Ik analysis for the hip simulator using the open sim simulatorEditorIJAERD
The model of the project to create a detailed assembly of muscles spotting the hip joint. Additional muscles
and combinations were added to the baseline lower extremity assemblies currently available in OpenSim. The geometry
of the muscles was adjusted to pair moment arms reported here. The slack moment and the isometric were added to the
arithmetic value of the tanquntial assembly of joints
Current damage predictors in high-valued systems are based on strain measurements and crack detection, thus, estimating the remaining useful life difficult. The US Army Research Laboratory developed damage precursor detection technique to outsmart fatigue prior to crack initiation. Our successful approach track the evolution in the materials microstructure, electrical inductance or capacitance, or thermal response.
The Mechanical Behavior Of A Nylon Seat Belt Exposed To Cyclical Loads: A Num...IJERA Editor
This work aims to study the mechanical behavior of a nylon seat belt when it is exposed to cyclical loads through
the Finite Element Methods. This work used as base the brazilian regulamentoy standard ABNT NBR 7337:2011
to create the virtual model of the seat belt, with the following dimensions: 1.20mm thick, 48mm width and
250mm length. The next step was to import this CAD model to ANSYS 14.5 software, to create the correct
material model for this case and apply the correct boundary conditions in order to analyze its behavior under a
load that varies from 0 to 2000 N at a 10 Hz frequency. The final step was to analyze this numerical results that
referring to this component under these conditions.
Ik analysis for the hip simulator using the open sim simulatorEditorIJAERD
The model of the project to create a detailed assembly of muscles spotting the hip joint. Additional muscles
and combinations were added to the baseline lower extremity assemblies currently available in OpenSim. The geometry
of the muscles was adjusted to pair moment arms reported here. The slack moment and the isometric were added to the
arithmetic value of the tanquntial assembly of joints
Current damage predictors in high-valued systems are based on strain measurements and crack detection, thus, estimating the remaining useful life difficult. The US Army Research Laboratory developed damage precursor detection technique to outsmart fatigue prior to crack initiation. Our successful approach track the evolution in the materials microstructure, electrical inductance or capacitance, or thermal response.
The Mechanical Behavior Of A Nylon Seat Belt Exposed To Cyclical Loads: A Num...IJERA Editor
This work aims to study the mechanical behavior of a nylon seat belt when it is exposed to cyclical loads through
the Finite Element Methods. This work used as base the brazilian regulamentoy standard ABNT NBR 7337:2011
to create the virtual model of the seat belt, with the following dimensions: 1.20mm thick, 48mm width and
250mm length. The next step was to import this CAD model to ANSYS 14.5 software, to create the correct
material model for this case and apply the correct boundary conditions in order to analyze its behavior under a
load that varies from 0 to 2000 N at a 10 Hz frequency. The final step was to analyze this numerical results that
referring to this component under these conditions.
Denodo DataFest 2016: What’s New in Denodo Platform – Demo and RoadmapDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptGwp7
Curious about product roadmap? In this session, we will review some of the new key features introduced this year in the Denodo Platform in areas such as performance, self-service, security and monitoring. We will also take a sneak peek at the most exciting features in the roadmap for Denodo 7.0.
In this session, you will learn:
• New performance-related features in big data scenarios
• New governance and self-service features
• New connectivity, data transformation, and enterprise-wide deployment features
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
This groundbreaking book offers a revolutionary new perspective on enterprise architecture and its mission-critical role in the design, implementation, and transformation of IT ecosystems.
The age of transformation is upon us. And for corporate IT departments, supporting and sustaining enterprise architecture requires a fundamentally new approach.
Transformative Enterprise Architecture has the solution. It presents a new methodology that boldly redefines the characteristics and competencies that every large-scale IT team must develop to function successfully. Topics include:
• Establishing a mature enterprise architecture system with an eye toward continuous improvement
• Ensuring the economic sustainability of IT infrastructure
• Staying agile in an era of uncertainty
Written especially for CIOs, CTOs, and other corporate stakeholders, Transformative Enterprise Architecture goes beyond frameworks, tools, processes, and patterns. It can help your organization survive and thrive in these times of rapid change, disruptive innovation, and intense competition.
