Validation of a Lower Back 'Wearable'-Based Sit-to-Stand and Stand-to-Sit Algorithm for Patients With Parkinson's Disease and Older Adults in a Home-Like Environment

Autor: Minh H. Pham, Elke Warmerdam, Morad Elshehabi, Christian Schlenstedt, Lu-Marie Bergeest, Maren Heller, Linda Haertner, Joaquim J. Ferreira, Daniela Berg, Gerhard Schmidt, Clint Hansen, Walter Maetzler
Jazyk: angličtina
Rok vydání: 2018
Předmět:
030506 rehabilitation
gyroscope
Direction detection
Parkinson's disease
MSCA-ITN-ETN
Wearable computer
Grant number 721577
Accelerometer
Faculty of Medicine
lcsh:RC346-429
03 medical and health sciences
Unknown
0302 clinical medicine
Keep Control
Medizinische Fakultät
Wearables
Parkinson's Disease
Home-like Evironment
European Union (EU)
Horizon 2020
MSCA-ITN-ETN
Keep Control
Grant number 721577
medicine
Methods
ddc:6
In patient
ddc:610
older adults
lcsh:Neurology. Diseases of the nervous system
Stand to sit
Horizon 2020
Parkinson's Disease
postural transition
Sit to stand
business.industry
home-like activities
Wearables
article
medicine.disease
accelerometer
Dyskinesia
Neurology
PD patients
Home-like Evironment
Neurology (clinical)
medicine.symptom
European Union (EU)
0305 other medical science
business
Algorithm
ScholarlyArticle
030217 neurology & neurosurgery
Zdroj: Frontiers in Neurology
Frontiers in Neurology, Vol 9 (2018)
Frontiers in neurology 9, 652 (2018). doi:10.3389/fneur.2018.00652
Frontiers in Neurology.-Lausanne : Frontiers Media SA; 2018.-S.652:1-652:11.
DOI: 10.3389/fneur.2018.00652
Popis: Introduction: Impaired sit-to-stand and stand-to-sit movements (postural transitions, PTs) in patients with Parkinson's disease (PD) and older adults (OA) are associated with risk of falling and reduced quality of life. Inertial measurement units (IMUs, also called “wearables”) are powerful tools to monitor PT kinematics. The purpose of this study was to develop and validate an algorithm, based on a single IMU positioned at the lower back, for PT detection and description in the above-mentioned groups in a home-like environment.Methods: Four PD patients (two with dyskinesia) and one OA served as algorithm training group, and 21 PD patients (16 without and 5 with dyskinesia) and 11 OA served as test group. All wore an IMU on the lower back and were videotaped while performing everyday activities for 90–180 min in a non-standardized home-like environment. Accelerometer and gyroscope signals were analyzed using discrete wavelet transformation (DWT), a six degrees-of-freedom (DOF) fusion algorithm and vertical displacement estimation.Results: From the test group, 1,001 PTs, defined by video reference, were analyzed. The accuracy of the algorithm for the detection of PTs against video observation was 82% for PD patients without dyskinesia, 47% for PD patients with dyskinesia and 85% for OA. The overall accuracy of the PT direction detection was comparable across groups and yielded 98%. Mean PT duration values were 1.96 s for PD patients and 1.74 s for OA based on the algorithm (p < 0.001) and 1.77 s for PD patients and 1.51 s for OA based on clinical observation (p < 0.001).Conclusion: Validation of the PT detection algorithm in a home-like environment shows acceptable accuracy against the video reference in PD patients without dyskinesia and controls. Current limitations are the PT detection in PD patients with dyskinesia and the use of video observation as the video reference. Potential reasons are discussed.
Databáze: OpenAIRE
Externí odkaz: