Chemotherapy induced peripheral neuropathy (CIPN) is a frequent side effect of a number of chemotherapeutic agents which are widely used in the treatment of common cancers. Sensory symptoms primarily affect the fingers and toes and include numbness, tingling and pain. However, there is limited understanding of how these touch impairments may impact manipulation skills. Questionnaire and focus group methods were used to describe the experience of CIPN side effects impacting manual activities in 25 self-selected participants recruited from a cancer charity web site advertisement. Participants’ responses demonstrated varying degrees of impact of touch impairment associated with CIPN on the performance of manual activities involving bimanual, unimanual and directed touch. Examination of the components of the affected activities, together with participants’ reports of alterations in their experience of touch, were used to generate theory-informed hypotheses about why some manual tasks are more affected than others. In future research it is proposed that quantitative objective measures should be used to complement patient reported outcome measures in evaluating the mechanisms underlying issues in sensory motor control of manual activities caused by the effects of CIPN.

The ability to perceive others’ emotions and one’s own interoceptive states has been the subject of extensive research. Very little work, however, has investigated the ability to recognise others’ interoceptive states, such as whether an individual is feeling breathless, nauseated, or fatigued. This is likely owing to the dearth of stimuli available for use in research studies, despite the clear relevance of this ability to social interaction and effective caregiving. This paper describes the development and validation of two stimulus sets for use in research into the perception of others’ interoceptive states. The Interoceptive States Vocalisations (ISV) database and the Interoceptive States Point Light Displays (ISPLD) database include 191 vocalisation and 159 point light display stimuli. Both stimulus sets underwent two phases of validation, and all stimuli were scored in terms of their quality and recognisability, using five different measures. The ISV also includes control stimuli featuring non-interoceptive vocalisations. Some interoceptive states were consistently recognised better than others, but variability was observed within, as well as between, stimulus categories. Stimuli are freely available for use in research, and are presented alongside all stimulus quality scores, in order for researchers to select the most appropriate stimuli based on individual research questions.

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations, which require precise control over timing and synchronization, particularly in ensemble performance. This is crucial because both deliberate expressive nuances and accidental timing deviations can affect the overall timing of a performance. This discussion prompts the question of how musicians adjust their temporal dynamics to achieve synchronization within an ensemble. This paper introduces a novel feedback correction model based on the Kalman Filter, aimed at improving the understanding of interpersonal timing in ensemble music performances. The proposed model performs similarly to other linear correction models in the literature, with the advantage of low computational cost and good performance even in scenarios where the underlying tempo varies.

The current study aimed at identifying neural mechanisms involved in the monitoring of familiar ADL sequence and in the detection of different types of sequence error (i.e. omission, repetition and order violation). In addition, we explored the effect of steps predictability in sequence monitoring.

IntroductionPsychophysical studies suggest texture perception is mediated by spatial and vibration codes (duplex theory). Vibration coding, driven by relative motion between digit and stimulus, is involved in the perception of very fine gratings whereas coarse texture perception depends more on spatial coding, which does not require relative motion.MethodsWe examined cortical activation, using functional Magnetic Resonance Imaging associated with fine and coarse tactile spatial gratings applied by sliding or touching (sliding vs. static contact) on the index finger pad.ResultsWe found regions, contralateral to the stimulated digit, in BA1 in S1, OP1, OP3, and OP4 in S2, and in auditory cortex, which were significantly more activated by sliding gratings but did not find this pattern in visual cortex. Regions in brain areas activated by vibrotactile stimuli (including auditory cortex) were also modulated by whether or not the gratings moved. In a control study we showed that this contrast persisted when the salience of the static condition was increased by using a double touch.DiscussionThese findings suggest that vibration from sliding touch invokes multisensory cortical mechanisms in tactile processing of roughness. However, we did not find evidence of a separate visual region activated by static touch nor was there a dissociation between cortical response to fine vs. coarse gratings as might have been expected from duplex theory.

