Background: Anterior cruciate ligament (ACL) injuries commonly reduce knee stability and increase joint loading, often leading to compensatory gait alterations that may increase injury risk. Functional electrical stimulation (FES) of the biceps femoris long head (BFLH) during the gait stance may improve knee stability by reducing harmful joint loading, but the effects on voluntary muscle control remain unclear.

Research question: This study examined whether FES of the BFLH during the stance phase of the gait reduces ACL-relevant knee joint loading in healthy adults and whether it alters voluntary muscle control. Additionally, the use of gluteus maximus (GLMAX) sEMG as a proxy for BFLH activation was assessed.

Method: Nine healthy participants walked on a treadmill under control and FES-assisted conditions. Kinematic, kinetic, and sEMG data were analyzed using statistical parametric mapping and linear mixed-effects models.

Results: FES of the BFLH significantly reduced internal knee rotation moment (KRM) with 9.37% during 42–48% of the gait cycle (p = 0.0002; d = 0.42). Knee adduction moment (KAM) showed non-significant reductions in both legs (non-stimulated: p = 0.0317, d = 0.18; stimulated: p = 0.0492, d = 0.37). Knee abduction angle (KAA) and knee rotation angle (KRA) showed no significant changes (p > 0.05). In sEMG analysis, inconsistent timing between GLMAX and BFLH activation indicated GLMAX is not a reliable surrogate for estimating BFLH activity. Regarding voluntary control, only peak KAM increased slightly over strides during FES-assisted walking (p = 0.006), possibly due to muscle fatigue. No significant retention or after-effects were observed.

Conclusion: Targeted FES of the BFLH can reduce ACL-relevant knee loading without impairing voluntary motor control. sEMG results highlight the need for direct BFLH monitoring, as GLMAX is an unreliable proxy. These findings support further exploration of FES strategies for ACL injury prevention and rehabilitation.

Abstract: Gait event detection (GED) plays an important role in clinical gait analysis and rehabilitation. Real-time detection of temporal gait features within the gait cycle is essential for closed-loop control of wearable assistive devices and neuroprostheses. Machine-learning algorithms used in these systems require training datasets of real-world walking, which are currently collected using insole footswitches. However, insole footswitches suffer from performance and reliability issues. This study evaluates the use of inertial measurement units (IMUs) as an alternative for detecting foot-ground contact events during gait. The detection performance of five IMU placement locations and four rule-based detection algorithms was assessed in laboratory and real-world settings, using force plates and footswitches as ground-truth reference. In the laboratory, the heel-mounted IMU combined with a vertical jerk–based algorithm achieved an F1-score of 97.6% and a mean timing error of –1.5 ± 1.5 ms relative to force plates. Although insole footswitches achieved a comparable F1-score (97.2%), they showed significantly larger timing errors (17.7 ± 11.7 ms, p <
0.001). In the real-world environments, the heel-mounted IMU combined with a zero-velocity update–based detection algorithm achieved a mean timing error of –5.0 ± 23.4 ms relative to the footswitches. These findings show that a single heel-mounted IMU combined with a rule-based detection algorithm offers a practical and accurate alternative to insole footswitches for collecting gait datasets in real-world environments.