Benjamin Filtjens1, Alice Nieuwboer2, Nicholas D'cruz2, Joke Spildooren3, Peter Slaets4, Bart Vanrumste5. 1. eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium; Intelligent Mobile Platform Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium. Electronic address: benjamin.filtjens@kuleuven.be. 2. Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001 Heverlee, Belgium. 3. Rehabilitation Research Center (REVAL), Department of Rehabilitation Sciences, Hasselt University, Agoralaan Gebouw A, 3590 Diepenbeek, Belgium. 4. Intelligent Mobile Platform Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium. 5. eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000 Leuven, Belgium.
Abstract
BACKGROUND: Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG). OBJECTIVE: To determine the validity of a data-driven approach for automated gait event detection. METHODS: 15 freezers were asked to complete several straight-line and 360 degree turning trials in a 3D gait laboratory during the off-period of their medication cycle. Trials that contained a freezing episode were indicated as freezing trials (FOG) and trials without a freezing episode were termed as functional gait (FG). Furthermore, the highly varied gait data between onset and termination of a FOG episode was excluded. A Temporal Convolutional Neural network (TCN) was trained end-to-end with lower extremity kinematics. A Bland-Altman analysis was performed to evaluate the agreement between the results of the proposed model and the manual annotations. RESULTS: For FOG-trials, F1 scores of 0.995 and 0.992 were obtained for IC and EC, respectively. For FG-trials, F1 scores of 0.997 and 0.999 were obtained for IC and EC, respectively. The Bland-Altman plots indicated excellent timing agreement, with on average 39% and 47% of the model predictions occurring within 10 ms from the manual annotations for FOG-trials and FG-trials, respectively. SIGNIFICANCE: These results indicate that our data-driven approach for detecting gait events in PD patients with FOG is sufficiently accurate and reliable for clinical applications.
BACKGROUND: Manual annotation of initial contact (IC) and end contact (EC) is a time consuming process. There are currently no robust techniques available to automate this process for Parkinson's disease (PD) patients with freezing of gait (FOG). OBJECTIVE: To determine the validity of a data-driven approach for automated gait event detection. METHODS: 15 freezers were asked to complete several straight-line and 360 degree turning trials in a 3D gait laboratory during the off-period of their medication cycle. Trials that contained a freezing episode were indicated as freezing trials (FOG) and trials without a freezing episode were termed as functional gait (FG). Furthermore, the highly varied gait data between onset and termination of a FOG episode was excluded. A Temporal Convolutional Neural network (TCN) was trained end-to-end with lower extremity kinematics. A Bland-Altman analysis was performed to evaluate the agreement between the results of the proposed model and the manual annotations. RESULTS: For FOG-trials, F1 scores of 0.995 and 0.992 were obtained for IC and EC, respectively. For FG-trials, F1 scores of 0.997 and 0.999 were obtained for IC and EC, respectively. The Bland-Altman plots indicated excellent timing agreement, with on average 39% and 47% of the model predictions occurring within 10 ms from the manual annotations for FOG-trials and FG-trials, respectively. SIGNIFICANCE: These results indicate that our data-driven approach for detecting gait events in PDpatients with FOG is sufficiently accurate and reliable for clinical applications.
Authors: Tecla Bonci; Francesca Salis; Kirsty Scott; Lisa Alcock; Clemens Becker; Stefano Bertuletti; Ellen Buckley; Marco Caruso; Andrea Cereatti; Silvia Del Din; Eran Gazit; Clint Hansen; Jeffrey M Hausdorff; Walter Maetzler; Luca Palmerini; Lynn Rochester; Lars Schwickert; Basil Sharrack; Ioannis Vogiatzis; Claudia Mazzà Journal: Front Bioeng Biotechnol Date: 2022-06-02