Efficiency of the Memory Polynomial Model in Realizing Digital Twins for Gait Assessment
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Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
27th European Signal Processing Conference (EUSIPCO) -- SEP 02-06, 2019 -- A Coruna, SPAIN
Anahtar Kelimeler
Gait rehabilitation, Nonlinear time-varying modeling, IMU, Multi-sensor integration, Digital twin, Machine learning
Kaynak
2019 27th European Signal Processing Conference (Eusipco)
