RAS is currently being tested as a rehabilitation strategy for people suffering from Parkinson's Disease (PD) [1]. Through this internship, we will develop a model of this protocol using the framework of coordination dynamics. Namely, we will inspire from the Haken-Kelso-Bunz (HKB) model [2], with a time-dependent forcing signal mimicking the RAS. We will study the dynamic states that such a model is capable of producing, focusing on obtaining a stabilizing effect of the RAS, deriving a map of the parameter space. Then, we will calibrate the model with real data from BeatMove [3] in order to assess the parameter region where movement stabilization can be obtained. In a parallel approach, we will investigate the potential higher-order interaction effect of the RAS [4]. To this end, we will consider a coupled hybrid oscillator of van der Pol / Rayleigh [2] (from which the HKB model was derived) for limb coordination, and apply the RAS on the link between each node/oscillator, hence representing a time-periodic modulation of the connection between the two limbs. Similar to the previous approach, we will calibrate this coupled model and map its parameter space in order to unveil how to best enhance movement stability.

[1] https://doi.org/10.1038/s41531-024-00852-6

[2] https://doi.org/10.1007/s00422-021-00890-w

[3] https://www.beatmove.fr/en/

[4] https://doi.org/10.1038/s41593-022-01070-0

Modulating Motor Stereotypy through Auditory-Motor Synchronization

Loïc Damm (EuroMov DHM) (with Laurie Galvan and Sofiane Ramdani)

This interdisciplinary project investigates how auditory-motor synchronization (AMS) can modulate motor stereotypy—repetitive and rhythmical behaviors commonly observed in neurodevelopmental disorders such as autism. Grounded in sensorimotor integration theory and neurophysiological insights into parvalbumin (PV) interneuron dysfunction, the study explores whether AMS training can alter gamma-band oscillations (GBO) and promote motor repertoire enrichment. The internship will offer a unique opportunity to:

(i) explore mechanisms underlying stereotyped behaviors and their cortical correlates;

(ii) engage in human-subjects experimentation with EEG and motion capture tools;

(iii) analyze complex data (kinematics, spectral EEG, nonlinear measures);

(iv) contribute to the development of therapeutic strategies targeting sensorimotor plasticity.

The project bridges neuroscience, digital health, and embodied interaction, with potential applications in clinical interventions for ASD and related conditions.

https://doi.org/10.1177/13623613221105479

https://doi.org/10.1093/pnasnexus/pgae132

Determinants of Rescuer Endurance During Cardiopulmonary Resuscitation

Loïc Damm (EuroMov DHM) (with Stefan Janaqi)

Current CPR guidelines recommend that rescuers rotate every two minutes because chest compression quality typically declines as fatigue develops. However, switching rescuers is not always possible, particularly in out-of-hospital settings. Interestingly, some individuals are able to maintain high-quality compressions for much longer periods with minimal performance degradation. This project aims to identify the physiological, biomechanical, and anthropometric factors that determine rescuer endurance during prolonged CPR. Particular attention will be given to fatigue resistance, physical fitness, body composition, and movement strategies. Machine learning approaches will be used to identify the key determinants of performance and predict which individuals are most likely to sustain effective chest compressions over time. Understanding the characteristics of these high-performing rescuers could improve training strategies, team organization, and ultimately patient outcomes during cardiac arrest.

https://doi.org/10.1161/CIR.0000000000001369

https://doi.org/10.1016/j.acepjo.2026.100395

Gesture-speech synchronization in dyad

Ludovic MARIN (EuroMov DHM) (with Patrice GUYOT)

The SYNCOGEST project is an ANR based funding aiming to model spontaneous gestures in face-to-face interactions to enhance the naturalness of embodied conversational agents. The master project will be part of the Syncogest consortium and will conduct a multimodal data collection (audio, video, MOCAP) of a dyadic corpus to analyze gesture-speech synchronizations. Twenty dyads will be scrutinized when interacting together in an open discussion. The goal of the master will be to capture and analyze gestures and body motions that are relevant for nonverbal communication. The gestures will be first annotated by linguists using the method of Rohrer et al. (2023), while the master will analyze the kinematic properties (velocity, amplitude, etc.) of each dyad. Hence, in a second part, the master will help building along with experts in AI, the automation of gesture segmentation using the method of Lozano-Goupil et al. (2022) in order to develop a general methodology of extraction of relevant movements for any dyadic conversations. This work will contribute to improving the modeling of human-like co-speech gestures in conversational AI

https://doi.org/10.1177/25152459221140842

https://doi.org/10.1016/j.neuropsychologia.2022.108347

Towards a User-friendly Agent-based Model of Approach and Avoidance Dynamics Regarding Physical Activity for Health

