Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes

Rear-foot, mid-foot and fore-foot motion during the stance phase of gait

Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.

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Removal of Movement Artifact from High-Density EEG Recorded During Walking and Running

Automatic detection of gait events using kinematic data

Electrocortical activity is coupled to gait cycle phase during treadmill walking.

A kinematic-based technique for the estimation of the times at which gait events occur is presented. A kinematic-based model (KM) is defined by the trajectory of a point, which has an anatomically fixed location on the subject's body, about a time at which a measurement system defined gait event takes place. The times at which subsequent occurrences of the gait event takes place are determined by identifying the kinematic pattern that best fits the previously defined KM. The results of an experiment that used the gait patterns of a normal and a pathological walker indicate that the accuracy of the algorithm is limited by the kinematic data sampling interval and that optimal kinematic predictors of gait event times occur within the primary (sagittal) plane of motion. The technique is intended to obviate the need for multiple force plates, instrumented floors and instruments which are worn by the subject for the purpose of determining the times at which gait events occur.

N.L. Roman

Papers

Kinematic-based technique for event time determination during gait.

A video-based technique for measuring ankle-subtalar motion during stance.