The Importance of Models in Data Analysis with Small Human

Movement Datasets: Inspirations from Neurorobotics Applied to

Posture Control of Humanoids and Humans

Vittorio Lippi

, Christoph Maurer

and Thomas Mergner

Neurological University Clinic, University of Freiburg, Freiburg im Breisgau, Germany

Keywords: Posture Control, Machine Learning, Neurorobotics, Parametric Nonlinear Systems.

Abstract: Machine learning has shown impressive improvements recently, thanks especially to the results shown in

deep learning applications. Besides important advancements in the theory, such improvements have been

associated with an increment in the complexity of the used models (i.e. the numbers of neurons and connec-

tions in neural networks). Bigger models are possible given the amount of data used in the training process is

increased accordingly. In medical applications, however, the size of datasets is often limited by the availability

of human subjects and the effort required to perform human experiments. This position paper proposes the

integration of bioinspired models with machine learning.

1 INTRODUCTION

During the last decade, there have been great im-

provements in machine learning applications, in the

sense that the machine learning systems got more

powerful and accurate. This improvement is associ-

ated with a resurgence of the use of neural networks,

in particular of deep learning. As shown in Fig 1, the

size of the neural networks has increased in the order

of magnitudes during the last 40 years as has the num-

ber of samples used for the training. A massive da-

taset of training samples is not always available, how-

ever. In the case of data from human experiments, the

reason for the difficulty in getting a huge amount of

data lies in the effort required to perform the experi-

ments and in the fact that human data are often de-

scribed by a large number of relevant features; in

some cases, there are more features than samples

(Hastie & Tibshirani, 2004). For this reason, when

working with human data, regularization is of pri-

mary importance. Deep learning systems are finding

application in the analysis of human movements

(Abdu-Aguye & Gomaa, 2019b, 2019a) and, while

the results are promising, the field is still at the begin-

https://orcid.org/0000-0001-5520-8974

https://orcid.org/0000-0001-9050-279X

https://orcid.org/0000-0001-7231-164X

ning and hence the possibilities are still to be fully ex-

plored. In this position paper, we will present exam-

ples that show the advantage of integrating models in

the analysis of human experiments. The particular

case of human and humanoid posture control is pre-

sented and some examples will be discussed. The ap-

plication of ML to human posture control analysis is

already a research topic, for example to design diag-

nostic tools in a clinical setup (Costa et al., 2016). The

issue will be shown from the point of view of both the

analysis of human data and the control of humanoid

robots’ balance. Modern research on human and hu-

manoid posture control already uses mathematical

models (Alexandrov et al., 2017; Boonstra et al.,

2014; Engelhart et al., 2014; Goodworth & Peterka,

2018; Mergner, 2010; Pasma et al., 2014; van

Asseldonk et al., 2006; H van der Kooij et al., 2007;

Herman van der Kooij et al., 2005). The presented

models are designed to describe, and in some cases

predict, human behavior in specific experiments, and

they incorporate hypotheses about neural movement

control and empirical findings. It comes natural when

applying machine learning to also try to integrate the

knowledge represented by such models with the

adaptability of the learning systems. The examples

Lippi, V., Maurer, C. and Mergner, T.

The Importance of Models in Data Analysis with Small Human Movement Datasets: Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans.

DOI: 10.5220/0010297005790585

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2021), pages 579-585

ISBN: 978-989-758-486-2

579

Figure 1: Increase of dataset sizes and numbers of neurons of the neural network through the last 70 years in comparison with

recent posture control and balance applications. On the left (green dots) the number of samples in several datasets used in

machine learning applications, on the right (green squares) the number of neurons in neural networks developed during the

years (from the Perceptron to GoogleNet). The graphs are adapted from (Goodfellow et al., 2016) where the complete list of

NN architectures presented in the figure is available. The red marks represent the number of samples or subjects used in recent

applications (on the left plot) and the size of the neural network in the respective solution (right plot). Specifically, the star

represents (Jafari et al., 2019), the cross Lippi (2018), and the dot (Lippi et al., 2020). It is evident how the three posture

control examples rely on smaller datasets and smaller architectures compared to the possibilities of deep learning at the state

of the art.

presented in the following will try to cover different

applications (i.e. classification, control, and system

identification) and show the advantages of the inte-

gration of modelling and learning. The methods used

in the two examples are on-line linear regression and

deep learning (convolutional neural network); they

are presented not with the intention to compare dif-

ferent ML methods but to show how posture control

models can be integrated in different set-ups.