Christophe Gernigon (EuroMov DHM) (with Sylvain Vauttier and Rémi Altamore)

For people who have been physically inactive for a long time, regular physical activity goals can be either rewarding challenges to be met, or threats to self-esteem to be avoided by all manner of excuses. The dynamics of approach and avoidance motivations that result from such perceptions have just been modeled as agent-based models (ABM). However, this ABM is still predominantly formal and has only been validated in a competitive sport context. The aim of this master's internship will be to develop a user-friendly Netlogo-type (http://ccl.northwestern.edu/netlogo) computer version of the ABM, and to validate this version by comparing the statistical properties of its simulation outputs with those of longitudinal data from people who need to engage in regular physical activity for their health.

References:

https://doi.org/10.1037/gpr0000055

https://doi.org/10.1177/1948550617691100

Characterizing the microstructure of reaching movements after a stroke for better rehabilitation

Denis MOTTET, Karima BAKHTI and Emmanuel GUIGON

(EuroMov DHM, CHU Montpellier, ISIR Paris)

Rehabilitation is key to sensorimotor recovery after stroke. Yet, it is still unclear how to tailor the nature and the dose of the therapy to the exact functional deficits of each individual patient.

Does characterizing the microstructure of reaching movements for each patient help to better identify his/her specific deficit to improve his/her rehabilitation?

In this internship, we will monitor the microstructure of reaching movements in people with stroke and healthy controls, and interpret the observed deficits with a biologically plausible computational model of sensorimotor control calibrated for each patient.

References:

https://doi.org/10.1016/j.apmr.2013.10.006

https://hal.archives-ouvertes.fr/hal-03276320v2

Clinical Validation of Visuomotor Performance Metrics in Post-Stroke Rehabilitation

Karima BAKHTI (EuroMov DHM) (with Makii MUTHALIB et Denis MOTTET)

This project aims to evaluate upper limb performance markers, such as speed and precision, during functional daily movements in post-stroke individuals. We will analyze the kinematics of visuomotor tasks involving a speed-accuracy trade-off (circular steering) with continuous feedback. Measurements have been conducted using a graphic tablet and a Kinect sensor. The goal is to assess the feasibility and relevance of these tools in a clinical setting. A comparison will be made between the performances of post-stroke patients and healthy subjects. We aim to validate the sensitivity of this instrumented evaluation against clinical scores, particularly the Wolf Motor Function Test and The Block and Box Test. This approach will help better quantify motor deficits and rehabilitation progress. It will contribute to refining physiotherapy management. The protocol will include statistical analyses of correlation and discrimination. This internship is part of a translational effort toward clinical practice.

Références

doi: 10.1177/15459683251331582

https://doi.org/10.1186/s13063-021-05689-5

Predicting Real-World Arm Use After Stroke: From Clinical Data to Smart Models

Karima BAKHTI (EuroMov DHM) (with Makii MUTHALIB and Nicolas SUTTON-CHARANI)

This internship focuses on developing predictive models of real-world arm use in daily life among post-stroke patients, using already collected clinical and contextual data. Arm use is measured via accelerometry (funcUseRatio), while predictors include motor scores (Fugl-Meyer), functional tests (Wolf Motor Function Test, Box and Block Test), autonomy (Barthel Index), and a semi-structured interview exploring barriers to upper limb use. The intern will apply supervised learning methods to train models on complete datasets and generate predictions in cases where accelerometric data are unavailable. The main goal is to identify predictors associated with non-use of the paretic upper limb in daily activities. This approach will help reveal discrepancies between expected and actual use, shedding light on behavioral or environmental factors. The project aims to refine personalized rehabilitation strategies while accounting for uncertainty in the data.

Références :

doi : 10.1109/ICMLA.2013.26

https://doi.org/10.1186/s13063-021-05689-5

Evidential fusion of actimetric data coming from several sensors

Nicolas Sutton-Charani (EuroMov DHM)

The theory of belief function proposes a formal framework where uncertainty can be modelled with different levels leveraging the adaptation of uncertain models to the uncertainty type (aleatory or epistemic). In this framework several combination rules have been proposed that can imply the fusion of the information collected through different sensors. This internship will compare different evidential fusion strategies (concatenation, conjunctive, disjunctive) inside ML models in the activity recognition perspective.

References

Wenjun Ma, Yuncheng Jiang, Xudong Luo, A flexible rule for evidential combination in Dempster–Shafer theory of evidence, Applied Soft Computing, Volume 85, 2019, 105512, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2019.105512.

