Added some refs

doc/paper/paper.bib

@@ -23,9 +23,25 @@ @article{Andersson2018
   journal = {Mathematical Programming Computation}
 }
 
+@article{belaiseEMGmarkerTrackingOptimisation2018a,
+  title = {An {{EMG}}-Marker Tracking Optimisation Method for Estimating Muscle Forces},
+  author = {B{\'e}laise, Colombe and Dal Maso, Fabien and Michaud, Benjamin and Mombaur, Katja and Begon, Micka{\"e}l},
+  year = {2018},
+  month = feb,
+  volume = {42},
+  pages = {119--143},
+  issn = {1573-272X},
+  doi = {10.1007/s11044-017-9587-2},
+  abstract = {Existing algorithms for estimating muscle forces mainly use least-activation criteria, which do not necessarily lead to physiologically consistent results. Our objective was to assess an innovative forward dynamics-based optimisation, assisted by both electromyography (EMG) and marker tracking, for estimating the upper-limb muscle forces. A reference movement was generated, and EMG was simulated to reproduce the desired joint kinematics. Random noise was added to both simulated EMG and marker trajectories in order to create 30 trials. Then, muscle forces were estimated using (1) the innovative EMG-marker tracking forward optimisation, (2) a marker tracking forward optimisation with a least-excitation criterion, and (3) static optimisation with a least-activation criterion. Approaches (1) and (2) were solved using a direct multiple shooting algorithm. Finally, reference and estimated joint angles and muscle forces for the three optimisations were statistically compared using root-mean-square errors (RMSEs), biases, and statistical parametric mapping. The joint angles RMSEs were qualitatively similar across the three optimisations: (1) 1.63{$\pm$}0.51\$1.63 \textbackslash pm 0.51\$\textdegree; (2) 2.02{$\pm$}0.64\$2.02 \textbackslash pm 0.64\$\textdegree; (3) 0.79{$\pm$}0.38\$0.79 \textbackslash pm 0.38\$\textdegree. However, the muscle forces RMSE for the EMG-marker tracking optimisation (20.39{$\pm$}13.24\$20.39 \textbackslash pm 13.24\$~N) was about seven times smaller than those resulting from the marker tracking (124.22{$\pm$}118.22\$124.22 \textbackslash pm 118.22\$~N) and static (148.15{$\pm$}94.01\$148.15 \textbackslash pm 94.01\$~N) optimisations. The originality of this novel approach is close tracking of both simulated EMG and marker trajectories in the same objective function, using forward dynamics. Therefore, the presented EMG-marker tracking optimisation led to accurate muscle forces estimations.},
+  file = {/home/pariterre/Zotero/storage/G899XEWA/Bélaise et al. - 2018 - An EMG-marker tracking optimisation method for est.pdf},
+  journal = {Multibody System Dynamics},
+  language = {en},
+  number = {2}
+}
+
 @article{damsgaardAnalysisMusculoskeletalSystems2006,
   title = {Analysis of Musculoskeletal Systems in the {{AnyBody Modeling System}}},
-  author = {Damsgaard, Michael and Rasmussen, John and Christensen, S\o ren T\o rholm and Surma, Egidijus and {de Zee}, Mark},
+  author = {Damsgaard, Michael and Rasmussen, John and Christensen, S{\o}ren T{\o}rholm and Surma, Egidijus and {de Zee}, Mark},
   year = {2006},
   month = nov,
   volume = {14},
@@ -156,6 +172,23 @@ @article{hillHeatShorteningDynamic1938
   number = {843}
 }
 