Independent study : A Matlab AnyBody interface to compute torque requirements for Assisting and Resisting modes for a leg exoskeleton helping perform a leg curl.
Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Reh...Luca Parisi
Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...sugiuralab
Wearable Accelerometer Optimal Positions for Human Motion Recognition. The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020), March 10-11, 2020
Denodo DataFest 2016: What’s New in Denodo Platform – Demo and RoadmapDenodo
Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/ptGwp7
Curious about product roadmap? In this session, we will review some of the new key features introduced this year in the Denodo Platform in areas such as performance, self-service, security and monitoring. We will also take a sneak peek at the most exciting features in the roadmap for Denodo 7.0.
In this session, you will learn:
• New performance-related features in big data scenarios
• New governance and self-service features
• New connectivity, data transformation, and enterprise-wide deployment features
This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6
This groundbreaking book offers a revolutionary new perspective on enterprise architecture and its mission-critical role in the design, implementation, and transformation of IT ecosystems.
The age of transformation is upon us. And for corporate IT departments, supporting and sustaining enterprise architecture requires a fundamentally new approach.
Transformative Enterprise Architecture has the solution. It presents a new methodology that boldly redefines the characteristics and competencies that every large-scale IT team must develop to function successfully. Topics include:
• Establishing a mature enterprise architecture system with an eye toward continuous improvement
• Ensuring the economic sustainability of IT infrastructure
• Staying agile in an era of uncertainty
Written especially for CIOs, CTOs, and other corporate stakeholders, Transformative Enterprise Architecture goes beyond frameworks, tools, processes, and patterns. It can help your organization survive and thrive in these times of rapid change, disruptive innovation, and intense competition.
Independent study : A Matlab AnyBody interface to compute torque requirements for Assisting and Resisting modes for a leg exoskeleton helping perform a leg curl.
Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Reh...Luca Parisi
Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...sugiuralab
Wearable Accelerometer Optimal Positions for Human Motion Recognition. The 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech 2020), March 10-11, 2020
Lower Limb Musculoskeletal Modeling for Standing and Sitting Event by using M...ijsrd.com
This paper shows how the musculoskeletal modeling for standing and sitting event of lower limb of humans is possible using MSMS (Musculoskeletal Modeling Software). Concept, significance and factors of musculoskeletal modeling of lower limb have been detailed. It presents how the complexity of biomechanics related to lower limb can modeled with MSMS and also represents how such model can be useful in generating MATLAB/SIMULINK ® model that can be further used in the development of prototype neuroprosthesis model for paraplegic patients having lower extremity disorders. Proposed modeling includes 12 leg virtual muscles which shows its accuracy for event of standing to sitting event with due consideration of the coordinating position, Mass, Inertia used for rigid body segment, and Joint Type, Translational Axes, Rotational Axes used for lower limb joints. The result generated by MSMS for proposed modeling has been presented. Merits and demerits of proposed modeling have also been discussed.
Human action recognition with kinect using a joint motion descriptorSoma Boubou
- We proposed a novel descriptor for motion of skeleton joints.
- Proposed descriptor proved to outperform the state-of-the-art descriptors such as HON4D and the one proposed by Chen et al 2013.
- Our proposed approached proved to be effective for periodic actions (e.g., Waving, Walking, Jogging, Side-Boxing, etc).
- Grouping was effective for actions with unique joints trajectories (e.g., Tennis serving, Side kicking , etc).
- Grouping joints into eight groups is always effective with actions of MSR3D dataset.
Passenger seat is main part of vehicle which has direct effect on her/his convenience. Seat suspension can remove unwanted and harmful vibration if right parameters were selected. Each of human body organs has specific natural frequency. When vehicle vibration reaches to this natural frequency, resonance will occur, and this phenomenon is harmful in long term. Usually lumped models used to predict human body response to vibration. In this paper, via Kitazaki biodynamic model, the seat to head vibration transmissibility was minimized by artificial neural network method. By this method, the optimum spring constant, damper coefficient and mass values were found.
