Analysis of Human Body Motion:

Towards Comprehensive Pattern Recognition Techniques

in the Processing of Measuring Data

U. Betz1, J. Konradi1, J. Huthwelker2 Cl. Wolf1, F. Werthmann2, R. Westphal3,

C. Dindorf4, B. Taetz4, G. Bleser4, F. Bodem1, Ph. Drees2

1 Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Center of the Johannes Gutenberg University Mainz 2 Department of Orthopedics and Trauma Surgery, University Medical Center of the Johannes Gutenberg University Mainz 3 Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz 4 Department Computer Science, AG wearHEALTH and AG Augmented Vision, TU Kaiserslautern and DFKI

17th International Conference on Biomedical Engineering 9 -12 December 2019, Singapore

Analysis of Human Body Motion:

Towards Comprehensive Pattern Recognition Techniques

in the Processing of Measuring Data

U. Betz1, J. Konradi1, J. Huthwelker2 Cl. Wolf1, F. Werthmann2, R. Westphal3,

C. Dindorf4, B. Taetz4, G. Bleser4, F. Bodem1, Ph. Drees2

1 Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Center of the Johannes Gutenberg University Mainz 2 Department of Orthopedics and Trauma Surgery, University Medical Center of the Johannes Gutenberg University Mainz 3 Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz 4 Department Computer Science, AG wearHEALTH and AG Augmented Vision, TU Kaiserslautern and DFKI

17th International Conference on Biomedical Engineering 9 -12 December 2019, Singapore

Conflicts of Interests: none

MotionLab DIERS Formetric III 4D™ analyzing system

Separate, aggregated DICAM parameters

Internal visualization of the course

Raw data export tool for continuous measuring and visualization is implemented

Software adjustments and data management

Export of raw data about 102 spinal parameters is realized.

Additional leg and foot parameters, ventral torso, EMG, and theoretically unlimited parameters would be available

Software adjustments and data management

Course of rotation across three gait cycles of selected vertebral bodies

Implementation of foot pressure data as variables in the raw data of the spinal model

Transformation in a Standardized Gait Cycle (SGC)

Linear transformation of the number of observations onto a scale from 0-100

Averaging of data from three gait cycles

Interpolating splines are applied to smooth curve progressions

Framework Project

„The rasterstereographic investigation of intersegmental spinal movement pattern in healthy participants according to phases of gait at different walking speeds - a prospective cross-sectional study“

Participants:

•201 structurally and functionally healthy participants in 3 age groups

(N=67 per group)

•Preliminary results of n=134

•Current focus on 5km/h walking speed and rotational results

Means within Standardized Gait Cycle (SGC)

Mean course of all segments through SGC 5 km/h (n=134)

Means within Standardized Gait Cycle (SGC)

Most rotation at T7

Means within Standardized Gait Cycle (SGC)

Maxima/Minima pelvis and lumbar spine occur sequentially (one after another) with similar amplitudes at beginning of stance and swing phase)

Means within Standardized Gait Cycle (SGC)

Maxima/Minima of thoracic spine occur nearly simultaneously but with very different amplitudes

Point of Intersection: not static but dynamic!

Sum of absolute values of vertebral body rotation across all subjects of SGC (rotational mean of n=134)

Scatter plots of maxima (left, blue) and minima (right, red) within SGC

high pelvic and lumbar individuality

Individual courses of spinal rotary motion during gait at 5 km/h within SGC

Mean courses in SGC with 5% an 95% percentiles

Individual courses of spinal rotary motion during gait at 5 km/h within SGC

Excellent intra-individual correlation and consistency

25 healthy people

3 measurements (day 1, day 2, 4 weeks after)

two walking speeds (2 & 4 km/h)

ICC > .93

Cronbach´s alpha > .93

DAY1

DAY2

4WEEKS

Example 1 - 4 km/h - day1

Example 1 - 4 km/h - day 2

Example 1 - 4 km/h – 4 weeks after

Example 2 - 4 km/h - day 1

Example 2 - 4 km/h - day 2

Example 2 - 4 km/h – 4 weeks after

Summary I

Modern motion analysis systems generate an ever increasing enormous number of measuring data of a different kind

Using our methodological advances a number of parameters can be evaluated by usual statistic analysis procedures

Very interesting results can already be achieved in this manner

BUT

Even among the healthy, there is a huge inter-individual variation across the results

Narrowing the view to single parameters entails the risk of misinterpretation

A pattern recognition by systematic analysis simultaneously including all data should be done

This necessitates new dedicated data processing approaches

Supervised learning and expert knowledge augmentation via data-

driven methods (general procedure)

Instead of only taking into account hand-crafted features based on expert knowledge:

Training data (N parameters)

Feature extraction

/ engineering (e.g. DFS)

M >> N features (automatically

generated + hand crafted)

Expert knowledge

Automatic feature selection

/ ranking (e.g. mRMR)

L << M most relevant features

Classifier selection

(e.g. SVM) Classifier

Example: gender classification

Most relevant parameters for this classification task were previously not

considered

Enables objective, data driven extraction of discriminative features, e.g. based on L3 movement in frontal plane

Outlook:

Representation learning to identify discriminative motion patterns

Unsupervised representation learning / feature extraction maps high-dimensional input data to low-dimensional latent spaces (distributions)

Investigate different priors / models on the latent spaces to support learning of meaningful representations, e.g. disentanglement of motion generating factors

Investigation, whether the resulting reduced representation allows for recognizing / clustering e.g. diseases (e.g. back pain, osteoarthritis) or even identifying individuals in a more reliable way compared to the original representation

Input data (high dim.)

Latent space (lower dim.)

with meaningful structure

Liu, Y. et al., 2018

Thank you for

your interest

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