+@article{jacksonImprovementsMeasuringShoulder2012a,
+  title = {Improvements in Measuring Shoulder Joint Kinematics},
+  author = {Jackson, M. and Michaud, B. and T{\'e}treault, P. and Begon, M.},
+  year = {2012},
+  month = aug,
+  volume = {45},
+  pages = {2180--2183},
+  issn = {0021-9290},
+  doi = {10.1016/j.jbiomech.2012.05.042},
+  abstract = {For many clinical applications it is necessary to non-invasively determine shoulder motion during dynamic movements, and in such cases skin markers are favoured. However, as skin markers may not accurately track the underlying bone motion the methods currently used must be refined. Furthermore, to determine the motion of the shoulder a model is required to relate the obtained marker trajectories to the shoulder kinematics. In Wu et al. (2005) the International Society of Biomechanics (ISB) proposed a shoulder model based on the position of bony landmarks. A limitation of the ISB recommendations is that the reference positions of the shoulder joints are not standardized. The aims of this research project were to develop a method to accurately determine shoulder kinematics using skin markers, and to investigate the effect of introduction of a standardized reference configuration. Fifteen subjects, free from shoulder pathology, performed arm elevations while skin marker trajectories were tracked. Shoulder kinematics were reconstructed using a chain model and extended Kalman filter. The results revealed significant differences between the kinematics obtained with and without introduction of the reference configuration. The curves of joint angle tended towards 0\textdegree{} for 0\textdegree{} of humerus elevation when the reference configuration was introduced. In conclusion, the shoulder kinematics obtained with introduction of the reference configuration were found to be easier to interpret than those obtained without introduction of the reference configuration.},
+  file = {/home/pariterre/Zotero/storage/QM9PSZ5R/Jackson et al. - 2012 - Improvements in measuring shoulder joint kinematic.pdf;/home/pariterre/Zotero/storage/QNMG6Z6P/S0021929012003235.html},
+  journal = {Journal of Biomechanics},
+  keywords = {3D kinematics,Chain model,Joint angles,Reference configuration,Shoulder},
+  language = {en},
+  number = {12}
+}
+
 @article{kainzJointKinematicCalculation2016,
   title = {Joint Kinematic Calculation Based on Clinical Direct Kinematic versus Inverse Kinematic Gait Models},
   author = {Kainz, H. and Modenese, L. and Lloyd, D. G. and Maine, S. and Walsh, H. P. J. and Carty, C. P.},
@@ -233,13 +266,35 @@ @article{manalOneparameterNeuralActivation2003
   number = {8}
 }
 
+@article{Michaud2018biorbdoptim,
+  title = {Biorbd-Optim: {{An}} Optimal Control Framework for Biomechanical Analyses Using Biorbd},
+  author = {Michaud, Benjamin and Begon, Mickael},
+  year = {2020},
+  howpublished = {Web page}
+}
+
 @article{Michaud2018biorbdViz,
   title = {Biorbd-Viz: {{A}} Vizualization Python Toolbox for Biorbd},
   author = {Michaud, Benjamin and Begon, Mickael},
   year = {2018},
   howpublished = {Web page}
 }
 
+@article{moissenetOptimizationMethodTracking2019a,
+  title = {An {{Optimization Method Tracking EMG}}, {{Ground Reactions Forces}}, and {{Marker Trajectories}} for {{Musculo}}-{{Tendon Forces Estimation}} in {{Equinus Gait}}},
+  author = {Moissenet, Florent and B{\'e}laise, Colombe and Piche, Elodie and Michaud, Benjamin and Begon, Micka{\"e}l},
+  year = {2019},
+  volume = {13},
+  publisher = {{Frontiers}},
+  issn = {1662-5218},
+  doi = {10.3389/fnbot.2019.00048},
+  abstract = {In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 {$\pm$} 3.3 mm for pelvis translations, and 1.9 {$\pm$} 1.3\textdegree{} for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 {$\pm$} 5.3 \%) in a relatively fast process (3.88 {$\pm$} 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 {$\pm$} 5.5 N), even without the use of a complex foot-ground contact model.},
+  file = {/home/pariterre/Zotero/storage/JP8357X9/Moissenet et al. - 2019 - An Optimization Method Tracking EMG, Ground Reacti.pdf},
+  journal = {Frontiers in Neurorobotics},
+  keywords = {Co-contraction,Direct multiple shooting,Musculo-tendon forces,musculoskeletal modeling,neuromusculoskeletal simulation},
+  language = {English}
+}
+
 @article{sethOpenSimSimulatingMusculoskeletal2018,
   title = {{{OpenSim}}: {{Simulating}} Musculoskeletal Dynamics and Neuromuscular Control to Study Human and Animal Movement},
   shorttitle = {{{OpenSim}}},
@@ -295,6 +350,32 @@ @article{trinlerMuscleForceEstimation2019b
   keywords = {Gait analysis,Muscle force estimation,Musculoskeletal modelling}
 }
 