Computational Motor Control: State Space Models for Motor Adaptation (JAIST s...hirokazutanaka
This is lecure 3 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=dtpgJLRt90M
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
1. Inference of computational
models of tendon networks via
sparse experimentation
Manish Umesh Kurse
Apr 11, 2012
1
Brain-Body Dynamics Laboratory
Ph.D. committee: Dr. Francisco J.Valero-Cuevas, Dr. Hod Lipson,
Dr. Gerald E. Loeb, Dr. Eva Kanso
2. 2
MSMS:
Davoodi
et
al.,
2007
Measurement of internal states Injury, deformity, surgery
h4p://www.ispub.com/
Ergonomics, prosthetic design, etc.
h4p://www.anybodytech.com
Inputs Outputs
femoris (rectfem), gluteus medialis/glueteus minimus (glmed/min),
Muscles cooperate
to exert force
Solutions in muscle activation s
task-specific activation se
2
12
3
LIMB
3
1
Fy
Fx
Muscle 2
Muscle 1
Target x-for
Target y-f
Task-specific activation
Fig. 1. Three muscle ‘‘schematic model’’ conceptually illustrates the nece
region of force space, the feasible force set, is achievable given this mu
y-force. (c) The valid coordination patterns for the x and y targets can a
Kutch andValero-Cuevas, 2011
Computational modeling of musculoskeletal
systems
3. 3
Modeling : Structure based on observation + experimental
measurement of some parameters.
Drawbacks:
•
Not
possible
to
measure
all
parameters.
•
Not
validated
with
experimental
input-‐output
data.
•
Structure
assumed
need
not
be
funcEonally
accurate
representaEon.
R
System
✓
s
R
Structure
assumed,
parameters fit.
1 R2
Infer structure and
parameters from input-
output data
(✓)
4. 4
Develop computational methods to simultaneously infer
structure and parameter values of functionally accurate
models of musculoskeletal systems directly from
experimental input-output data.
Objective
Inputs Outputs
We examined 5 different postures in 3 specimens, and 3 different posture
the final specimen. Each posture was neutral in add-abduction. The exami
postures were chosen to cover the workspace and simulate those found
everyday tasks. After positioning the finger in a specific posture, we determi
the action matrix for the finger: we applied 128 combinations of tendon tensi
representing all possible combinations of 0 and 10 N across the seven tendons,
held each combination for 3 s. The fingertip forces resulting from each coordi
tion pattern was determined by averaging the fingertip load cell readings acr
the hold period. Linear regression was performed on each fingertip fo
component using the tendon tensions as factors. In this way, the fingertip fo
vector generated by 1 N of tendon tension was determined for all muscles.
force vector generated by each muscle was scaled by an estimate for maxim
muscle force (Valero-Cuevas et al., 2000) to generate the columns of the act
matrix for each specimen and posture examined.
2.2. Action matrix for human leg model
We also studied the necessity of muscles for mechanical output fo
simplified, but plausible, sagittal plane model of the human leg (hip, knee,
ankle joints). The model contained 14 muscles/muscle groups (Kuo and Za
1993) (muscle/muscle group abbreviation in parentheses): medial and lat
gastrocnemius (gastroc), soleus (soleus), tibialis posterior (tibpost), peron
brevis (perbrev), tibialis anterior (tibant), semimembranoseus/semitendeno
biceps femoris long head (hamstring), biceps femoris short head (bfsh), rec
femoris (rectfem), gluteus medialis/glueteus minimus (glmed/min), adduc
Muscles cooperate
to exert force
2
12
3
LIMB
3
1
Fy
Fx
Kutch andValero-Cuevas, 2011
5. Tendon networks of the fingers
5
Lateral bands
Central slip
Terminal slip
Retinacular ligament
Sagittal band
Transverse fibers
Clavero et al. (2003). “Extensor Mechanism of the Fingers: MR Imaging-Anatomic Correlation”, Radiographics
Netter, F. Atlas of Human Anatomy, 3rd edition, pp 447-453
9. Dissertation outline
Experimental actuation of a cadaveric hand.