Shafer, Glenn. A Mathematical Theory of Evidence. Princeton University Press, 1976. JSTOR, https://doi.org/10.2307/j.ctv10vm1qb.

Mutual Impact of Hip and Knee Joint configurations on Torque production

François Bailly and Christine Azevedo (INRIA, CAMIN)

While studies of the human movement often focus on joint torques and kinematics, many isolate single joints, overlooking multi-joint interactions. In this internship, we want to explore the impact of hip joint configurations on the knee and conversely. For that purpose, we already acquired data from fourteen participants (7 females, 7 males) performing maximal voluntary contractions on a dynamometer with their right hip and knee. Four types of acquisitions were performed: passive, isometric, isokinetic concentric and isokinetic eccentric acquisitions for several hip and knee angle configurations. The goal of this internship is to organize and preprocess raw data collected during biomechanical experiments, develop scripts or pipelines for data cleaning and normalization, create insightful visualizations to represent joint kinetics/kinematics and compare to literature data. Finally, we expect the intern to assist in the biomechanical interpretation of findings.

https://doi.org/10.1016/j.jbiomech.2007.03.022

https://doi.org/10.1186/s40798-021-00330-w

Ground-Truthing Posture: Validating Markerless Video for Early Detection of Writing Disorders

Lauren Sismeiro, Binbin Xu, Frédéric Puyjarinet and Gérard Dray (EuroMov DHM, Montpellier)

Contact : lauren.sismeiro@mines-ales.fr ; gerard.dray@umontpellier.fr

AVIAREPTE (Analyse Vidéo et Intelligence Artificielle pour le Repérage Précoce des Troubles de l’Écriture) is developing a low-cost, camera-based system to detect early signs of dysgraphia in children. Beyond hand movement, posture is a key clinical marker, but markerless video estimation (MediaPipe, OpenCV) has never been validated against a gold standard for this task. In this internship, you will use 3D motion capture to build ground-truth references for body, hand and wrist posture during handwriting, and quantify how accurately our camera-based pipeline recovers them, using ergonomic scores inspired by REBA/RULA. The validated pipeline will then be tested on an existing adult dataset and, ultimately, on real school-based acquisitions with children, where atypical postures are expected. A hands-on project combining motion capture, computer vision and applied clinical research, with direct translational impact for non-invasive screening in schools.

Maggioni, V.; Azevedo-Coste, C.; Durand, S.; Bailly, F. Optimisation and Comparison of Markerless and Marker-Based Motion Capture Methods for Hand and Finger Movement Analysis. Sensors 2025, 25, 1079. https://doi.org/10.3390/s25041079

Wang, C.; Carnieto Tozadore, D.; Bruno, B.; Dillenbourg, P. Quantitative analysis of the correlation between body posture quality assessment scores and handwriting quality measures. Scientific Reports 2025, 15, 1-14. https://doi.org/10.1038/s41598-025-08405-4

Cortical Dynamics of a Proactive Inhibition During Overground Locomotion:A simultaneous mobile EEG–fNIRS study

Stéphane Perrey (EuroMov DHM), Lénaic Borot (IMT Mines Ales) Binbin Xu (EuroMov DHM), Samy-Nicolas Castro Novoa (EuroMov DHM), Daghan Piskin (Univ Parderborn, Germany), Jochen Baumeister (Univ Parderborn, Germany)

Everyday locomotion requires pedestrians to maintain a state of proactive stopping readiness while preserving forward movement.

Although this state is critical for safe navigation in unpredictable environments, its underlying cortical mechanisms remain insufficiently characterized.

Our recent mobile EEG study demonstrated that anticipated stopping increases step-interval variability and alters gait-phase-modulated spectral dynamics.

Specifically, cautious walking was associated with attenuated and delayed left frontotemporal theta desynchronization and reorganized centroparietal alpha- and beta-band synchronization.

The proposed study will replicate and extend these findings through simultaneous mobile EEG–fNIRS during ecologically valid overground walking.

Healthy adults will perform fluent and cautious walking conditions while anticipating a potential stop, with heel-strike events enabling gait-cycle alignment.

EEG will characterize rapid, phase-specific oscillatory dynamics, whereas fNIRS will quantify condition-related hemodynamic responses across cortical regions.

Spatial coverage will prioritize bilateral prefrontal and premotor cortices, with sensorimotor and parietal regions included where technically feasible.

Multimodal analyses will determine whether EEG reorganization is accompanied by altered hemodynamic recruitment and whether these responses relate to gait variability.

