About the lab
EuroMov Digital Health in Motion (EuroMov DHM) is a scientific merger between researchers interested in the discovery of the sensorimotor markers of health (mainly from the research unit ‘Movement to Heath’ or M2H host at EuroMov building from Univ. Montpellier) and researchers in cognitive automation (mainly from Laboratoire de Génie Informatique et d’Ingénierie de Production, LGI2P from IMT Mines Alès).
Featured research (20)
Background Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on haemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients. Methods Twenty-one healthy old adults and 21 post-stroke patients were recruited and realized two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform. Results and conclusions Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.
This symposium addresses the contributions and impacts of digital tools in high-level sport via combined perspectives in physiology, biomechanics, communication systems/electronics and computer science/data sciences.
Notio is a device based on a wind sensor which offers estimates of the CdA (drag coefficient multiplied by the area) of the pair cyclist and bike. Notio is used with a specific analysis software, which computes CdA estimates after a ride. The Aeroscale Company proposes a half-day service with their own wind sensor and experimental protocol, to also deliver estimates of the CdA. In both cases, the main objective of a wind sensor is to give estimates in outdoor conditions. The aeroscale specificity is that all experiments are done without any power sensor, in freewheel. In our study, we experimented Notio device and software as well as Aeroscale Service through an incremental protocol with increasing disks, which led us to obtain sensitivity measure precisions of 4.8% for Notio and 0.5% for Aeroscale, with good reliabilities (ICC=0.98 for Notio and 0.93 for Aeroscale).
Dementia is a neurological disorder associated with aging that can cause a loss of cognitive functions, impacting daily life. Alzheimer's disease (AD) is the most common cause of dementia, accounting for 50–70% of cases, while frontotemporal dementia (FTD) affects social skills and personality. Electroencephalography (EEG) provides an effective tool to study the effects of AD on the brain. In this study, we propose to use shallow neural networks applied to two sets of features: spectral-temporal and functional connectivity using four methods. We compare three supervised machine learning techniques to the CNN models to classify EEG signals of AD / FTD and control cases. We also evaluate different measures of functional connectivity from common EEG frequency bands considering multiple thresholds. Results showed that the shallow CNN-based models achieved the highest accuracy of 94.54% with AEC in test dataset when considering all connections, outperforming conventional methods and providing potentially an additional early dementia diagnosis tool.
- Faculté des Sciences du Sport (STAPS)
About Stephane Perrey
- My current researches are in the centre of issues, which have for objectives to identify, quantify and explain mechanisms responsible for changes of the motor function in humans. Especially, functional investigation of the brain during various motor tasks involving fatigue is realized by using electrophysiological and near-infrared spectroscopy signals.