Ankle Exoskeletons in Walking and Load-Carrying Tasks: Insights into Biomechanics and Human-Robot Interaction

TL;DR

This work tackles the lack of standardized frameworks for evaluating ankle exoskeleton biomechanics and human–robot interaction during walking and load-carrying. It integrates inertial motion capture, EMG, and on-board sensing in a SmartOs-based ankle exoskeleton to quantify kinematics, muscle activity, and HRI torque across three experimental tasks, using a descriptive, non-statistical approach due to a small sample size. The findings show that the exoskeleton can preserve gait patterns while modestly reducing ankle RoM and muscle activity, but HRI torque dynamics reveal areas for improved synchronization, particularly under load and in active control. The study contributes a rigorous methodology and interpretive insights that can inform adaptive, personalized control strategies and cross-device comparisons in the development of wearable robotic assistance for locomotion and load-carrying tasks.

Abstract

Background: Lower limb exoskeletons can enhance quality of life, but widespread adoption is limited by the lack of frameworks to assess their biomechanical and human-robot interaction effects, which are essential for developing adaptive and personalized control strategies. Understanding impacts on kinematics, muscle activity, and HRI dynamics is key to achieve improved usability of wearable robots. Objectives: We propose a systematic methodology evaluate an ankle exoskeleton's effects on human movement during walking and load-carrying (10 kg front pack), focusing on joint kinematics, muscle activity, and HRI torque signals. Materials and Methods: Using Xsens MVN (inertial motion capture), Delsys EMG, and a unilateral exoskeleton, three experiments were conducted: (1) isolated dorsiflexion/plantarflexion; (2) gait analysis (two subjects, passive/active modes); and (3) load-carrying under assistance. Results and Conclusions: The first experiment confirmed that the HRI sensor captured both voluntary and involuntary torques, providing directional torque insights. The second experiment showed that the device slightly restricted ankle range of motion (RoM) but supported normal gait patterns across all assistance modes. The exoskeleton reduced muscle activity, particularly in active mode. HRI torque varied according to gait phases and highlighted reduced synchronization, suggesting a need for improved support. The third experiment revealed that load-carrying increased GM and TA muscle activity, but the device partially mitigated user effort by reducing muscle activity compared to unassisted walking. HRI increased during load-carrying, providing insights into user-device dynamics. These results demonstrate the importance of tailoring exoskeleton evaluation methods to specific devices and users, while offering a framework for future studies on exoskeleton biomechanics and HRI.

Figures (17)

• Figure 1: SmartOs architecture and exoskeleton's mechanical structure.
• Figure 2: Experimental setup for walking data collection. (a) Treadmill. (b) Ankle-H2 module. (c) LLOS board. (d) CCU board. (e) Delsys Trigno Workstation with wireless communication with the Delsys sensors. (f) Xsens Awinda station with wireless communication with the Xsens IMUs. (g) Laptop running Trigno Control Utility and Xsens MVN 2021.2 with wireless communication with CCU.
• Figure 3: Ankle exoskeleton's angle (orange), motor torque (green) and HRI torque (blue) for DF and PF movements in passive control.
• Figure 4: Angular position of the right ankle joint during one gait cycle, comparing different assistance conditions. The shadowed region represents the swing phase.
• Figure 5: Average gait cycle of muscle activity of TA (blue) and GM (green) of the two participants. The shadow region marks the swing phase.