Abstract
The development of a sensor that can measure balance during quiet standing and predict stepping response in the event of perturbation has many clinically relevant applica- tions, including closed-loop control of a neuroprothesis for standing. This study investigated the feasibility of an algorithm that can predict in real-time when an able-bodied individual who is quietly standing will have to make a step to compensate for an external perturbation. Anterior and posterior perturbations were performed on 16 able-bodied subjects using a pul- ley system with a dropped weight. A linear relationship was found between the peak center of pressure (COP) velocity and the peak COP displacement caused by the perturbation. This result suggests that one can predict when a person will have to make a step based on COP velocity measurements alone. Another important feature of this finding is that the peak COP velocity occurs considerably before the peak COP displacement. As a result, one can predict if a subject will have to make a step in response to a perturbation sufficiently ahead of the time when the subject is actually forced to make the step. The proposed instability detection algorithm will be implemented in a sensor system using insole sheets in shoes with minitur- ized pressure sensors by which the COPv can be continuously measured. The sensor system will be integrated in a closed-loop feedback system with a neuroprosthesis for standing in the near future.
Highlights
Neuro-musculo-skeletal disorders often result in degradation of the human balance control system
To rectify the problem in this study we have focused our efforts at analyzing the dynamic properties of the center of pressure (COP), i.e., COP velocity (COPv), and using that information to predict stepping resulted from perturbations during quiet standing
We found a linear relationship between the peak COPv and the peak COP caused by a perturbation applied to an able-bodied individual during quiet standing
Summary
Neuro-musculo-skeletal disorders often result in degradation of the human balance control system. The long term goal of this study is to develop a neuroprothesis using a functional electrical stimulation (FES) technology [1] which can be used by people with balance impairment in their daily life. The natural expansion of this work is the development of a sensor that can measure balance in real-time during quiet standing. This sensor can be used in close-loop with a neuroprosthesis for quiet standing to facilitate balance control. The main focus of this article is the development of an algorithm for instability detection that can be later used as an integral part of a sensor for measuring balance and stability during quiet standing. The proposed algorithm will be implemented in a miniaturized footsole sensor system
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