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<p>Personalized exoskeleton assistance has substantial potential to enhance human locomotion performance. However, current human-in-the-loop optimization methods for generating personalized assistance are cumbersome and time-consuming. Since humans can perceive locomotion through internal sensory feedback, user preference-based self-tuning may facilitate the individualization of exoskeleton assistance to meet individual needs. Here, we explore a user-driven human-in-the-loop tuning approach for walking assistance, hypothesizing that individuals can quickly find their preferred personalized assistance through subjective perception. We conducted experiments with 11 healthy participants, who were instructed to tune four control parameters while wearing a hip exoskeleton and walking on a treadmill. The tuning procedure concluded when participants indicated that they had found their preferred assistance. Then we surveyed the sense of agency to assess the user experience. We evaluated the effort of walking with the preferred setting and explored the metabolic cost landscape around it. Participants identified their preference in 10.9 ± 0.9 min, while testing 30.5 settings and spending 18.7 s per setting on average. Preferred assistance profiles varied widely between participants, with timing differences of up to 22.5% of the stride time. The metabolic cost of walking with the preferred assistance was reduced by 16.6 ± 1.1% compared to walking with the exoskeleton in a zero-torque condition. Timing deviations of up to ±8% of the stride time did not significantly affect metabolic cost reduction, indicating the robustness of the preferred assistance profiles. Significant changes in the sense of agency between unassisted and assisted walking demonstrate its sensitivity to partial exoskeleton assistance. The results highlight the potential of preference-based user-tuning while suggesting that additional guidance throughout the user-tuning procedure may support a systematic exploration, thereby advancing the preference-based individualization of exoskeleton assistance.</p>

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User preference-based human-in-the-loop tuning of exoskeleton assistance during walking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Niklas Schäfer,
  • Guoping Zhao,
  • Bowen Li,
  • Mario Kupnik,
  • André Seyfarth,
  • Philipp Beckerle,
  • Martin Grimmer

摘要

Personalized exoskeleton assistance has substantial potential to enhance human locomotion performance. However, current human-in-the-loop optimization methods for generating personalized assistance are cumbersome and time-consuming. Since humans can perceive locomotion through internal sensory feedback, user preference-based self-tuning may facilitate the individualization of exoskeleton assistance to meet individual needs. Here, we explore a user-driven human-in-the-loop tuning approach for walking assistance, hypothesizing that individuals can quickly find their preferred personalized assistance through subjective perception. We conducted experiments with 11 healthy participants, who were instructed to tune four control parameters while wearing a hip exoskeleton and walking on a treadmill. The tuning procedure concluded when participants indicated that they had found their preferred assistance. Then we surveyed the sense of agency to assess the user experience. We evaluated the effort of walking with the preferred setting and explored the metabolic cost landscape around it. Participants identified their preference in 10.9 ± 0.9 min, while testing 30.5 settings and spending 18.7 s per setting on average. Preferred assistance profiles varied widely between participants, with timing differences of up to 22.5% of the stride time. The metabolic cost of walking with the preferred assistance was reduced by 16.6 ± 1.1% compared to walking with the exoskeleton in a zero-torque condition. Timing deviations of up to ±8% of the stride time did not significantly affect metabolic cost reduction, indicating the robustness of the preferred assistance profiles. Significant changes in the sense of agency between unassisted and assisted walking demonstrate its sensitivity to partial exoskeleton assistance. The results highlight the potential of preference-based user-tuning while suggesting that additional guidance throughout the user-tuning procedure may support a systematic exploration, thereby advancing the preference-based individualization of exoskeleton assistance.