Tactile sensitivity is affected by age, as shown by the deterioration of spatial acuity assessed with the two-point discrimination task. This is assumed to be partly a result of age-related changes of the peripheral somatosensory system. In particular, in the elderly, the density of mechanoreceptive afferents decreases with age and the skin tends to become drier, less elastic and less stiff. To assess to what degree mechanoreceptor density, skin hydration, elasticity and stiffness can account for the deterioration of tactile spatial sensitivity observed in the elderly, several approaches were combined, including psychophysics, measurements of finger properties, modelling and simulation of the response of first-order tactile neurons. Psychophysics confirmed that the Elderly group has lower tactile acuity than the Young group. Correlation and commonality analysis showed that age was the most important factor in explaining decreases in behavioural performance. Biological elasticity, hydration and finger pad area were also involved. These results were consistent with the outcome of simulations showing that lower afferent density and lower Young's modulus (i.e. lower stiffness) negatively affected the tactile encoding of stimulus information. Simulations revealed that these changes resulted in a lower build-up of task-relevant stimulus information. Importantly, the reduction in discrimination performance with age in the simulation was less than that observed in the psychophysical testing, indicating that there are additional peripheral as well as central factors responsible for age-related changes in tactile discrimination. KEY POINTS: Ageing effects on tactile perception involve the deterioration of spatial sensitivity, although the contribution of central and peripheral factors is not clear. We combined psychophysics, measurements of finger properties, modelling and simulation of the response of first-order tactile neurons to investigate to what extent skin elasticity, stiffness, hydration, finger pad area and afferent density can account for the lower spatial sensitivity observed in the elderly. Correlation and commonality analysis revealed that age was the most important factor to predict behavioural performance. Skin biological elasticity, hydration and finger pad area contributed to a lesser extent. The simulation of first-order tactile neuron responses indicated that reduction in afferent density plays a major role in the deterioration of tactile spatial acuity. Simulations also showed that lower skin stiffness and lower afferent density affect the build-up of stimulus information and the response of SA1 (i.e. type 1 slowly adapting fibres) and RA1 (i.e. type 1 rapidly adapting fibres) afferent fibres.

A human handing over an object modulates their grasp and movements to accommodate their partner's capa-bilities, which greatly increases the likelihood of a successful transfer. State-of-the-art robot behavior lacks this level of user understanding, resulting in interactions that force the human partner to shoulder the burden of adaptation. This paper investigates how visual occlusion of the object being passed affects the subjective perception and quantitative performance of the human receiver. We performed an experiment in virtual reality where seventeen participants were tasked with repeatedly reaching to take a tool from the hand of a robot; each of the three tested objects (hammer, screwdriver, scissors) was presented in a wide variety of poses. We carefully analysed the user's hand and head motions, the time to grasp the object, and the chosen grasp location, as well as participants' ratings of the grasp they just performed. Results show that initial visibility of the handle significantly increases the reported holdability and immediate usability of a tool. Furthermore, a robot that offers objects so that their handles are more occluded forces the receiver to spend more time in planning and executing the grasp and also lowers the probability that the tool will be grasped by the handle. Together these findings indicate that robots can more effectively support their human work partners by increasing the visibility of the intended grasp location of objects being passed.

BACKGROUND:Apraxia and action disorganization syndrome (AADS) after stroke can disrupt activities of daily living (ADL). Occupational therapy has been effective in improving ADL performance, however, inclusion of multiple tasks means it is unclear which therapy elements contribute to improvement. We evaluated the efficacy of a task model approach to ADL rehabilitation, comparing training in making a cup of tea with a stepping training control condition. METHODS:Of the 29 stroke survivors with AADS who participated in this cross-over randomized controlled feasibility trial, 25 were included in analysis [44% females; mean(SD) age = 71.1(7.8) years; years post-stroke = 4.6(3.3)]. Participants attended five 1-hour weekly tea making training sessions in which progress was monitored and feedback given using a computer-based system which implemented a Markov Decision Process (MDP) task model. In a control condition, participants received five 1-hour weekly stepping sessions. RESULTS:Compared to stepping training, tea making training reduced errors across 4 different tea types. The time taken to make a cup of tea was reduced so the improvement in accuracy was not due to a speed-accuracy trade-off. No improvement linked to tea making training was evident in a complex tea preparation task (making two different cups of tea simultaneously), indicating a lack of generalisation in the training. CONCLUSIONS:The clearly specified but flexible training protocol, together with information on the distribution of errors, provide pointers for further refinement of task model approaches to ADL rehabilitation. It is recommended that the approach be tested under errorless learning conditions with more impaired patients in future research. TRIAL REGISTRATION:Retrospectively registered at ClinicalTrials.gov on 5th August 2019 [NCT04044911] https://clinicaltrials.gov/ct2/show/NCT04044911?term=Cogwatch&rank=1.

