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

Abstract

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.

Development of a High-Power Capacity Open Source Electrical Stimulation System to Enhance Research into FES-Assisted Devices: Validation of FES Cycling

“…If other volunteers had participated in the experiment, the mechanical adjustments would have to be carried out individually, which would have demanded a relative effort to define the best FES pulse parametrization (i.e., the stimulation range of each angle). Studies have suggested the use of IMUs as a better approach for cycling phase detection, which could reduce this effort and improve FE -pedaling by SCI individuals [35][36][37].…”

ICEP: An Instrumented Cycling Ergometer Platform for the Assessment of Advanced FES Strategies

“…In order to derive a stimulation pattern, several procedures could be considered. One could, for instance, detect the maximal force and then set the start angle at the first zero transition before the maximum and the stop angle at the first zero transition after the maximum [ 14 ]. Such a range would likely maximize the power output, but also the muscular fatigue.…”

“…The increase in FES cyclists, which include individuals with various types of motor disabilities, has created a growing need for assessment tools in order to determine the most efficient electrical stimulation strategies to adapt to the different needs, such as optimizing the stimulation patterns (the timing in which the cyclist’s leg muscles are activated) in order to achieve a smooth and powerful cycling movement. Furthermore, selecting the stimulation parameters, such as electrical pulse shape and charge density, that will maximize the torque generated by the muscle contraction while minimizing the subsequent muscular fatigue will have a major influence over the cycling efficiency and its potential health benefits [ 12 , 13 , 14 , 15 , 16 , 17 ].…”

OIDA: An optimal interval detection algorithm for automatized determination of stimulation patterns for FES-Cycling in individuals with SCI

“…knee-extension, knee-flexion) during cycling. Previously, research groups have investigated the use of inertial measurement units (IMUs) to control stimulation in FES-Cycling, by assessing the thigh [ 13 , 14 ] or knee angle [ 15 , 16 ]. The motivation for this was to create a simpler definition of stimulation intervals, which automatically adapts for certain variations during cycling (e.g.…”