Automatic detection of gait events using kinematic data

O'Connor, C.M.; Thorpe, Susannah K. S.; O’Malley, Mark; Vaughan, Christopher L.

doi:10.1016/j.gaitpost.2006.05.016

Cited by 450 publications

(304 citation statements)

References 15 publications

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“…For the Full-body Task, an analysis of the kinematic data was performed using Matlab R_2012a software. All the signals were digitally low-pass filtered at 7 Hz, using a second order Butterworth filter (O'Connor, Thorpe, O'Malley, & Vaughan, 2007). The Center of Mass displacement in three 14 directions (X, Y and Z) was calculated using the Zatsiorsky formula (Zatsiorsky, Seluyanov, & Chugunova, 1990), where the Center of Mass (COM) was derived from 12 defined segments and its total displacement computed.…”

Section: Kinematic Data Analysismentioning

confidence: 99%

Expert players accurately detect an opponent’s movement intentions through sound alone.

Camponogara¹,

Rodger²,

Craig³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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Sounds offer a rich source of information about events taking place in our physical and social environment. However, outside the domains of speech and music, little is known about whether humans can recognize and act upon the intentions of another agent’s actions detected through auditory information alone. In this study we assessed whether intention can be inferred from the sound an action makes, and in turn, whether this information can be used to prospectively guide movement. In 2 experiments experienced and novice basketball players had to virtually intercept an attacker by listening to audio recordings of that player’s movements. In the first experiment participants had to move a slider, while in the second one their body, to block the perceived passage of the attacker as they would in a real basketball game. Combinations of deceptive and nondeceptive movements were used to see if novice and/or experienced listeners could perceive the attacker’s intentions through sound alone. We showed that basketball players were able to more accurately predict final running direction compared to nonplayers, particularly in the second experiment when the interceptive action was more basketball specific. We suggest that athletes present better action anticipation by being able to pick up and use the relevant kinematic features of deceptive movement from event-related sounds alone. This result suggests that action intention can be perceived through the sound a movement makes and that the ability to determine another person’s action intention from the information conveyed through sound is honed through practice.

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Section: Kinematic Data Analysismentioning

confidence: 99%

Expert players accurately detect an opponent’s movement intentions through sound alone.

Camponogara¹,

Rodger²,

Craig³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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show abstract

“…Motion capture systems are routinely used to capture full body human movement, and postprocessing techniques are widely available to extract meaningful results from the action performed. The complexity of implementing the newly defined event detection algorithms during post-processing of stair descent or stair ascent movement trials is no more onerous than what would be undertaken during routine gait analyses [6][7][8][9]. In addition, the newly defined methods of event detection can be easily applied retrospectively; which facilitates the calculation of stance and swing phase durations from existing datasets where noninstrumented staircases were used.…”

Section: Discussionmentioning

confidence: 99%

“…For stair descent, these were the Foot Acceleration Algorithm (FAA, [6]) and the Foot Velocity Algorithm (FVA, [7]), both of which are used to determine FO in overground gait. FAA determines FO from local maxima in trailing-limb toe horizontal acceleration, whilst FVA determines FO from local maxima in trailing-limb foot centre vertical velocity.…”

Section: The Algorithmsmentioning

confidence: 99%

“…Algorithms based on segmental kinematics (marker based) have previously been used to define initialcontact and toe-off during overground gait. These include using local maximas in the horizontal acceleration of the toe marker [6], in the vertical velocity of the foot [7], or in the foot to sacrum change in displacement (the 'coordinate-algorithm') [8]. The recent use of trail-limb local maxima hip extension to detect lead-limb initial-contact during overground gait [9] highlights that kinematic events further up the kinetic chain can be used as accurate alternatives to using foot kinematics to define the start or end of stance.…”

Section: Introductionmentioning

confidence: 99%

“…The recent use of trail-limb local maxima hip extension to detect lead-limb initial-contact during overground gait [9] highlights that kinematic events further up the kinetic chain can be used as accurate alternatives to using foot kinematics to define the start or end of stance. Although kinematic algorithms for overground gait exist [6][7][8][9], they are unlikely to be as accurate for event detection when applied to stair ambulation because of the differences in segmental kinematics between each mode of gait. There are no formal reports regarding the use of kinematicallyderived algorithms for the detection of TD and FO in stair ambulation, and given the sparse use and/or availability of force-instrumented staircases, such algorithms could have wide appeal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Stair-specific algorithms for identification of touch-down and foot-off when descending or ascending a non-instrumented staircase

et al. 2014

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Research HighlightsAlgorithms defining touch-down and foot-off (TD/FO) in stair ambulation are presented Performance of these algorithms was determined by comparison to force defined events TD/ FO were determined with acceptable precision in young and old adultsThe algorithms performed equally well at differing riser heights (85-255 mm)These algorithms can be used when force-instrumented staircases are unavailable AbstractThe present study introduces four event detection algorithms for defining touch-down and foot-off during stair descent and stair ascent using segmental kinematics. For stair descent, vertical velocity minima of the whole body centre-of-mass was used to define touch-down, and foot-off was defined as the instant of trail limb peak knee flexion. For stair ascent, vertical velocity local minima of the lead-limb toe was used to define touch-down, and footoff was defined as the local maxima in vertical displacement between the toe and pelvis. The performance of these algorithms was determined as the agreement in timings of kinematically derived events to those defined kinetically (ground reaction forces). Data were recorded while 17 young and 15 older adults completed stair descent and ascent trials over a four-step instrumented staircase. Trials were repeated for three stair riser height conditions (85 mm, 170 mm, and 255 mm). Kinematically derived touch-down and foot-off events showed good agreement (small 95% limits of agreement) with kinetically derived events for both young and older adults, across all riser heights, and for both ascent and descent. In addition, agreement metrics were better than those returned using existing kinematically derived event detection algorithms developed for overground gait. These results indicate that touch-down and foot-off during stair ascent and descent of non-instrumented staircases can be determined with acceptable precision using segmental kinematic data.

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Wearable Sensors for Motion Capture

Mishra¹,

Kiourti²

2021

Antenna and Sensor Technologies in Modern Medical Applications

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Automatic detection of gait events using kinematic data

Cited by 450 publications

References 15 publications

Expert players accurately detect an opponent’s movement intentions through sound alone.

Expert players accurately detect an opponent’s movement intentions through sound alone.

Stair-specific algorithms for identification of touch-down and foot-off when descending or ascending a non-instrumented staircase

Wearable Sensors for Motion Capture

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