By integrating temporal and spatial neurophysiological measures, the study will advance understanding of proactive inhibitory control in locomotion and establish a foundation for research in aging and neurological populations.

Modelizing the recurrency of a new physical activity habit assessed by accelerometer

Julie Boiché (with Emmanuel Le Clezio and Rémy Dadier)

Euromov DHM - IES

Julie.boiche [@] umontpellier.fr

While controlled psychological processes are deemed essential to drive the initiation of structured and newly adopted behaviors, automatic factors play a major role in their maintenance through time. There are few longitudinal studies reporting data in individuals who regularly adopt a new physical activity habit, and all of them used self-report tools to assess automatic processes (i.e., habits scores) and behavior adoption. The purpose of the internship will be to (1) recruit a sample of healthy individuals that intent to adopt a new active routine on a regular basis and will be equipped with a GT3X accelerometer during a 4-week follow-up ; (2) use classification techniques to track the adoption of the new PA behavior and (3) conduct time-series analyses to estimate the increase of objectively assessed behavior adoption through time.

The internship will be supervised by a mixed team from the Euromov and IES laboratories, respectively specialists in human movement and the analysis of temporal signals.

References

https://doi.org/10.1016/j.psychsport.2018.12.007

https://doi.org/10.1111/sms.12730

doi:10.51257/a-v1-r1815

doi : 10.51257/a-v1-te5220

https://www.machinelearningplus.com/time-series/time-series-analysis-python/

Modulation of Working Memory by an Alternating Magnetic Field in Humans

Sofiane Ramdani with Maëlys Moulin and Nicolas Bouisset

LIRMM IDH, EuroMov DHM

Working memory enables the temporary maintenance and manipulation of information required to perform a task. It relies on dynamic interactions within distributed brain networks, particularly frontoparietal networks, involving neural oscillations in the theta (4-7 Hz) and gamma (30-80 Hz) frequency bands [1]. Gamma oscillations have notably been associated with the active maintenance of information and memory load, making them a relevant target for rhythmic neuromodulation techniques.

Transcranial alternating current stimulation (tACS) applies weak sinusoidal currents through the scalp to interact with ongoing brain oscillations. Although some studies have reported changes in working memory performance, the observed effects are generally small and heterogeneous. Their interpretation is also limited by variability in intracranial electric fields and by the cutaneous or visual sensations associated with current delivery.

Transcranial alternating magnetic stimulation (tAMS) is an emerging rhythmic neuromodulation approach whose potential cognitive effects have yet to be established. It uses a sinusoidal magnetic field to induce electric fields within biological tissues through electromagnetic induction, without directly injecting current through scalp electrodes. To date, human tAMS research has focused primarily on magnetophosphene perception and exposure thresholds [2], while its capacity to modulate higher-order cognitive functions, including working memory, remains largely unexplored.

The MEMOCHAM project therefore investigates a novel cognitive application of tAMS and provides the first direct comparison with tACS within a common experimental framework. It will compare the effects of tACS and tAMS at 40 and 60 Hz on working memory in 102 healthy adults. Participants will be assigned to one of five experimental groups: 40-Hz tACS, 60-Hz tACS, 40-Hz tAMS, 60-Hz tAMS, or a no-stimulation control condition. Working memory will be assessed before, during, and after stimulation using a verbal Sternberg task. Behavioral performance will be related to changes in EEG activity recorded before and after stimulation. This protocol will determine whether the observed effects depend on the stimulation modality or frequency and assess their robustness within a preregistered experimental framework.

https://doi.org/10.3389/fpsyg.2018.00401

https://doi.org/10.1016/j.brs.2024.05.004

Critical force prediction during squat exercise: a multimodal approach

Lénaïc Borot (with Stéphane Perrey and Nicolas Sutton-Charani)

EuroMov DHM

Prediction of fatigue in sporting activities is representing a major challenge independently of practicing level. Fatigue is a progressive process involving sharing information from peripheral and central systems. This process begins as soon as exercise starts and delaying the onset of fatigue symptoms is one of the major goals of training. The purpose of this internship is to use a multimodal approach including peripheral measurements (EMG, muscular NIRS) and central measurements (fNIRS neuroimaging) during an all-out sub-maximal squat exercise. The analysis will be combined with explainable machine learning regression methods that can take into account temporal evolution of extracted features.

The internship will be part of the LabCom Oxymove which brings together the EuroMov DHM laboratory and the company Semaxone.

Vergotte, G., Sutton-Charani, N., Lacerenza, M., Pla, S., Besson, P., Perrey, S. (2025).

