Movement consistency during repetitive tool use action

The consistency and repeatability of movement patterns has been of long-standing interest in locomotor biomechanics, but less well explored in other domains. Tool use is one of such a domain; while the complex dynamics of the human-tool-environment system have been approached from various angles, to date it remains unknown how the rhythmicity of repetitive tool-using action emerges. To examine whether the spontaneously adopted movement frequency is a variable susceptible to individual execution approaches or emerges as constant behaviour, we recorded sawing motion across a range of 14 experimental conditions using various manipulations. This was compared to free and pantomimed arm movements. We found that a mean (SD) sawing frequency of 2.0 (0.4) Hz was employed across experimental conditions. Most experimental conditions did not significantly affect the sawing frequency, signifying the robustness of this spontaneously emerging movement. Free horizontal arm translation and miming of sawing was performed at half the movement frequency with more than double the excursion distance, showing that not all arm movements spontaneously emerge at the observed sawing parameters. Observed movement frequencies across all conditions could be closely predicted from movement time reference data for generic arm movements found in the Methods Time Measurement literature, highlighting a generic biomechanical relationship between the time taken for a given distance travelled underlying the observed behaviour. We conclude that our findings lend support to the hypothesis that repetitive movements during tool use are executed according to generic and predictable musculoskeletal mechanics and constraints, albeit in the context of the general task (sawing) and environmental constraints such as friction, rather than being subject to task-specific control or individual cognitive schemata

Reliability of equine visual lameness classification as a function of expertise, lameness severity and rater confidence

Visual equine lameness assessment is often unreliable, yet the full understanding of this issue is missing. Here, we investigate visual lameness assessment using near-realistic, three-dimensional horse animations presenting with 0–60 per cent movement asymmetry. Animations were scored at an equine veterinary seminar by attendees with various expertise levels. Results showed that years of experience and exposure to a low, medium or high case load had no significant effect on correct assessment of lame (P>0.149) or sound horses (P≥0.412), with the exception of a significant effect of case load exposure on forelimb lameness assessment at 60 per cent asymmetry (P=0.014). The correct classification of sound horses as sound was significantly (P<0.001) higher for forelimb (average 72 per cent correct) than for hindlimb lameness assessment (average 28 per cent correct): participants often saw hindlimb lameness where there was none. For subtle lameness, errors often resulted from not noticing forelimb lameness and from classifying the incorrect limb as lame for hindlimb lameness. Diagnostic accuracy was at or below chance level for some metrics. Rater confidence was not associated with performance. Visual gait assessment may overall be unlikely to reliably differentiate between sound and mildly lame horses irrespective of an assessor’s background

Spontaneous bimanual independence during parallel tapping and sawing - Fig 5

<p>Top: relationship of the median absolute difference between observed and expected cycle duration for the hand engaging in sawing compared to the hand engaging in tapping. Most of the deviation from the expected ratio is due to the difference between expected and observed sawing cycle duration. Bottom: median absolute difference between observed and expected cycle duration (left) and within-participant variation thereof (right) for sawing and tapping. Complementary to the data presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178188#pone.0178188.g003" target="_blank">Fig 3</a>, the deviation from the expected cycle duration is larger for sawing, however the within-participant variation in the deviation is similar for sawing and tapping.</p

Signal processing.

<p>Maxima detection in the signals derived from optical motion capture (top) and an accelerometer (bottom). Each maximum (red circle) marks the start of a new sawing cycle, the time elapsed between maxima defines the sawing cycle duration.</p

Boxplots showing within-participant variation as an index of stability.

<p>Top: within-participant IQR for the hand performing tapping (left) and the hand performing sawing (right), for both the single handed task (‘single’) and the dual movement tasks (remainder). Bottom, left: participant-specific difference between within-participant variation for the hand performing tapping and the hand performing sawing. Dashed line: no difference in variation. Bottom, right: participant-specific ratio of the variation observed during the single hand task compared to the equivalent action in the four dual movement tasks. Dashed line: no difference in variation. A value of 0.5 corresponds to half the amount of variation in the single hand task, a value of 2 corresponds to twice the variation. For further details on experimental conditions and legend, please refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178188#pone.0178188.g002" target="_blank">Fig 2</a>.</p

Schematic illustration of the four saws, drawn to scale.

<p>A–wood saw (experiment #1); B–piercing saw (experiment #2 and #3); C–hardpoint hand saw (experiment #3); D–hacksaw (experiment #4). The white rectangles depict a schematic illustration of the attached accelerometer unit for experiment #2 to #4.</p

Cycle durations across all experimental conditions.

<p>This table presents the median [IQR] for the single and dual movement tasks for both, tapping and sawing, as well as the metronome beat for the three conditions in which tapping was performed to match the set beat of a metronome.</p

Data collection for experiment #4.

<p>Left: participant sawing into a plastic pipe, right: saw instrumented with accelerometer. The individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details.</p

Results for experiment #5.

<p>Boxplots of sawing frequency (top) and excursion amplitude (bottom), showing participant-specific median values (left) and within-participant variation (right) in experiment #5. Black/grey dots: individual data points. 05F –free arm movements, 05M –mimed sawing, 05R –real sawing.</p