A5091570983- Biomedical Text Mining and Ontologies
- Research Data Management Practices
- Balance, Gait, and Falls Prevention
- EEG and Brain-Computer Interfaces
- Gait Recognition and Analysis
- Robot Manipulation and Learning
- AI-based Problem Solving and Planning
- Muscle activation and electromyography studies
- Non-Invasive Vital Sign Monitoring
- Parkinson's Disease Mechanisms and Treatments
- Epilepsy research and treatment
- Context-Aware Activity Recognition Systems
- Neuroscience and Neural Engineering
- Neurological disorders and treatments
- Health Systems, Economic Evaluations, Quality of Life
- Time Series Analysis and Forecasting
- 3D Surveying and Cultural Heritage
- Manufacturing Process and Optimization
- Indoor and Outdoor Localization Technologies
- Advanced Chemical Sensor Technologies
- Neuroinflammation and Neurodegeneration Mechanisms
- Animal Vocal Communication and Behavior
- Privacy-Preserving Technologies in Data
- Privacy, Security, and Data Protection
- Biotechnology and Related Fields
UCB Pharma (Belgium)
2017-2022
Applied Radar (United States)
2021
University of Freiburg
2021
KU Leuven
2003-2020
UCB Pharma (Netherlands)
2017
This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting rigid links and joints. The integrates both instantaneous task specification estimation geometric uncertainty in unified framework. Major components are the use feature coordinates, defined with respect object frames, which facilitate specification, introduction coordinates model uncertainty. While focus is on an existing velocity- based control scheme...
Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with ECG using an existing algorithm. This algorithm classifies basis heart rate features, extracted from increase. was applied recordings 11 patients setting...
Wearable devices can capture objective day-to-day data about Parkinson’s Disease (PD). This study aims to assess the feasibility of implementing wearable technology collect from multiple sensors during daily lives PD patients. The Parkinson@home is an observational, two-cohort (North America, NAM; Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria self-reported diagnosis PD, possession a smartphone and age≥18 years....
A wearable electroencephalogram (EEG) device for continuous monitoring of patients suffering from epilepsy would provide valuable information the management disease. Currently no EEG setup is small and unobtrusive enough to be used in daily life. Recording behind ear could prove a solution setup. This article examines feasibility recording epileptic ear. It achieved by comparison with scalp recordings. Traditional behind-the-ear were simultaneously acquired 12 temporal, parietal, or...
Background Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies date conducted highly controlled laboratory environments. Objective This paper aims assess whether sensor-based analysis of real-life can be objectively and remotely monitor motor fluctuations PD. Methods The Parkinson@Home validation study provides a new reference data set for the development digital biomarkers persons PD daily life....
Digital health technologies (smartphones, smartwatches, and other body-worn sensors) can act as novel tools to aid in the diagnosis remote objective monitoring of an individual's disease symptoms, both clinical care research. Nonetheless, such digital have yet widely demonstrate value research due insufficient data interpretability lack regulatory acceptance. Metadata, i.e., that accompany describe primary data, be utilized better understand context sensor assist management, sharing,...
Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help preclinical drug development. The purpose this paper is to establish a computational gait approach for the Noldus Catwalk system, which footprints are automatically captured stored.We present - our knowledge first machine learning based comprises step decomposition, definition extraction meaningful features, multivariate sequence alignment, feature selection, training different...
Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for epileptic seizures. However, is also needed day-to-day lives of people with epilepsy, where video not feasible. Wearables could fill this gap by providing patients an accurate log their seizures.Although there already systems available that provide promising results detection tonic-clonic seizures (TCSs), research area often limited to from 1 biosignal modality or only during night...
Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because the possibility to combine datasets different studies, it with other types, and reuse multiple times for various purposes. To date, there exist no standards capturing or storing DHT biosensor applicable across modalities disease areas, which can also capture trial environment-specific aspects, so-called metadata. In this perspectives paper, we propose metadata...
Understanding the mechanisms of epileptogenesis is essential to develop novel drugs that could prevent or modify disease. Neuroinflammation has been proposed as a promising target for therapeutic interventions inhibit epileptogenic process evolves from traumatic brain injury. However, it remains unclear whether cytokine-related pathways, particularly TNFα signaling, have critical role in development epilepsy. In this study, we investigated innate inflammation an vitro model post-traumatic...
iTASC (acronym for dasiainstantaneous task specification and controlpsila) by J. De Schutter (2007) is a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems. integrates both instantaneous estimation geometric uncertainty in unified framework. Automatic derivation controller estimator equations follows from model that obtained using modeling procedure. The applies large variety systems (mobile robots, multiple systems, dynamic human-robot...
The use of wearable sensing technology for objective, non-invasive and remote clinimetric testing symptoms has considerable potential. However, the accuracy achievable with such is highly reliant on separating useful from irrelevant sensor data. Monitoring patient using digital sensors outside controlled, clinical lab settings creates a variety practical challenges, as recording unexpected user behaviors. These behaviors often violate assumptions protocols, where these protocols are designed...
Passive monitoring in daily life may provide valuable insights into a person's health throughout the day. Wearable sensor devices play key role enabling such non-obtrusive fashion. However, data collected reflect multiple and behavior-related factors together. This creates need for structured principled analysis to produce reliable interpretable predictions that can be used support clinical diagnosis treatment. In this work we develop modelling approach free-living gait (walking) analysis....
Abstract The European General Data Protection Regulation ( GDPR ) has dotted the i’s and crossed t’s in context of academic medical research. One year into , it is clear that a change mind uptake new procedures required. Research organisations have been looking at possibility to establish code-of-conduct, good practices and/or guidelines for researchers translate ’s abstract principles concrete measures suitable implementation. We introduce proposal implementation research which involves...
Passive and non-obtrusive health monitoring using wearables can potentially bring new insights into the user's status throughout day may support clinical diagnosis treatment. However, identifying segments of free-living data that sufficiently reflect is challenging. In this work we have studied problem modelling real-life gait which a very indicative behaviour for multiple movement disorders including Parkinson's disease (PD). We developed probabilistic framework unsupervised analysis gait,...
This paper describes how to automatically extract the presence and location of geometrical irregularities on a surface revolution. To this end partial 3D scan workpiece under consideration is acquired by structured light ranging. The application we focus detection removal burrs industrial workpieces. Cylindrical metallic objects cause strong specular reflection in every direction. These highlights are compensated for projected patterns, hence 'adaptive scanning'. triangular mesh produced...
<sec> <title>BACKGROUND</title> Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for epileptic seizures. However, is also needed day-to-day lives of people with epilepsy, where video not feasible. Wearables could fill this gap by providing patients an accurate log their </sec> <title>OBJECTIVE</title> Although there already systems available that provide promising results detection tonic-clonic seizures (TCSs), research area often...
Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts digital sensor data, for which, a consensus on common storage, exchange archival data format standard, has yet be reached. To address this gap, we propose Time Series Data Format (TSDF): unified, standardized storing all types across diverse disease areas. We pose series design criteria review...