2
3
4
5
6
New inference approach to
learn functions of tendon
routing.
Application to the human
index finger.
Experimental validation of an existing
model.
Tendon network simulator and
sensitivity analysis.
Inference of anatomy-based models
from experimental data.
1
Analytical models Anatomy-based models
ASME SBC ’10 & IEEETBME ’12
ASB ’11
CSB ’12
ASME SBC ’09
9
10. Cadaver finger control
10
Load cells
Strings to the tendons
Motion capture markers
6 DOF Load cell
DC motors
Positon encoders
1 2 3 4 5 6
1
15. Conclusions
• Spring-based (muscle-like) control effective
to control movement.
• Simple tap requires a coordinated set of
tendon excursions.
• Neutral equilibrium in specific postures and
tendon tensions.
15
1 2 3 4 5 6
17. Inference of analytical functions
17
Co-authors: Dr. Hod Lipson, Dr. FranciscoValero-Cuevas
1 2 3 4 5 6
2
• Analytical functions for tendon excursions
s = f(✓)
Deshpande et al. 2009
• State of the art : Polynomial regression
s s
‘Controller’
‘Plant’
• Why? R(✓) =
@s
@✓
⌧(✓) = R(✓)Fm
R
✓
R1
R2
(✓)
18. • Can we simultaneously learn form and
parameter values from data?
• Compare accuracy with polynomial regression.
Specific Aims
18
1 2 3 4 5 6
s = f(✓)
20. Robotic tendon driven system
20
s1
s2
s3
2
1
3
Position encoders
Motors keeping
tendons taut
Load cells
Motion capture
markers
Motion capture
camera
1 2 3 4 5 6
Landsmeer
model I
Landsmeer
model II
Landsmeer
model III
s = 3.6sin(0.5θ)
s = 0.6θ + 3.2(1 −
θ/2
tan(θ/2)
)
s = 1.8θ
s = f(✓1, ✓2, ✓3)
21. 21
Schmidt and Lipson, 2009
Polynomial regression
Koza 1992
Symbolic regression vs.
Linear
Quadratic
Cubic
Quartic
1 2 3 4 5 6
22. Comparing symbolic and polynomial regressions
22
2
5
10
20
2
5
10
20
2
5
10
20
Tendon 1
2
5
10
20
Tendon 2
Tendon 3
2
5
10
20
n/256
n/16
n/64
n n
2
n
4
n
8
n
16
n
32
n
64
n
128
n
256
X
X
X
XX
n/256
n/16
n/64
2
5
10
20
n n
2
n
4
n
8
n
16
n
32
n
64
n
128
n
256
n/256
n/16
n/64
n/256
n/16
n/64
n/256
n/16
n/64
n/256
n/16
n/64
Symbolic
Quartic
Linear
Quadratic
Cubic
Dataset size (n =1688) Dataset size (n =1688)
X
X
X
Cross-validation Extrapolation
RMSerror(%)
RMSerror(%)
X Error for all sizes > 5%
Min training set size < n/256
2
5
10
20
Tendon 1
Tendon 2
2
5
10
20
25%
75%
125%
25%
75%
125%
25%
75%
125%
2
5
10
20
Tendon 3
RMSerror(%)
Extrapolation by volume (%)
0 25 15075 10050 125
Symbolic
Quartic
Linear
Quadratic
Cubic
X
X
X All extrapolation errors > 5%
Achievable extrapolation > 150%
Fewer training data
points required
More extrapolatable
2
23. 23
Extrapolation by volume (%)
0
25
50
75
100
125
150
>150
n n
2
n
4
n
8
n
16
n
32
n
64
n
128
Training set size (n =1688)
Extrapolationbyvolume(%)
Symbolic
Quartic
Linear
Quadratic
Cubic
Comparing symbolic and polynomial regressions
Fewer training data
points required
More extrapolatable
Kurse et al. 2012 (in press)
1 2 3 4 5 6
24. 24
Landsmeer
model I
Landsmeer
model II
Landsmeer
model III
s = 3.6sin(0.5θ)
s = 0.6θ + 3.2(1 −
θ/2
tan(θ/2)
)
s = 1.8θ
Simulated musculoskeletal systems
Landsmeer comb. Expressions
