THONNAT , Monique

In this paper, we introduce a new approach for Activities of Daily Living (ADL) recognition. In order to discriminate between activities with similar appearance and motion, we focus on their temporal structure. Actions with subtle and similar motion are hard to disambiguate since long-range temporal information is hard to encode. So, we propose...

In this paper, we present a new attention model for the recognition of human action from RGB-D videos. We propose an attention mechanism based on 3D articulated pose. The objective is to focus on the most relevant body parts involved in the action. For action classification, we propose a classification network compounded of spatio-temporal...

We present a clinical and therapeutic approach aiming to create new care for patients with neurocognitive disorders (NCD). Enriched environment including physical and cognitive stimulation improves cognitive functions 1. The aim of this paper is to describe a method for non-pharmacological management of NCD using serious exergames (SEG). SEG...

To investigate the accuracy of deep learning methods applied to seizure video data, in discriminating individual semiologic features of dystonia and emotion in epileptic seizures. A dataset of epileptic seizure videos was used from patients explored with stereo-EEG for focal pharmacoresistant epilepsy. All patients had hyperkinetic (HKN)...

In this work, we propose a multi-stream approach with knowledge distillation to classify epileptic seizures and psychogenic non-epileptic seizures. The proposed framework utilizes multi-stream information from keypoints and appearance from both body and face. We take the detected keypoints through time as spatio-temporal graph and train it with...

Seizure events feature temporary abnormalities in muscle control or movements. They are usually caused by excessive neuronal activities in the brain, and are called epileptic seizures (ES). Nevertheless, not all seizures are epileptic in origin. Some are caused by psychological reasons, and such type of seizures are called psychogenic...

In this paper, we propose a complete framework based on a Hierarchical Activity Models (HAMs) to understand and recognise Activities of Daily Living (ADL) in unstructured scenes. At each particular time of a long-time video, the framework extracts a set of space-time trajectory features describing the global position of an observed person and...

In this paper, we focus on the spatio-temporal aspect of recognizing Activities of Daily Living (ADL). ADL have two specific properties (i) subtle spatio-temporal patterns and (ii) similar visual patterns varying with time. Therefore, ADL may look very similar and often necessitate to look at their fine-grained details to distinguish them....

Objectives.-Rhythmic, stereotyped movements occur in some epileptic seizures. We aimed to document time-evolving frequencies of antero-posterior rocking occurring during prefrontal seizures, using a quantitative video analysis. Methods.-Six seizures from 3 patients with prefrontal epilepsy yet different sublobar local-izations were analyzed...

Activity Recognition from RGB-D videos is still an open problem due to the presence of large varieties of actions. In this work, we propose a new architecture by mixing a high level handcrafted strategy and machine learning techniques. We propose a novel two level fusion strategy to combine features from different cues to address the problem of...

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...

International audience

Population aging has been motivating academic research and industry to develop technologies for the improvement of older people's quality of life, medical diagnosis, and support on frailty cases. Most of available research prototypes for older people monitoring focus on fall detection or gait analysis and rely on wearable, environmental, or...

Population aging has been motivating academic research and industry to develop technologies for the improvement of older people's quality of life, medical diagnosis, and support on frailty cases. Most of available research prototypes for older people monitoring focus on fall detection or gait analysis and rely on wearable, environmental, or...

Patients with Alzheimers disease show cognitive decline commonly associated with psycho-behavioural disorders like depression, apathy and motor behaviour disturbances. However current evaluations of psycho-behavioural disorders are based on interviews and battery of neuropsychological tests with the presence of a clinician. So these evaluations...

In order to fully capture the complexity of the behavioural, functioning and cognitive disturbances in Alzheimer Disease (AD) and related disorders information and communication techniques (ICT), could be of interest. This article presents using 3 clinical cases the feasibility results of an automatic video monitoring system aiming to assess...