Abstract With sliding contact humans are able to perceive tactile features at the micron scale, such as a single dot raised only few microns when placed on a smooth surface. Frictional effects are important in determining the tactile cues available in sliding and depend on a variety of factors. In this study, we investigated how detection sensitivity to a single micro dot is affected by surface roughness and moistening of the index finger. These manipulations were chosen to alter the skin-surface interaction and the resulting forces acting on the skin. We found that detection threshold was 6-fold higher for the rough surfaces when compared to smooth surfaces. Moistening the finger with water or water and soap reduced the friction as well as the magnitude of tangential force variations when compared to the dry finger, regardless of the surface geometry. However, detection sensitivity improved for the ‘smooth’ surfaces but worsened for the ‘rough’ ones with moistening. We suggest that this is due to the different nature of neural noise generated when making contact with smooth or rough background surfaces, and the extent to which different fluid environments modulate friction and the forces acting on the skin with consequences for the neural response.

We review four current computational models that simulate the response of mechanoreceptors in the glabrous skin to tactile stimulation. The aim is to inform researchers in psychology, sensorimotor science and robotics who may want to implement this type of quantitative model in their research. This approach proves relevant to understanding of the interaction between skin response and neural activity as it avoids some of the limitations of traditional measurement methods of tribology, for the skin, and neurophysiology, for tactile neurons. The main advantage is to afford new ways of looking at the combined effects of skin properties on the activity of a population of tactile neurons, and to examine different forms of coding by tactile neurons. Here, we provide an overview of selected models from stimulus application to neuronal spiking response, including their evaluation in terms of existing data, and their applicability in relation to human tactile perception.

The synthesis of realistic robot grasps in a simulated environment is pivotal in generating datasets that support sim-to-real transfer learning. In a step toward achieving this goal, we propose PrendoSim, an open-source grasp generator based on a proxy-hand simulation that employs NVIDIA’s physics engine (PhysX) and the recently released articulated-body objects developed by Unity (https://prendosim.github.io). We present the implementation details, the method used to generate grasps, the approach to operationally evaluate stability of the generated grasps, and examples of grasps obtained with two different grippers (a parallel jaw gripper and a three-finger hand) grasping three objects selected from the YCB dataset (a pair of scissors, a hammer, and a screwdriver). Compared to simulators proposed in the literature, PrendoSim balances grasp realism and ease of use, displaying an intuitive interface and enabling the user to produce a large and varied dataset of stable

Roughness perception through fingertip contact with a textured surface can involve spatial and temporal cues from skin indentation and vibration respectively. Both types of cue may be affected by contact forces when feeling a surface and we ask whether, on a given trial, discrimination performance relates to contact forces. We examine roughness discrimination performance in a standard psychophysical method (2-interval forced choice, in which the participant identifies which of two spatial textures formed by parallel grooves feels rougher) while continuously measuring the normal and tangential forces applied by the index finger. Fourteen participants discriminated spatial gratings in fine (spatial period of 320–580 micron) and coarse (1520–1920 micron) ranges using static pressing or sliding contact of the index finger. Normal contact force (mean and variability) during pressing or sliding had relatively little impact on accuracy of roughness judgments except when pressing on surfaces in the coarse range. Discrimination was better for sliding than pressing in the fine but not the coarse range. In contrast, tangential force fluctuations during sliding were strongly related to roughness judgment accuracy.