2 EXAMPLES

2.1 The Disturbance Identification and

Compensation (DEC) Model for

Posture Control

The examples presented in this section will make use

of a bio-inspired posture control model, the DEC

(Mergner et al., 2003). A brief description of the

model is provided as an introduction to the following

examples for a more in-depth description see Lippi &

Mergner (2017), where the DEC is implemented as a

modular control system for humanoid robots. The

DEC control is designed to a describe how human

postural control mechanisms interact with movement

execution control. A schema of the DEC control is

shown in Fig. 2 (top), The components of the control

are: (A) A servo control loop for each degree of free-

dom. The controller is a PD controller, or PID in some

implementations (the block "C" in Fig 2.). The con-

trolled variable consists either of the joint angle, the

orientation in space of the above joint, or the orienta-

tion in space of the centre of mass of the whole body

above the controlled joint. The control is imple-

mented in a modular way, and each module performs

sensor fusion and control. (B) Multisensory estima-

tion of external disturbances, i.e. rotation and transla-

tion of the supporting link or support, contact forces,

and field forces such as gravity. The disturbance esti-

mates are fed into the servo so that the joint torque

compensates on-line for the disturbances while exe-

cuting the desired movements.

The disturbance compensation mechanism al-

lows the system to use a low loop gain and thus stable

control in face of neural time delays, or, in case of

humanoid control, of delays due to signal transmis-

sion or low sample rate (Ott et al., 2016). The refer-

ence input to each module determines its postural

function, e.g. maintaining a given orientation of the

supported link (either in space or with respect to the

supporting link), or maintaining the COM above its

supporting joint. Modules exchange information with

neighbouring modules, i.e. those mechanically inter-

connected.

ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods

580

2.2 Online Learning for the Posture

Control of the Lucy Robot

Small human/humanoid datasets may suffice to use

linear learning systems. As an example, our previous

work (Lippi, 2018) shows how the nonlinear DEC

model can be integrated with a linear learning system

to make it capable of controlling human posture con-

trol. The challenge here was represented by the

closed-loop nature of the posture control, i.e. by the

fact that the body is intrinsically unstable and the con-

trol is always active. The machine learning process is

then based on data that are influenced by the use of

the learned predictor itself. Therefore, an on-line

training approach was proposed. It improved the con-

trol of the body sway without endangering control

stability. In Fig. 2 the structure of the bioinspired pre-

dictor is shown. The ML model was a rather simple

linear model, implemented in a way so that it could

learn incrementally as the robot was balancing.

In particular, previously and here the learning sys-

tems are trained to predict the COM sway 𝛼



, with

an anticipation of 𝑇



 70 ms. The inputs taken

into account are the previous sensory-based values for

the body sway angle 𝛼



and the reference value 𝑦





𝛼



(sampled at previous steps). Every 10 ms an input

vector is constructed using delayed versions of the in-

put signals:

𝑥







𝛼



𝜏 𝛼



𝜏  ∆𝑡 𝛼



𝜏  2∆𝑡 𝛼



𝜏  ∆𝑡 𝛼



𝜏  ∆𝑡 𝛼



𝜏 

2∆𝑡



where 𝜏𝑡



𝑇



and ∆t is set to 64 ms. The

predictor has the structure of an affine application,

where the parameter to be learned are the elements of

the transformation matrix. Specifically, the disturb-

ance to be predicted at the time i, 𝑦



can be arranged

in a vector of target values 𝑌𝑦



𝑦



⋯ 𝑦



, and the

observed input is integrated into the matrix

𝑋

𝑥





𝑥





…𝑥





11 1

 (1)

The weight matrix is computed as 𝑊𝑌𝑋



, us-

ing the pseudoinverse operation that can be imple-

mented on-line. The values used to build X and Y are

affected by the prediction, as shown in Fig. 2.

The use of the real robotic platform Lucy, with

real noisy sensors, helped to evaluate the hypothesis

about predictions in a real-world setup. The robust-

ness of the system was tested including an additional

delay in the loop. The prediction system allows the

system to stand with a delay of 60 ms, while the sys-

tem without prediction becomes unstable at 10 ms.

