Vladislavs Dovgalecs

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This study investigated a new performance indicator to assess climbing fluency (smoothness of the hip trajectory and orientation of a climber using normalized jerk coefficients) to explore effects of practice and hold design on performance. Eight experienced climbers completed four repetitions of two, 10-m high routes with similar difficulty levels, but(More)
In this paper, we describe a new application for multimedia indexing, using a system that monitors the instrumental activities of daily living to assess the cognitive decline caused by dementia. The system is composed of a wearable camera device designed to capture audio and video data of the instrumental activities of a patient, which is leveraged with(More)
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. New spatio-temporal features, color(More)
This paper presents a method for indexing activities of daily living in videos acquired from wearable cameras. It addresses the problematic of analyzing the complex multimedia data acquired from wearable devices, which has been recently a growing concern due to the increasing amount of this kind of multimedia data. In the context of dementia diagnosis by(More)
In this paper, a method for location recognition in a visual lifelog is presented. Its motivation is the detection of activity related places within an indoor environment to facilitate navigation in the lifelog. It takes advantage of a camera mounted on the shoulder, which is primarily designed for the behavioral analysis of Instrumental Activities of Daily(More)
The analysis of video acquired with a wearable camera is a challenge that multimedia community is facing with the proliferation of such sensors in various applications. In this paper, we focus on the problem of automatic visual place recognition in a weakly constrained environment, targeting the indexing of video streams by topological place recognition. We(More)
In this paper we address the problem of location recognition from visual lifelogs by leveraging visual features and temporal information in an unified framework. The proposed method features a co-training approach that takes advantage of both labeled and unlabeled data using a confidence measure we propose for this task. It exploits jointly two SVM(More)