I, I, I
Target 1.8✓1 + 1.8✓2 + 1.8✓3
Evolved 1.8✓1 + 1.8✓2 + 1.8✓3
I, II, III
Target
1.8✓1 + 3.6sin(0.5✓2) + 0.6✓3
(1.6✓3)/tan(0.5✓3) + 3.2
Evolved
1.8✓1 + 3.61sin(0.5✓2) + 1.54✓3
0.778sin(✓3)
II, II, I
Target 3.6sin(0.5✓1)+3.6sin(0.5✓2)+1.8✓3
Evolved 3.6sin(0.5✓1)+3.6sin(0.5✓2)+1.8✓3
Table 1: Target and inferred expressions with training, cross-validation and extrap
for some combinations of Landsmeer’s models I, II, III
1 2 3 4 5 6
25. Error vs. number of parameters
25
RMSerror(%)
Cross-validationExtrapolation
Symbolic
Quartic
Linear
Quadratic
Cubic
Experimental data
With no noise
Number of parameters
Simulated data
With noise added
1
2
3
0 20 40
1
2
3
5
.0001
.01
1
0 20 40
.0001
.01
1
0 20 40
1
2
5
10
1
2
5
10
1 2 3 4 5 6
26. Conclusions
• Symbolic regression outperforms polynomial
regression
• Number of training data points
• Extrapolatability
• Robustness to noise
• Number of parameters
• Insight on physics
26
1 2 3 4 5 6
Kurse et al. 2012 (in press)
27. 27
Novel method of
inference of
analytical functions
from data
Application to the
human finger
Schmidt and Lipson, 2009
s1 = f(✓1, ✓2, ✓3, ✓4)
1 2 3 4 5 6
28. Analytical functions: Index finger
28
Constant moment arm
(Linear)
Polynomial regressions
Landsmeer based models
Landsmeer
model I
Landsmeer
model II
Landsmeer
model III
s = 3.6sin(0.5θ)
s = 0.6θ + 3.2(1 −
θ/2
tan(θ/2)
)
s = 1.8θ
Landsmeer, 1961, Brook 1995
3
Co-authors: Dr. Hod Lipson, Dr. FranciscoValero-Cuevas
1 2 3 4 5 6
Eg.An et al. 1983,Valero-Cuevas et al. 1998
Eg. Franko et al. 2011
Eg. Brook et al. 1995
29. Specific aims
• Infer analytical functions for the seven tendons of
the index finger.
• Compare against polynomial regression and
Landsmeer based models.
29
1 2 3 4 5 6
33. Across hands
33
FDP FDS EIP EDC LUM FDI FPI
2
5
10
20
2
5
10
20
50
Tendon
NormalizedRMSerror(%)
Symbolic
Landsmeer
Quartic
Linear
Quadratic
Cubic
1 2 3 4 5 6
FDP FDS EIP EDC LUM FDI FPI
2
5
10
20
Symbolic
Landsmeer
Quartic
Linear
Quadratic
Cubic
10
20
50
Tendon
NormalizedRMSerror(%)
34. Conclusions
• For subject-specific models as well as generalizable
models,
• Symbolic regression more accurate than other models.
• Error bounds on generalizability.
• Models insight on tendon routing.
34
1 2 3 4 5 6
36. Anatomy-based modeling
36
Co-author: Dr. FranciscoValero-Cuevas
Netter, F. Atlas of Human Anatomy, 3rd edition, pp 447-453
1 2 3 4 5 6
4
Clavero et al. 2003
Boutonniere deformity
http://www.ispub.com/
Swan-neck deformity
Mallet finger deformity
37. 37
• Widely used representation:
An-Chao normative model
(1978, 79)
TE=RB+UB
RB=0.133 RI+0.167 EDC+0.667 LU
UB=0.313 UI+0.167 EDC
ES=0.133 RI+0.313 UI+0.167 EDC+0.333 LU
Chao et al. 1978,79
1 2 3 4 5 6
38. Validation of An-Chao model
38
6 DOF loadcell
Load cells measur-
ing tendon tensions
Strings connecting
tendons to motors
Fingertip force vector
1 2 3 4 5 6
39. Validation of An-Chao normative model
39
• Large magnitude and direction errors in fingertip force
magnitude and direction.