The prediction system was compared with a Smith

Figure 2: Integration of the prediction system based on a

linear learning model. On the left the Lucy robot, a human-

oid with 14 DoF, where the system was tested. The schema

above shows how the DEC control integrates disturbances

estimation. In this specific case, the predicted effect is the

gravity torque. The “prop” block represents the propriocep-

tive feedback, based on joint angles, while “disturbances

estimation” is implemented through a sensor fusion inte-

grating proprioception and vestibular (IMU) input. The pre-

diction is compared with the measured value as shown in

the schema below: The threshold function has the effect

that, when the prediction and the sensor value are similar,

the prediction is used, while the sensed value is used when

the difference between the two is large. This approach re-

sembles a Smith predictor and the way the efference copy

mechanism is used in modeling human behavior.

predictor (that is based just on the model of the sys-

tem) and, as result, proved to produce a better perfor-

mance.

2.3 How Models Can Benefit from

Machine Learning (ML): System

Identification with CNN

A previous work (Lippi et al., 2020) presented a

method for posture control parameter identification

based on CNN. It represents an example of how ML

can provide a tool for modelling, exploiting the

knowledge of the posture control system in the form

of a parametric model; the CNN identifies the param-

eters of such a model.

Human posture control exhibits nonlinearities

such as deadbands and gain non-linearities. Nonlinear

models are more complex to be fitted on human data

than linear models and, in the general case, expensive

iterative procedures need to be used. This issue

brought us to the idea to identify the parameters of a

nonlinear bio-inspired posture control system, the

DEC model using ML. The advantage lies here in the

The Importance of Models in Data Analysis with Small Human Movement Datasets: Inspirations from Neurorobotics Applied to Posture

Control of Humanoids and Humans

581

fact that using the trained network is almost

immediate, whereas training the CNN would be more

computationally expensive.

The training set was produced with parameters

from uniform distributions, filtered with the con-

straint that they would produce a stable simulation.

The number of samples can be as large as needed, be-

ing here produced through a simulation. In order to

obtain more human-like examples, the data-set was

enriched with samples of larger body sways. In the

future, the CNN can also be tested a posteriori by

comparing the distribution of the parameters it pro-

duces for the validation set with the expected distri-

bution for the real data. This can help in choosing hy-

perparameters as shown in previous works (Sforza et

al., 2011; Sforza & Lippi, 2013). Fig. 3 schematically

summarizes the pipeline of the work. The input of the

network is a 2-channel picture, representing the mod-

ulus and the phase of the fft of the body sway com-

puted on time windows (in Fig.3 the two channels are

visualized as “green” and “blue”). Because of its ar-

chitecture, i.e. training the same weights on different

parts of the image, the CNN is able to recognize pat-

terns translated in time and in frequency. While the

invariance in time has the obvious advantage of mak-

ing the recognition of a specific motion feature inde-

pendent from its onset in the input signal, the invari-

ance in frequency has no obvious physical interpreta-

tion. The SIP model proved to be suitable to describe

the analysed posture control scenario, this even in the

sub-optimal case of identifying the control parameter

of the ankle joint in a DIP model.

3 CONCLUSIONS AND FUTURE

WORK

This position paper gives two examples of the use of

posture control models in learning. The examples

suggest that the modeling can be useful in reducing

the number of features used with the ML algorithm,

simplifying the complexity of the ML system re-

quired to perform the task, or increasing the number

of training samples using simulations to produce arti-

ficial data.

The identification of posture control model pa-

rameters can be applied to the benchmarking of hu-

manoid robots (Lippi et al., 2019; Torricelli et al.,

2020) and to the analysis of clinical data (Exarchos et

al., 2015).