Event Abstract Back to Event Acting is not the same as feeling: Emotion expression in gait is different for posed and induced emotions Bianca A. Schuster1, Sophie L. Sowden1, Diar Abdlkarim1, Alan M. Wing1 and Jennifer L. Cook1* 1 University of Birmingham, School of Psychology, United Kingdom INTRODUCTION The past decade has witnessed an unprecedented growth in human–computer interaction. With this progress comes growing demand for computers to sense and recognize users’ affective states (Cowie et al., 2001; Pantic & Rothkrantz, 2003; Hudlicka, 2003). Indeed, the development of emotion-sensitive computer systems may have important implications for variety of areas from automatic customer services (Fragopanagos & Taylor, 2005) to early recognition and diagnosis of clinical conditions (Michalak, et al., 2009). Automated emotion detection has largely focused on facial expressions (e.g., Kenji, 1991), however, whole-body movement carries numerous emotion-related cues, which humans can rapidly detect (e.g., Clarke et al., 2005; De Meijer, 1989; Dittrich et al., 1996; Montepare et al., 1987, 1999; Walk and Homan, 1984; Wallbott and Scherer, 1986). Velocity, for instance, comprises an important cue as to a person’s underlying emotional state: faster (high velocity) body movements tend to indicate anger and happiness, whilst low velocity movements are indicative of sadness (Chouchourelou et al., 2006; Edey et al., 2017; Gross et al., 2012; Halovic & Kroos, 2018; Michalak et al., 2009; Roether et al., 2009). Consequently, whole-body cues are increasingly being incorporated into computerized emotion recognition technologies (Janssen et al., 2008; Pantic & Rothkrantz, 2003). However, at present this remains a field which lags behind the advances made in the detection of emotion from face cues. One issue which has received interest in the context of emotion recognition from faces, but which has been overlooked with respect to whole-body emotion recognition, is the question of differences between posed and induced/spontaneous expressions. Although emotion tracking software typically aims to detect naturally occurring, spontaneous expressions, much of our knowledge of movement kinematics comes from the posed expressions of professional actors (e.g., Jannsen et al., 2008; Roether et al., 2009, Venture et al., 2014). Even when posing is aided by induction methods such as autobiographical recall, the actor remains aware of the effects they are expected to produce, and likely exaggerates particular movement patterns. Consequently, kinematic measures derived from studies using posed expressions alone may not correspond to naturally occurring emotional expressions. Indeed, with respect to facial expressions, preliminary evidence suggests that induced and posed expressions differ with regards to timing and amplitude (Schmidt et al., 2006; Valstar et al., 2006). The current study recorded happy, angry and sad walks, as executed by student volunteers. We compared the velocity of these walks when the emotion was ‘posed’ and when the emotion was naturally ‘induced’ by watching emotional film clips. METHODS Kinematic data was obtained from 31 healthy participants (24 females) with self-reported unimpaired motor function. All participants gave informed consent to participate and received course credit or a monetary incentive as reimbursement. The study was approved by the University of Birmingham Ethics Committee. Walking data was recorded using the Zeno™ Walkway (ProtoKinetics LLC, Havertown, USA) gait mat. All participants first carried out a ‘baseline’ walk for a duration of 120 seconds. Following this, participants watched 3 film clips (average length: 2.5 minutes) which had been selected for their propensity to induce happy, angry and sad emotional states, as assessed in a pilot study. Film-clip order was pseudo-randomized between participants. Between films participants viewed a 1-minute-long filler clip, to reset their mood to neutral. Immediately after each clip, participants walked continuously across the gait mat, stepping off the end to turn around each time. Walks were recorded for 30 seconds, resulting in 7 passes, across the full length of the gait mat, on average. Subsequently, participants rated their current mood (positive – negative), arousal (calm – excited), intensity for the target emotion and 4 other basic emotions (anger, happiness, sadness, disgust, surprise) and the extent to which they felt emotionally neutral on a 10-point scale. After watching all the film clips, participants executed posed walks, simulating happy, angry and sad emotional states according to the instruction … “Imagine you were angry (happy/sad). Walk across the mat how you think you would walk if you were angry (happy/sad)”. PKMAS software (ProtoKinetics LLC, Havertown, USA) was employed to process each walk and calculate the average velocity (distance travelled/ambulation time, centimeters/second (cm/s)) across the walk period (120 seconds for baseline walks, 30 seconds for all other walks). RESULTS Emotion induction was successful: emotion rating discreteness (target emotion rating minus average rating of all non-target emotions) scores