scholarly journals Acoustic specification of upper limb movement in voicing: Exploratory data report and pre-registration

2019 ◽  
Author(s):  
Wim Pouw ◽  
Alexandra Paxton ◽  
Steven A. Harrison ◽  
James A. Dixon

Hand gestures communicate complex information to listeners through the visual information created by movement. In a recent study, however, we found that there are also direct biomechanical effects of high-impetus upper limb movement on voice acoustics. Here we explored whether listeners could detect information about movement in voice acoustics of another person. In this exploratory study, participants listened to a recording of a vocalizer who was simultaneously producing low- (wrist movement) or high- (arm movement) impetus movements at three different tempos. Listeners were asked to synchronize their own movement (wrist or arm movement) with that of the vocalizer. Listeners coupled with the frequency of the vocalizer arm (but not wrist) movements, and showed phase-coupling with vocalizer arm (but not wrist) movements. However, we found that this synchronization occurred regardless of whether the listener was moving their wrist or arm. This study shows that, in principle, there is acoustic specification of arm movements in voicing, but not wrist movements. These results, if replicated, provide novel insight into the possible interpersonal functions of gesture acoustics, which may lie in communicating bodily states. The second part of the paper is a pre-registration for the confirmatory study that will assess the research question in a larger sample with more diverse and naturalistic stimuli.

2020 ◽  
Vol 117 (21) ◽  
pp. 11364-11367 ◽  
Author(s):  
Wim Pouw ◽  
Alexandra Paxton ◽  
Steven J. Harrison ◽  
James A. Dixon

We show that the human voice has complex acoustic qualities that are directly coupled to peripheral musculoskeletal tensioning of the body, such as subtle wrist movements. In this study, human vocalizers produced a steady-state vocalization while rhythmically moving the wrist or the arm at different tempos. Although listeners could only hear and not see the vocalizer, they were able to completely synchronize their own rhythmic wrist or arm movement with the movement of the vocalizer which they perceived in the voice acoustics. This study corroborates recent evidence suggesting that the human voice is constrained by bodily tensioning affecting the respiratory–vocal system. The current results show that the human voice contains a bodily imprint that is directly informative for the interpersonal perception of another’s dynamic physical states.


2019 ◽  
Author(s):  
Wim Pouw ◽  
Alexandra Paxton ◽  
Steven A. Harrison ◽  
James A. Dixon

We show that upper limb movement affects voice acoustics in a way that allows listeners to synchronize to those movements. In this pre-registered motion-tracking study (within-subjects, N = 30), participants listened to vocalizers producing a steady-state phonation while moving the upper limb with a wrist versus an arm motion at different tempos. Listeners were asked to synchronize their own wrist or arm movement with what they perceived in the voice acoustics to be the movement of the vocalizer. Previous research has shown that higher physical impetus gestures can directly affect lower vocal tract activity, leading to peaks in the fundamental frequency (F0; perceived as pitch) and intensity. Here we show that listeners can attune to acoustic information so as to synchronize both in frequency and in phasing with the vocalizer, even with very subtle movements. We interpret the current results in support of a possible ecological psychological approach to speech prosody.


Author(s):  
Kai Chen ◽  
Richard A. Foulds

The dependence of muscle force on muscle length gives rise to a “spring–like” behavior which has been shown to play an important role during human movement. Neville Hogan (Hogan, 1985) proposed a mathematical model in terms of impedance control of arm movement. Discussing this work, Dr. Hogan admits that it can not effectively model all aspects of the performance of the system. He said “Controlling the complete dynamic behavior of the limb may be beyond the capacity of the central nervous system. If the disturbance is sufficiently abrupt, then, because of the inevitable transmission delays, continuous intervention based on neural feedback information will not be a feasible method of modulating these quantities.”. However, the model proposed in this study, accomplished most the work which Hogan believed was not feasible. In order to validate the result of proposed model, this study perform sensitivity analysis between the results produced by the dynamics system and the results measured, the comparison showed the difference between these two results were less than 10%, which strongly support the idea that proposed dynamic model can accurately reflect dynamics system in the upper limb movement control.


PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11301
Author(s):  
Kristof Vandael ◽  
Tasha R. Stanton ◽  
Ann Meulders

Background Proprioception refers to the perception of motion and position of the body or body segments in space. A wide range of proprioceptive tests exists, although tests dynamically evaluating sensorimotor integration during upper limb movement are scarce. We introduce a novel task to evaluate kinesthetic proprioceptive function during complex upper limb movements using a robotic device. We aimed to evaluate the test–retest reliability of this newly developed Dynamic Movement Reproduction (DMR) task. Furthermore, we assessed reliability of the commonly used Joint Reposition (JR) task of the elbow, evaluated the association between both tasks, and explored the influence of visual information (viewing arm movement or not) on performance during both tasks. Methods During the DMR task, participants actively reproduced movement patterns while holding a handle attached to the robotic arm, with the device encoding actual position throughout movement. In the JR task, participants actively reproduced forearm positions; with the final arm position evaluated using an angle measurement tool. The difference between target movement pattern/position and reproduced movement pattern/position served as measures of accuracy. In study 1 (N = 23), pain-free participants performed both tasks at two test sessions, 24-h apart, both with and without visual information available (i.e., vision occluded using a blindfold). In study 2 (N = 64), an independent sample of pain-free participants performed the same tasks in a single session to replicate findings regarding the association between both tasks and the influence of visual information. Results The DMR task accuracy showed good-to-excellent test–retest reliability, while JR task reliability was poor: measurements did not remain sufficiently stable over testing days. The DMR and JR tasks were only weakly associated. Adding visual information (i.e., watching arm movement) had different performance effects on the tasks: it increased JR accuracy but decreased DMR accuracy, though only when the DMR task started with visual information available (i.e., an order effect). Discussion The DMR task’s highly standardized protocol (i.e., largely automated), precise measurement and involvement of the entire upper limb kinetic chain (i.e., shoulder, elbow and wrist joints) make it a promising tool. Moreover, the poor association between the JR and DMR tasks indicates that they likely capture unique aspects of proprioceptive function. While the former mainly captures position sense, the latter appears to capture sensorimotor integration processes underlying kinesthesia, largely independent of position sense. Finally, our results show that the integration of visual and proprioceptive information is not straightforward: additional visual information of arm movement does not necessarily make active movement reproduction more accurate, on the contrary, when movement is complex, vision appears to make it worse.


SLEEP ◽  
2021 ◽  
Author(s):  
Dorothee Fischer ◽  
Elizabeth B Klerman ◽  
Andrew J K Phillips

Abstract Study Objectives Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual’s average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics. Methods Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: ‘scrambling’ the order of days; daily vs. weekly variation; naps; awakenings; ‘all-nighters’; and length of study. Results SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences. Conclusions Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.


2021 ◽  
Vol 12 ◽  
pp. 204062232110012
Author(s):  
Rocío Palomo-Carrión ◽  
Elisabeth Bravo-Esteban ◽  
Sara Ando-La Fuente ◽  
Purificación López-Muñoz ◽  
Inés Martínez-Galán ◽  
...  

Background: The capacity of children with hemiplegia to be engaged in anticipatory action planning is affected. There is no balance among spatial, proprioceptive and visual information, thus altering the affected upper limb visuomotor coordination. The objective of the present study was to assess the improvement in visuomotor coordination after the application of a unimanual intensive therapy program, with the use of unaffected hand containment compared with not using unaffected hand containment. Methods: A simple blind randomized clinical trial was realized. A total of 16 subjects with congenital infantile hemiplegia participated in the study with an age mean of 5.54 years old (SD:1.55). Two intensive protocols for 5 weeks of modified constraint-induced movement therapy (mCIMT) or unimanual therapy without containment (UTWC) were executed 5 days per week (2 h/day). Affected upper limb visuomotor coordination (reaction time, task total time, active range, dynamic grasp) was measured before–after intensive therapy using a specific circuit with different slopes (10°/15°). Results: Statistically significant inter-group differences were found after the intervention, with clinically relevant results for the mCIMT group not seen in UTWC, in the following variables: reaction time 10°slope ( p = 0.003, d = 2.44), reaction time 15°slope ( p = 0.002, d = 2.15) as well as for the task total time 10°slope ( p = 0.002, d = 2.25), active reach 10°slope ( p = 0.002, d = 2.7), active reach 15°slope ( p = 0.003, d = 2.29) and dynamic grasp 10°/15°slopes ( p = <0.001, d = 2.69). There were not statistically significant inter-group differences in the total task time with 15°slope ( p = 0.074, d = 1.27). Conclusions: The use of unaffected hand containment in mCIMT would allow improvements in the affected upper limb’s visuomotor coordination. Thus, it would favor clinical practice to make decisions on therapeutic approaches to increase the affected upper limb functionality and action planning in children diagnosed with infantile hemiplegia (4–8 years old).


Author(s):  
Zhi-Qiang Zhang ◽  
Lian-Ying Ji ◽  
Zhi-Pei Huang ◽  
Jian-Kang Wu

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