From the point of view of control applications,

synergies between machine learning and posture con-

trol can find applications in the control of wearable

Figure 3: The pipeline of the learning problem is presented

in Lippi et al.( 2020). The simulated scenario represents a

subject standing on a tilting support surface. The tilt profile

is a pseudo-random ternary sequence (PRTS) function for

all the simulations. The parameters of the simulations are

generated randomly and the output of the simulation is the

profile of the body COM sway. The training process, aim-

ing to identify the parameters, "reverses" the relationship

between the data: body sway, here transformed into a pic-

ture, is the input, and the parameters, centred around the

mean and divided by the variance of the training set ('Nor-

malization' block) are the target output. The identification

is formally a regression problem.

robots. Fig. 4. shows an example presenting the hy-

pothetical structure of the control system for a full-

body exoskeleton. The actuated ankle joint and the

fact that the robot’s geometry prevents the user from

having direct contact with the support surface implies

that the robot has to balance by itself. The balance and

posture control issues specific to legged humanoids

apply also to wearable robots. This implies the com-

plication of physical interactions between the robot

and the human. The figure provides a map of possible

applications of the ML approaches presented in the

examples (Section 2) for the components of the exo-

skeleton control.

Besides posture control and balance, a wearable

robot poses issues that have not been covered by the

presented examples and can still be solved with

proper integration of models and ML. Specifically, a

transparent transfer of voluntary movements between

the user and the robot requires the mapping of

trajectories between different kinematic structures,

even if the user’s joints are not necessarily coincident

with those of the robot (Godoy et al., 2018; Lee et al.,

2018). Machine learning techniques provide means to

also solve such problems (see for example (Makondo

et al., 2015). Learning trajectories and libraries of

trajectories associated with tasks, e.g. gait, can be

achieved by exploiting models for movement

representation such as movement primitives

(Paraschos et al., 2013; Schaal et al., 2005; Schaal,

2006) and the algorithms to generalize and transfer

ICPRAM 2021 - 10th International Conference on Pattern Recognition Applications and Methods

582

them. For tasks

Figure 4: Hypothetical configuration of a user wearing a full-body exoskeleton. The block diagram shows the two mechanical

components of the system (robot and user) as two separate blocks to highlight how their interaction is mediated by control

systems that can be tuned using machine learning and thereby can benefit from the integration of modeling. The robot model

"exos model” can integrate a learning process like the one presented in (Lippi, 2018). The control system parameters can be

tuned accordingly. The haptic feedback that the robot here produces, "physical interaction" block, could be designed on the

basis of human sensor fusion in order to map the behavior of the robot to match the perception of the user (for example, the

robot should be in equilibrium when the user perceives himself as being in equilibrium). For this purpose, using a model of

the user's posture control, the "user model", can be beneficial. On the other hand, such a model can also be used to anticipate

the user's movements in the block "intention recognition", which is used to provide commands to the control system of the

robot. Both the "Exos model” and the “User model” can be identified by means of machine learning (Lippi et al., 2020).

such as manipulations, where reaction forces may

reasonably be more important than the trajectories

themselves, models representing the stiffness of the

robot (e.g. Calinon, 2016; Calinon et al., 2007) or

specifications of the particular mechanical variables

(torques, velocity positions, etc.) involved in the task

can be used (Deimel, 2019b, 2019a). In all these cases

the models have a powerful regularization effect, in

that a model of human motor behavior can be learned

from a few samples, or even just a single sample

(Schaal, 2006).

The topic of reinforcement learning (RL) has not

been considered in specific examples. RL is a popular

way to solve problems where a measure of success

can be formalized (e.g, body sway amplitude, number

of falls of a robot) but the desired output may not be

explicitly available. An example can be the closed-

loop control in section 2.2 and in general the problem

of humanoid balance (e.g. in Phaniteja et al., 2018;

Vuga et al., 2013; Yang et al., 2017). As RL relies on

the exploration of a space of possible control policies

it can benefit substantially from training in

simulations (where making a mistake is not

expensive) and hence it can exploit posture control

models.

Overall, we contend that the proposed examples

suggest that knowledge of human behaviour models

(be they bio-inspired or just descriptive of a given

outcome) as well as models of human sensorimotor

functions are crucial for the analysis of human

behavioural data. The models may provide powerful

tools for the control of humanoid robots. Both the

functionality of the bio-inspired models and the

modern ML techniques will benefit from being

mutually integrated.

ACKNOWLEDGEMENTS

This work is supported by the project

COMTEST, a sub-project of

EUROBENCH (European Robotic

Framework for Bipedal Locomotion

Benchmarking,

www.eurobench2020.eu) funded by

the H2020 Topic ICT 27-2017 under

grant agreement number 779963.

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The Importance of Models in Data Analysis with Small Human Movement Datasets: Inspirations from Neurorobotics Applied to Posture

Control of Humanoids and Humans

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