+@article{verdugoEffectsTrunkMotion2020,
+  title = {Effects of {{Trunk Motion}}, {{Touch}}, and {{Articulation}} on {{Upper}}-{{Limb Velocities}} and on {{Joint Contribution}} to {{Endpoint Velocities During}} the {{Production}} of {{Loud Piano Tones}}},
+  author = {Verdugo, Felipe and Pelletier, Justine and Michaud, Benjamin and Traube, Caroline and Begon, Micka{\"e}l},
+  year = {2020},
+  volume = {11},
+  publisher = {{Frontiers}},
+  issn = {1664-1078},
+  doi = {10.3389/fpsyg.2020.01159},
+  abstract = {Piano performance involves several levels of motor abundancy. Identification of kinematic strategies that enhance performance and reduce risks of practice-related musculoskeletal disorders (PRMD) represents an important research topic since more than half of professional pianists might suffer from PRMD during their career. Studies in biomechanics have highlighted the benefits of using proximal upper-limb joints to reduce the load on distal segments by effectively creating velocity and force at the finger-key interaction. If scientific research has documented postural and expressive features of pianists' trunk motion, there is currently a lack of scientific evidence assessing the role of trunk motion in sound-production and in injury prevention. We address this gap by integrating motion of the pelvis and thorax in the analysis of both upper-limb linear velocities and joint angular contribution to endpoint velocities. Specifically, this study aims to assess kinematic features of different types of touch and articulation and the impact of trunk motion on these features. Twelve pianists performed repetitive loud and slow-paced keystrokes. They were asked to vary: i) body implication (use of trunk and upper limb motion or use of only upper limb motion), ii) touch (struck touch, initiating the attack with the fingertip at a certain distance from the key surface, or pressed touch, initiating the attack with the fingertip in contact with the key surface), and iii) articulation (staccato, short finger-key contact time, or tenuto, sustained finger-key contact time). Data were collected using a 3D motion capture system and a sound recording device. Results show that body implication, touch, and articulation modified kinematic features of loud keystrokes, which exhibited not only downward but also important forward segmental velocities (particularly in pressed touch and staccato articulation). Pelvic anterior rotation had a prominent role in the production of loud tones as it effectively contributed to create forward linear velocities at the upper limb. The reported findings have implications for performance, teaching and research domains since they provide evidence of how pianists' trunk motion can actively contribute to the sound-production and might not only be associated with either postural or expressive features.},
+  file = {/home/pariterre/Zotero/storage/KUMJ4975/Verdugo et al. - 2020 - Effects of Trunk Motion, Touch, and Articulation o.pdf},
+  journal = {Frontiers in Psychology},
+  keywords = {articulation,Biomechanics,inverse kinematics,Piano performance,Touch,trunk motion},
+  language = {English}
+}
+
+@article{Verschueren2019,
+  title = {Acados: A Modular Open-Source Framework for Fast Embedded Optimal Control},
+  author = {Verschueren, Robin and Frison, Gianluca and Kouzoupis, Dimitris and {van Duijkeren}, Niels and Zanelli, Andrea and Novoselnik, Branimir and Frey, Jonathan and Albin, Thivaharan and Quirynen, Rien and Diehl, Moritz},
+  year = {2019},
+  archivePrefix = {arXiv},
+  eprint = {1910.13753},
+  eprinttype = {arxiv},
+  journal = {arXiv preprint},
+  primaryClass = {math.OC}
+}
+
 @article{wachterImplementationInteriorpointFilter2006a,
   title = {On the Implementation of an Interior-Point Filter Line-Search Algorithm for Large-Scale Nonlinear Programming},
   author = {W{\"a}chter, Andreas and Biegler, Lorenz T.},