1 2 3 4 5 6
(Sagittal plane)
FDP FDS EIP EDC LUM FDI FPI
0
20
40
60
Direrror(degrees)
FDP FDS EIP EDC LUM FDI FPI
0
200
400
600
800
1000
Magerror%
Magnitude errors Direction errors
Flex
Tap
Extend
40. 40
• Let the physics and mechanics
decide force distribution.
• Existing musculoskeletal
modeling software do not model
tendon networks.
• Environment to understand role
of components in force
transformation.
Valero-Cuevas and Lipson, 2004
1 2 3 4 5 6
41. Specific aims
• Develop a modeling environment to
represent these tendon networks.
• Study sensitivity of fingertip force output to
properties of the extensor mechanism.
41
1 2 3 4 5 6
Tendon network simulator
and sensitivity analysis
5
42. Import MRI scan of bones.
Define tendon network.
Tendon network simulator
Solve the nonlinear finite
element problem.
1 2 3 4 5 6
42
43. Iteratively,
• Node and element penetration testing.
• Apply input Forces in increments
• Solve by Newton-Raphson iteration method the displacements of
nodes, U(i), for system equilibrium :
Finite Element Method
• Assemble the internal force vector and the tangent stiffness
matrix in each element.
43
47. Sensitivity analysis of parameters and topology
47
Tessellated
bones
i. Locations
of nodes
ii. Cross-sectional
areas
iii. Resting lengths
iv. Topology
1 2 3 4 5 6
50. Conclusions
50
• Developed a novel tendon network simulator
to represent these tendon networks.
• Studied what properties the fingertip force
output is most sensitive to.
51. 51
1 2 3 4 5 6
Simultaneous inference of topology and
parameter values
Valero-Cuevas et al. 2007 Saxena et al. (in review)
Inference of anatomy-based models
Co-authors: Dr. Hod Lipson, Dr. FranciscoValero-Cuevas
6
R
✓
R1
R2
(✓)
52. 52
Specific aims
•Simultaneous inference of 3D tendon networks from
input-output data in simulation.
•Inference of models of the finger’s extensor
mechanism directly from input-output data via sparse
experimentation.
Inference of anatomy-based models
Co-authors: Dr. Hod Lipson, Dr. FranciscoValero-Cuevas
6
1 2 3 4 5 6
53. Data
530 5000 10000 15000
0.1
0.5
2
10
50
Fitness error vs iterations
TotalRFerroras%
Num evaluations
CPU 1
CPU 2
CPU N
...
?
Topology and
parameter inference
of 3D models
1 2 3 4 5 6
54. Inference of tendon networks in simulation
54
6 DOF loadcell
Load cells measur-
ing tendon tensions
Strings connecting
tendons to motors
Fingertip force vector
3 Postures,
3 sets of inputs
1 2 3 4 5 6
57. Inference using EEA
57
Test suite
Converged?
5N 5N
3N
1N1.5N
3N
Start3 Random tests
Measured data
Evolve models
No
End
Two best tests selected
Estimation Exploration
1N
3N3N
Identify most
`intelligent’
tests
(posture +
tendon
tensions)
1 2 3 4 5 6
59. Conclusions
• Demonstrated for the first time the successful inference
of model topology and parameters of a complex
musculoskeletal system from experimental input-output
data.
• Inferred models are more accurate than models in the
literature.
59
1 2 3 4 5 6
62. Conclusions and future work
62
•Applies to other systems.
•Step towards subject-specific models inferred from
data.