Prediction of Critical Force Based on Time-Domain Near Infrared Spectroscopy and

Electromyography With Explainable Machine Learning. ISOTT 2025-annual meeting of the International Society on Oxygen Transport, 24-28 August, Thessalonik, Greece

Live hand motion estimation using a synchronized multi-camera system

François Bailly (with Christine Azevedo-Coste), INRIA

Deep-learning-based hand kinematics capturing tools do not perform well in difficult conditions. Non-standard hands (e.g., spastic hands), along with finger occlusions or overlapping medical apparatus (such as orthoses) which hinder reliable keypoint detection [a] prevent the use of such tools in specific clinical contexts. To overcome these limitations, our research team designed a low-cost ring-like 10-cameras setup to simultaneously capture as many hand viewpoints as possible (1080p, 30 fps) and lower reconstruction errors. The calibration is performed using a method adapted for multiple cameras with no joint views described in [b]. On the software side, the team developed a live acquisition software which dynamically selects the optimal subset of cameras to estimate the motion of the hand most precisely. The internship will consist in testing and optimizing this software on data acquired on 10 tetraplegic participants having participated in a clinical trial. Very good coding skills (Python) are required, knowledge in motion capture is strongly recommended.

[a] Fanbin Gu, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Frontiers in Neurology, 13, December 2022.

[b] Oguz Kedilioglu, et al. Pricosa: High-precision 3d camera calibration with non-overlapping field of views. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP, pages 801–809. INSTICC, SciTePress, 2025.

Eye Gaze and Action Coupling in Paragliding

Isabelle MARC ( Euromov DHM, IMT Mines Ales), Jean Paul Veuillez (IMT Mines Ales), Eric Wyss (FFVL)

Paragliding pilots control the trajectory of their glider by continuously modifying its aerodynamic shape through both brake inputs and shifts in body weight, in response to changing atmospheric conditions. The coupling between gaze direction and motor actions is a well-established mechanism in the study of human movement and locomotion. It is therefore reasonable to assume that, in paragliding as well, the pilot's visual behavior directly influences motor control processes. Improving our understanding of the relationships between gaze behavior, pilot control actions, and the resulting flight trajectory could help optimize instructional guidelines provided to trainee pilots and, ultimately, contribute to a reduction in accident rates. The internship will involve studying relationships among three categories of variables: gaze-related variables, motor control variables (e.g., brake inputs and body position adjustments), and movement-related variables describing the flight itself (e.g., trajectory and velocity). The first objective of the internship will be to identify the most relevant variables and to determine the most robust assessment methods. A second phase will involve the design and implementation of an experimental protocol aimed at investigating the coupling between these different variables under real-world paragliding conditions during both take-off and landing phases.

Hollands, M. A., Patla, A. E., & Vickers, J. N. (2002). “Look where you’re going!”: gaze behaviour associated with maintaining and changing the direction of locomotion. Experimental brain research, 143(2), 221-230.

Wilkes, M., Long, G., Massey, H., Eglin, C., & Tipton, M. (2022). Quantifying risk in air sports: flying activity and incident rates in Paragliding. Wilderness & Environmental Medicine, 33(1), 66-74.

Riding into the Wind: A Field-Based Physiological and Perceptual Investigation

Thomas LAMBOLAIS (EuroMov DHM), (with Florian Douzery).

thomas.lambolais@mines-ales.fr

On flat terrain, aerodynamic drag accounts for ~90% of total resistance at 40 km·h⁻¹ [1], yet this has almost exclusively been studied in still air, whereas riders actually face variable wind conditions (headwind, crosswind, tailwind) [2]. Producing the same mechanical power output is not an equivalent exercise depending on wind direction. This question remains entirely unexplored in the scientific literature, despite growing interest within the cycling community. This internship aims to quantify, in the field, in cyclists (potentially from a professional Tour de France team), the impact of different wind conditions and riding speeds on physiological (gas exchange, SmO2,…) and psychological responses (perceived exertion, internal sensations). This study would be the first to investigate how wind direction modulates the physiological and perceptual cost of producing a given power output on the road. If the physiological cost of a given wattage varies with wind direction, this could inform target power / pacing choices during races according to weather conditions.

______

[1] Debraux P, Grappe F, Manolova AV, Bertucci W. Aerodynamic drag in cycling: methods of assessment. Sports Biomechanics. 2011;10(3):197-218. doi:10.1080/14763141.2011.592209

[2] Beaumont, F., Bogard, F., Murer, S., & Polidori, G. (2023). Fighting crosswinds in cycling: a matter of aerodynamics. Journal of Science and Medicine in Sport, 26(1), 46-51.

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