Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910926453604352 |
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| author | Tagliaferri, Maria T. Campeggi, Leonardo Beck, Owen N. Kang, Inseung |
| author_facet | Tagliaferri, Maria T. Campeggi, Leonardo Beck, Owen N. Kang, Inseung |
| contents | Falls during daily ambulation activities are a leading cause of injury in older adults due to delayed physiological responses to disturbances of balance. Lower-limb exoskeletons have the potential to mitigate fall incidents by detecting and reacting to perturbations before the user. Although commonly used, the standard metric for perturbation detection, whole-body angular momentum, is poorly suited for exoskeleton applications due to computational delays and additional tunings. To address this, we developed a novel ground perturbation detector using lower-limb kinematic states during locomotion. To identify perturbations, we tracked deviations in the kinematic states from their nominal steady-state trajectories. Using a data-driven approach, we further optimized our detector with an open-source ground perturbation biomechanics dataset. A pilot experimental validation with five able-bodied subjects demonstrated that our model distinguished perturbed from unperturbed gait cycles with 98.8% accuracy and only a delay of 23.1% within the gait cycle, outperforming the benchmark by 47.7% in detection accuracy. The results of our study offer exciting promise for our detector and its potential utility to enhance the controllability of robotic assistive exoskeletons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06985 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion Tagliaferri, Maria T. Campeggi, Leonardo Beck, Owen N. Kang, Inseung Robotics Falls during daily ambulation activities are a leading cause of injury in older adults due to delayed physiological responses to disturbances of balance. Lower-limb exoskeletons have the potential to mitigate fall incidents by detecting and reacting to perturbations before the user. Although commonly used, the standard metric for perturbation detection, whole-body angular momentum, is poorly suited for exoskeleton applications due to computational delays and additional tunings. To address this, we developed a novel ground perturbation detector using lower-limb kinematic states during locomotion. To identify perturbations, we tracked deviations in the kinematic states from their nominal steady-state trajectories. Using a data-driven approach, we further optimized our detector with an open-source ground perturbation biomechanics dataset. A pilot experimental validation with five able-bodied subjects demonstrated that our model distinguished perturbed from unperturbed gait cycles with 98.8% accuracy and only a delay of 23.1% within the gait cycle, outperforming the benchmark by 47.7% in detection accuracy. The results of our study offer exciting promise for our detector and its potential utility to enhance the controllability of robotic assistive exoskeletons. |
| title | Ground Perturbation Detection via Lower-Limb Kinematic States During Locomotion |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.06985 |