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Article

A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling

1
National Institute for Research in Computer Science and Automation (Inria), Camin Team, 34090 Montpellier, France
2
Le Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM), 34090 Montpellier, France
3
Institut Saint-Pierre (ISP), 34250 Palavas-les-Flots, France
4
Núcleo de Tecnologia Assistiva, Acessibilidade e Inovação (NTAAI), Universidade de Brasília (UnB), Brasília 70910-900, Brazil
5
Departamento de Engenharia Elétrica, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte 31270-901, Brazil
6
National Institute for Research in Computer Science and Automation (Inria), SED Service, 38330 Montbonnot, France
7
Rehabilitation Center Bouffard Vercelli, USSAP, 66000 Perpignan, France
*
Author to whom correspondence should be addressed.
Academic Editors: Milos Popovic, Samuel C.K. Lee and Kei Masani
Sensors 2021, 21(13), 4571; https://doi.org/10.3390/s21134571
Received: 10 May 2021 / Revised: 21 June 2021 / Accepted: 23 June 2021 / Published: 3 July 2021
Functional electrical stimulation (FES) is a technique used in rehabilitation, allowing the recreation or facilitation of a movement or function, by electrically inducing the activation of targeted muscles. FES during cycling often uses activation patterns which are based on the crank angle of the pedals. Dynamic changes in their underlying predefined geometrical models (e.g., change in seating position) can lead to desynchronised contractions. Adaptive algorithms with a real-time interpretation of anatomical segments can avoid this and open new possibilities for the automatic design of stimulation patterns. However, their ability to accurately and precisely detect stimulation triggering events has to be evaluated in order to ensure their adaptability to real-case applications in various conditions. In this study, three algorithms (Hilbert, BSgonio, and Gait Cycle Index (GCI) Observer) were evaluated on passive cycling inertial data of six participants with spinal cord injury (SCI). For standardised comparison, a linear phase reference baseline was used to define target events (i.e., 10%, 40%, 60%, and 90% of the cycle’s progress). Limits of agreement (LoA) of ±10% of the cycle’s duration and Lin’s concordance correlation coefficient (CCC) were used to evaluate the accuracy and precision of the algorithm’s event detections. The delays in the detection were determined for each algorithm over 780 events. Analysis showed that the Hilbert and BSgonio algorithms validated the selected criteria (LoA: +5.17/−6.34% and +2.25/−2.51%, respectively), while the GCI Observer did not (LoA: +8.59/−27.89%). When evaluating control algorithms, it is paramount to define appropriate criteria in the context of the targeted practical application. To this end, normalising delays in event detection to the cycle’s duration enables the use of a criterion that stays invariable to changes in cadence. Lin’s CCC, comparing both linear correlation and strength of agreement between methods, also provides a reliable way of confirming comparisons between new control methods and an existing reference. View Full-Text
Keywords: event detection; IMU; cyclic motion; rehabilitation; functional electrical stimulation; FES-cycling; gait cycle index event detection; IMU; cyclic motion; rehabilitation; functional electrical stimulation; FES-cycling; gait cycle index
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MDPI and ACS Style

Le Guillou, R.; Schmoll, M.; Sijobert, B.; Lobato Borges, D.; Fachin-Martins, E.; Resende, H.; Pissard-Gibollet, R.; Fattal, C.; Azevedo Coste, C. A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling. Sensors 2021, 21, 4571. https://doi.org/10.3390/s21134571

AMA Style

Le Guillou R, Schmoll M, Sijobert B, Lobato Borges D, Fachin-Martins E, Resende H, Pissard-Gibollet R, Fattal C, Azevedo Coste C. A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling. Sensors. 2021; 21(13):4571. https://doi.org/10.3390/s21134571

Chicago/Turabian Style

Le Guillou, Ronan, Martin Schmoll, Benoît Sijobert, David Lobato Borges, Emerson Fachin-Martins, Henrique Resende, Roger Pissard-Gibollet, Charles Fattal, and Christine Azevedo Coste. 2021. "A Novel Framework for Quantifying Accuracy and Precision of Event Detection Algorithms in FES-Cycling" Sensors 21, no. 13: 4571. https://doi.org/10.3390/s21134571

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