ArticleBrain sciences2023
Feasibility of an Intelligent Algorithm Based on an Assist-as-Needed Controller for a Robot-Aided Gait Trainer (Lokomat) in Neurological Disorders: A Longitudinal Pilot Study.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The impact of robot-assisted training on gait, balance, and endurance in patients with partial spinal cord injuries: insights from rehabilitation.Journal of neuroengineering and rehabilitation · 2026Trial
- Assessing walking ability using a robotic gait trainer: opportunities and limitations of assist-as-needed control in spinal cord injury.Journal of neuroengineering and rehabilitation · 2023Trial
- An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model.Bioengineering (Basel, Switzerland) · 2024Article
- Clinical Feasibility of Applying Immersive Virtual Reality during Robot-Assisted Gait Training for Individuals with Neurological Diseases: A Pilot Study.Brain sciences · 2024Article
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6 authors.
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Abstract
Most robotic gait assisted devices are designed to provide constant assistance during the training without taking into account each patient's functional ability. The Lokomat offers an assist-as-needed control via the integrated exercise "Adaptive Gait Support" (AGS), which adapts the robotic support based on the patient's abilities. The aims of this study were to examine the feasibility and characteristics of the AGS during long-term application. Ten patients suffering from neurological diseases underwent an 8-week Lokomat training with the AGS. They additionally performed conventional walking tests and a robotic force measurement. The difference between robotic support during adaptive and conventional training and the relationship between the robotic assessment and the conventional walking and force tests were examined. The results show that AGS is feasible during long-term application in a heterogeneous population. The support during AGS training in most of the gait phases was significantly lower than during conventional Lokomat training. A relationship between the robotic support level determined by the AGS and conventional walking tests was revealed. Moreover, combining the isometric force data and AGS data could divide patients into clusters, based on their ability to generate high forces and their level of motor control. AGS shows a high potential in assessing patients' walking ability, as well as in providing challenging training, e.g., by automatically adjusting the robotic support throughout the whole gait cycle and enabling training at lower robotic support.
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