R
System
✓
s
R
2
Infer structure and
parameters from input-
output data
(✓)
tension in each cord, which was fed back to the motor so that a desired amount of
tension could be maintained on each tendon. The fingertip was rigidly attached to
6 DOF load cell (JR3, Woodland, CA).
We examined 5 different postures in 3 specimens, and 3 different postures in
the final specimen. Each posture was neutral in add-abduction. The examined
postures were chosen to cover the workspace and simulate those found in
everyday tasks. After positioning the finger in a specific posture, we determined
the action matrix for the finger: we applied 128 combinations of tendon tensions
representing all possible combinations of 0 and 10 N across the seven tendons, and
held each combination for 3 s. The fingertip forces resulting from each coordina-
tion pattern was determined by averaging the fingertip load cell readings across
the hold period. Linear regression was performed on each fingertip force
component using the tendon tensions as factors. In this way, the fingertip force
vector generated by 1 N of tendon tension was determined for all muscles. The
force vector generated by each muscle was scaled by an estimate for maximum
muscle force (Valero-Cuevas et al., 2000) to generate the columns of the action
matrix for each specimen and posture examined.
2.2. Action matrix for human leg model
We also studied the necessity of muscles for mechanical output for a
simplified, but plausible, sagittal plane model of the human leg (hip, knee, and
ankle joints). The model contained 14 muscles/muscle groups (Kuo and Zajac,
1993) (muscle/muscle group abbreviation in parentheses): medial and lateral
gastrocnemius (gastroc), soleus (soleus), tibialis posterior (tibpost), peroneus
brevis (perbrev), tibialis anterior (tibant), semimembranoseus/semitendenosis/
biceps femoris long head (hamstring), biceps femoris short head (bfsh), rectus
femoris (rectfem), gluteus medialis/glueteus minimus (glmed/min), adductor
longus (addlong), iliacus (iliacus), tensor fac
(glmax). Moment arms for hip flexion, knee fle
of these muscles were obtained from a compute
et al., 2010). When necessary, multiple muscles
muscle groups. We derived a 3 Â 3 square Jaco
knee, and ankle angle to the foot position in t
orientation of the foot in space. This Jacobian m
combined with the moment arms and maxima
matrix mapping muscle activation to forces
Cuevas, 2005b), although our analysis of muscl
with respect to the endpoint forces.
2.3. Analyzing the action matrix to determine m
We used the action matrix to determine
for a given desired output force using standard
The muscle redundancy problem can be expre
(Chao and An, 1978; Spoor, 1983). These ineq
activation for each muscle lie between 0 and 1,
equal to the desired force. The inequality con
activation space called the task-specific activatio
produce the desired output force (Kuo and Z
Valero-Cuevas et al., 2000, 1998). We comput
specific activation set using a vertex enumera
1992). We then found the task-specific activat
output force for each muscle by projecting a
coordinate axes to determine the minimum and
While previous studies have used similar experi
Muscles cooperate
to exert force
Feasible force set,
one target force vector
Fy
Fx
Target x-force
Target y-f
Feasible force set
Target force vector
2
12
3
LIMB
3
1
Fy
Fx
Muscle 2
J.J. Kutch, F.J. Valero-Cuevas / Journal of Biomechanics 44 (2011) 1264–1270
Kutch andValero-Cuevas, 2011
63. Acknowledgements
63
Dr. Francisco
Valero-Cuevas
Dr. Hod
Lipson
Dr. Gerald
Loeb
Dr. Eva
Kanso Dr. Jason
Kutch
Josh Inouye
Sudarshan
Dayanidhi
Dr. Heiko
Hoffmann
Dr.Anupam
Saxena
Dr. Jae-Woong
Yi
Kornelius
Rácz
Brendan
Holt
Alex Reyes
Emily
Lawrence
Dr. Srideep
Musuvathy
John
Rocamora
Dr. Marta
Mora
Na-hyeon
Ko
Alison HuDr.
Evangelos
Theodorou
Dr. Caroline
LeClercq
Dr.Vincent
Rod Hentz
Dr. Nina
Lightdale
Dr. Isabella
Fasolla
Kari Oki
Dr.
Terrance
Sanger
64. Acknowledgements
64
The
NaEonal
Science
FoundaEon:
CAREER award,
EFRI - COPN to FVC
The National Institutes of Health
NIAMS/NICHD R01-AR050520; R01-AR052345