Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 28th European Signal Processing Conference |
| Untertitel | EUSIPCO 2020 - Proceedings |
| Seiten | 1333-1337 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9789082797053 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Niederlande Dauer: 24 Aug. 2020 → 28 Aug. 2020 |
Publikationsreihe
| Name | European Signal Processing Conference |
|---|---|
| ISSN (Print) | 2219-5491 |
Abstract
Monitoring the progress of patients during the rehabilitation process after an operation is beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. The supervised methods used for this in the literature need data labeling, which is a time and cost-intensive procedure. In this paper, we propose Deep Convolutional Neural Network (DCNN) for monitoring the progress of the rehabilitation, utilizing the kinematic data from a Wearable Sensor System (WSS). The WSS provides three-dimensional linear acceleration and angular velocity from multiple body parts such as the lower back and lower limbs during walking at any speed on level ground. Twelve patients with hip unilateral arthroplasty completed two weeks of gait training after the operation. The classification results of different Inertial Measurement Unit (IMU) placements revealed that the IMU placed at thigh achieved the highest accuracy. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring. This approach provides an objective and evidence-based way of understanding clinically important changes in human movement patterns in response to exercise therapy.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Signalverarbeitung
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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28th European Signal Processing Conference: EUSIPCO 2020 - Proceedings. 2021. S. 1333-1337 (European Signal Processing Conference).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Monitoring the rehabilitation progress using a DCNN and kinematic data for digital healthcare
AU - Alcaraz, Javier Conte
AU - Moghaddamnia, Sanam
AU - Penner, Maxim
AU - Peissig, Jürgen
PY - 2021
Y1 - 2021
N2 - Monitoring the progress of patients during the rehabilitation process after an operation is beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. The supervised methods used for this in the literature need data labeling, which is a time and cost-intensive procedure. In this paper, we propose Deep Convolutional Neural Network (DCNN) for monitoring the progress of the rehabilitation, utilizing the kinematic data from a Wearable Sensor System (WSS). The WSS provides three-dimensional linear acceleration and angular velocity from multiple body parts such as the lower back and lower limbs during walking at any speed on level ground. Twelve patients with hip unilateral arthroplasty completed two weeks of gait training after the operation. The classification results of different Inertial Measurement Unit (IMU) placements revealed that the IMU placed at thigh achieved the highest accuracy. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring. This approach provides an objective and evidence-based way of understanding clinically important changes in human movement patterns in response to exercise therapy.
AB - Monitoring the progress of patients during the rehabilitation process after an operation is beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. The supervised methods used for this in the literature need data labeling, which is a time and cost-intensive procedure. In this paper, we propose Deep Convolutional Neural Network (DCNN) for monitoring the progress of the rehabilitation, utilizing the kinematic data from a Wearable Sensor System (WSS). The WSS provides three-dimensional linear acceleration and angular velocity from multiple body parts such as the lower back and lower limbs during walking at any speed on level ground. Twelve patients with hip unilateral arthroplasty completed two weeks of gait training after the operation. The classification results of different Inertial Measurement Unit (IMU) placements revealed that the IMU placed at thigh achieved the highest accuracy. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring. This approach provides an objective and evidence-based way of understanding clinically important changes in human movement patterns in response to exercise therapy.
KW - CNN
KW - Digital Healthcare
KW - Gait Rehabilitation
KW - IMU
KW - Machine Learning
KW - Progress Monitoring
KW - Therapy Control
UR - http://www.scopus.com/inward/record.url?scp=85099276267&partnerID=8YFLogxK
U2 - 10.23919/Eusipco47968.2020.9287324
DO - 10.23919/Eusipco47968.2020.9287324
M3 - Conference contribution
AN - SCOPUS:85099276267
SN - 9781728150017
T3 - European Signal Processing Conference
SP - 1333
EP - 1337
BT - 28th European Signal Processing Conference
T2 - 28th European Signal Processing Conference, EUSIPCO 2020
Y2 - 24 August 2020 through 28 August 2020
ER -