Details
| Original language | English |
|---|---|
| Title of host publication | 27th European Signal Processing Conference (EUSIPCO 2019) |
| Subtitle of host publication | Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 5 |
| ISBN (electronic) | 9789082797039 |
| ISBN (print) | 9781538673003 |
| Publication status | Published - 2019 |
| Event | 27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain Duration: 2 Sept 2019 → 6 Sept 2019 |
Publication series
| Name | European Signal Processing Conference (EUSIPCO) |
|---|---|
| ISSN (Print) | 2219-5491 |
| ISSN (electronic) | 2076-1465 |
Abstract
One of the key issues of multi-sensory digital healthcare and therapy is the reliability and user compliance of the applied sensor system. In the context of digital gait analysis and rehabilitation, different technologies have been proposed allowing objective gait assessment and precise quantification of the rehabilitation progress using Inertial Measurement Unit (IMU) platforms. However, this depends largely on the estimation accuracy of the kinematics (body joint angles). This paper presents the concept of a digital equivalent based on the Memory Polynomial Model (MPM) to reduce the number of IMUs needed for the measurements and to simulate the physical mechanism of lower body joint angles based on acceleration data. The MPM parameter estimation is based on the Least Square (LS) approach and is performed using accelerometer records of non-pathological gait patterns. The Normalized Mean Square Error (NMSE) is used to evaluate the performance of the model. According to the results an NMSE of -20 dB is achieved, which indicates the great potential of applying the MPM to develop a digital twin. That kind of twin can serve as a prototype of the physical counterpart to improve the wearability of the sensor system and to reduce physically induced measurement errors as well.
Keywords
- Digital twin, Gait rehabilitation, IMU, Machine learning, Multi-sensor integration, Nonlinear time-varying modeling
ASJC Scopus subject areas
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Electrical and Electronic Engineering
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27th European Signal Processing Conference (EUSIPCO 2019) : Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (European Signal Processing Conference (EUSIPCO)).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Efficiency of the Memory Polynomial Model in Realizing Digital Twins for Gait Assessment
AU - Alcaraz, Javier Conte
AU - Moghaddamnia, Sanam
AU - Fuhrwerk, Martin
AU - Peissig, Jürgen
PY - 2019
Y1 - 2019
N2 - One of the key issues of multi-sensory digital healthcare and therapy is the reliability and user compliance of the applied sensor system. In the context of digital gait analysis and rehabilitation, different technologies have been proposed allowing objective gait assessment and precise quantification of the rehabilitation progress using Inertial Measurement Unit (IMU) platforms. However, this depends largely on the estimation accuracy of the kinematics (body joint angles). This paper presents the concept of a digital equivalent based on the Memory Polynomial Model (MPM) to reduce the number of IMUs needed for the measurements and to simulate the physical mechanism of lower body joint angles based on acceleration data. The MPM parameter estimation is based on the Least Square (LS) approach and is performed using accelerometer records of non-pathological gait patterns. The Normalized Mean Square Error (NMSE) is used to evaluate the performance of the model. According to the results an NMSE of -20 dB is achieved, which indicates the great potential of applying the MPM to develop a digital twin. That kind of twin can serve as a prototype of the physical counterpart to improve the wearability of the sensor system and to reduce physically induced measurement errors as well.
AB - One of the key issues of multi-sensory digital healthcare and therapy is the reliability and user compliance of the applied sensor system. In the context of digital gait analysis and rehabilitation, different technologies have been proposed allowing objective gait assessment and precise quantification of the rehabilitation progress using Inertial Measurement Unit (IMU) platforms. However, this depends largely on the estimation accuracy of the kinematics (body joint angles). This paper presents the concept of a digital equivalent based on the Memory Polynomial Model (MPM) to reduce the number of IMUs needed for the measurements and to simulate the physical mechanism of lower body joint angles based on acceleration data. The MPM parameter estimation is based on the Least Square (LS) approach and is performed using accelerometer records of non-pathological gait patterns. The Normalized Mean Square Error (NMSE) is used to evaluate the performance of the model. According to the results an NMSE of -20 dB is achieved, which indicates the great potential of applying the MPM to develop a digital twin. That kind of twin can serve as a prototype of the physical counterpart to improve the wearability of the sensor system and to reduce physically induced measurement errors as well.
KW - Digital twin
KW - Gait rehabilitation
KW - IMU
KW - Machine learning
KW - Multi-sensor integration
KW - Nonlinear time-varying modeling
UR - http://www.scopus.com/inward/record.url?scp=85075608110&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2019.8903143
DO - 10.23919/EUSIPCO.2019.8903143
M3 - Conference contribution
AN - SCOPUS:85075608110
SN - 9781538673003
T3 - European Signal Processing Conference (EUSIPCO)
BT - 27th European Signal Processing Conference (EUSIPCO 2019)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 27th European Signal Processing Conference, EUSIPCO 2019
Y2 - 2 September 2019 through 6 September 2019
ER -