for each video were significantly greater than zero (ps < .001). A repeated-measures ANOVA with within-subjects factors of condition (posed, induced), and emotion (happy, angry, sad) revealed a significant main effect for emotion (Figure 1; F(2,60) = 60.09, p < .001, partial eta squared = .67). There was no main effect for condition (F(1,30) = .041, p = .841, partial eta squared= .00). Collapsing across posed and induced revealed that angry and happy walks were the fastest, and sad walks were the slowest (angry: mean [M] = 118.90, standard error of the mean [SEM] = 3.29; happy: M = 114.96, SEM = 2.34; sad: M = 101.34, SEM = 2.95). Bonferroni-corrected post-hoc t-tests reveal that while there was no difference in velocity for happy and angry walks (t(30) = 2.49, p = .019) , sad and angry (t(30) = 9.56, p < .001) and happy and sad (t(30) = 8.46, p < .001) were significantly different. However, the ANOVA also revealed a significant condition x emotion interaction (F(2,60) = 38.16, p < .001, partial eta squared= .56). Separate ANOVAs for each condition revealed that, whereas velocity differed as a function of emotion for posed walks (F(2,60) = 57.39 p < .001, partial eta squared = .66), this was not the case for the induced condition (F(2,60) = 2.34, p =.105, partial eta squared = .07). Post-hoc tests further showed that, for the posed condition alone, there was no difference in velocity for happy and angry walks (t(30) = 1.98, p = .058). However, velocities for posed sad walks were significantly lower than those for posed angry walks (sad: M = 92.35, SEM = 3.82; angry: M = 124.50, SEM = 4.33; t(30) = 9.77, p < .001) and posed happy walks (happy: M = 117.85 , SEM = 2.40; t(30) = 9.04 p < .001). The equivalent tests, for the induced condition, showed no difference in velocity for sad and angry walks (sad: M = 110.32, SEM = 2.60; angry: M = 113.31, SEM = 2.74 t(30) = 2.40, p = .024), sad and happy (happy: M = 112.10, SEM = 2.63; t(30) = 1.10, p = .281) or happy and angry walks (t(30) = .96, p = .343). DISCUSSION The current study investigated whether the velocity of happy, angry and sad walks differed for walks that comprised posed simulations of emotion, compared to those that followed emotion induction and thus comprised natural expressions of emotion. Velocity differed as a function of emotion for posed simulations: in line with previous literature we observed faster velocities for angry walks and slower velocities for sad walks. Velocities for posed happiness were also, as expected, faster than sad but slower than angry, albeit the difference between happy and angry was not statistically significant. This pattern of data was not observed for walks that followed emotion induction. Although our emotion induction methods were successful, as evidenced by higher post-film-clip intensity ratings for the target emotion compared to non-target emotions (i.e. if a participant watched a happy video they gave high ratings on the happy scale and low ratings for sad, angry, disgusted and surprised) we saw no velocity differences between happy, angry and sad walks for the induced condition. These data highlight important differences between posed and naturally-occurring whole-body expressions of emotion, demonstrating in particular that, for induced emotions, gait velocity should not be relied upon to discriminate emotional state. Further exploration is required to identify the gait characteristics (e.g. cadence, step width, force, stride length) that are the best predictors of emotional state for induced, naturally-occurring, emotions. With respect to the further development of computerized emotion recognition methods, our data clearly show that algorithms aiming to detect spontaneous/naturally-occurring emotions should not rely on posed expression datasets for training purposes. Figure 1 Acknowledgements The work in this paper was funded by a European Research Council Starting Grant held by J.C.. We thank Connor Keating, Georgina Toms and Laura Guile for help with data collection. References Chouchourelou, A., Matsuka, T., Harber, K., and Shiffrar, M. (2006). The visual analysis of emotional actions. Soc. Neurosci. 1, 63–74. https://doi.org/10.1080/17470910600630599 Clarke, T. J., Bradshaw, M. F., Field, D. T., Hampson, S. E., & Rose, D. (2005). The Perception of Emotion from Body Movement in Point-Light Displays of Interpersonal Dialogue. Perception, 34(10), 1171–1180. https://doi.org/10.1068/p5203 Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18(1), 32–80. de Meijer, M. (1989). The contribution of general features of body movement to the attribution of emotions. Journal of Nonverbal Behavior, 13(4), 247–268. https://doi.org/10.1007/BF00990296 Dittrich, W. H., Troscianko, T., Lea, S. E. G., & Morgan, D. (1996). Perception of Emotion from Dynamic Point-Light Displays Represented in Dance. Perception, 25(6), 727–738. https://doi.org/10.1068/p250727 Edey, R., Yon, D., Cook, J., Dumontheil, I., & Press, C. (2017). Our own action kinematics predict the perceived affective states of others. Journal of Experimental Psychology: Human Perception and Performance, 43(7), 1263–1268. https://doi.org/10.1037/xhp0000423 Fragopanagos, N., Taylor, JG. (2005). Emotion recognition in human–computer interaction. Neural Networks, 18(4), 389–405. doi: 10.1016/j.neunet.2005.03.006. Gross, M. M., Crane, E. A., & Fredrickson, B. L. (2012). Effort-Shape and kinematic assessment of bodily expression of emotion during gait. Human Movement Science, 31(1), 202–221. https://doi.org/10.1016/J.HUMOV.2011.05.001 Halovic, S., & Kroos, C. (2018). Not all is noticed: Kinematic cues of emotion-specific gait. Human Movement Science, 57, 478–488. https://doi.org/10.1016/J.HUMOV.2017.11.008 Hudlicka, E. (2003). To feel or not to feel: the role of affect in human–computer interaction. International Journal of Human–Computer Studies. 