating.

Abstract

We herein present a hierarchical model-based framework for event recognition using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty relies on combining multiple sensors at decision (event) level, and handling their conflict using a probabilistic approach. The proposed approach for event conflict handling computes the event reliability for each sensor, and then combines them using Dempster-Shafer Theory with an alternative combination rule. The proposed framework is evaluated using multi-sensor recording of instrumental daily living activities (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease. Two evaluation cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) by a deterministic fashion, and the combination of conflicting events recognized by video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect®). The results show the framework improves the event recognition rate in both cases.

No abstract

We herein present a hierarchical model-based framework for event detection using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipment) with moving objects (e.g., a Person) detected by a video monitoring system. The event models follow a generic ontology based on natural language, which allows domain experts to easily adapt them. The framework novelty lies on combining multiple sensors at decision (event) level, and handling their conflict using a probabilistic approach. The event conflict handling consists of computing the reliability of each sensor before their fusion using an alternative combination rule for Dempster-Shafer Theory. The framework evaluation is performed on multisensor recording of instrumental activities of daily living (e.g., watching TV, writing a check, preparing tea, organizing week intake of prescribed medication) of participants of a clinical trial for Alzheimer's disease study. Two fusion cases are presented: the combination of events (or activities) from heterogeneous sensors (RGB ambient camera and a wearable inertial sensor) following a deterministic fashion, and the combination of conflicting events from video cameras with partially overlapped field of view (a RGB-and a RGB-D-camera, Kinect). Results showed the framework improves the event detection rate in both cases.