59(1), 1–32. doi: 10.1016/S1071-5819(03)00047-8. Janssen, D., Schöllhorn, W. I., Lubienetzki, J., Fölling, K., Kokenge, H., & Davids, K. (2008). Recognition of Emotions in Gait Patterns by Means of Artificial Neural Nets. Journal of Nonverbal Behavior, 32(2), 79–92. https://doi.org/10.1007/s10919-007-0045-3 Kenji, M. (1991). Recognition of facial expression from optical flow. IEICE Transactions on Information and Systems. 74(10), 3474–3483. Michalak, J., Troje, N. F., Fischer, J., Vollmar, P., Heidenreich, T., & Schulte, D. (2009). Embodiment of Sadness and Depression—Gait Patterns Associated With Dysphoric Mood. Psychosomatic Medicine, 71(5), 580-587. Montepare, J. M., Goldstein, S. B., & Clausen, A. (1987). The identification of emotions from gait information. Journal of Nonverbal Behavior, 11(1), 33–42. https://doi.org/10.1007/BF00999605 Montepare, J., Koff, E., Zaitchik, D., & Albert, M. (1999). The use of body movements and gestures as cues to emotions in younger and older adults. Journal of Nonverbal Behavior, 23(2), 133–152. https://doi.org/10.1023/A:1021435526134 Pantic M, Rothkrantz, LJ. (2003). Toward an affect-sensitive multimodal human–computer interaction. Proceedings of the IEEE, 91(9), 1370–1390. doi: 10.1109/JPROC.2003.817122. Schmidt, K. L., Ambadar, Z., Cohn, J. F., & Reed, L. I. (2006). Movement Differences between Deliberate and Spontaneous Facial Expressions: Zygomaticus Major Action in Smiling. Journal of Nonverbal Behavior, 30(1), 37–52. https://doi.org/10.1007/s10919-005-0003-x Roether, C. L., Omlor, L., Christensen, A., & Giese, M. A. (2009). Critical features for the perception of emotion from gait. Journal of Vision, 9(6), 15. https://doi.org/10.1167/9.6.15 Walk, R. D., & Homan, C. P. (1984). Emotion and dance in dynamic light displays. Bulletin of the Psychonomic Society, 22(5), 437–440. https://doi.org/10.3758/BF03333870 Wallbott, H. G., & Scherer, K. R. (1986). How universal and specific is emotional experience? Evidence from 27 countries on five continents. Information (International Social Science Council), 25(4), 763–795. https://doi.org/10.1177/053901886025004001 Valstar, M., & Pantic, M. (2006). Fully Automatic Facial Action Unit Detection and Temporal Analysis. In 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06) (p. 149). https://doi.org/10.1109/CVPRW.2006.85 Venture, G., Kadone, H., Zhang, T., Grèzes, J., Berthoz, A., & Hicheur, H. (2014). Recognizing Emotions Conveyed by Human Gait. International Journal of Social Robotics, 6(4), 621–632. https://doi.org/10.1007/s12369-014-0243-1 Keywords: emotion, Biological motion, kinematics, velocity, Gait, happy, angry, SAD Conference: 4th International Conference on Educational Neuroscience, Abu Dhabi, United Arab Emirates, 10 Mar - 11 Mar, 2019. Presentation Type: Oral Presentation (invited speakers only) Topic: Educational Neuroscience Citation: Schuster BA, Sowden SL, Abdlkarim D, Wing AM and Cook JL (2019). Acting is not the same as feeling: Emotion expression in gait is different for posed and induced emotions. Conference Abstract: 4th International Conference on Educational Neuroscience. doi: 10.3389/conf.fnhum.2019.229.00010 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 10 Feb 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Jennifer L Cook, University of Birmingham, School of Psychology, Birmingham, B15 2TT, United Kingdom, j.l.cook@bham.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Google Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Google Scholar Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook PubMed Bianca A Schuster Sophie L Sowden Diar Abdlkarim Alan M Wing Jennifer L Cook Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The original article [1] contained a minor error in the following sentence in the Discussion.

BACKGROUND:Variation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond. OBJECTIVES:To determine whether functional strength training (FST) improves upper limb recovery more than movement performance therapy (MPT). To identify: (a) neural correlates of response and (b) whether pre-intervention neural characteristics predict response. DESIGN:Explanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke. PARTICIPANTS:With some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke. INTERVENTIONS:Conventional physical therapy (CPT) plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was "hands-off" progressive resistive exercise cemented into functional task training. MPT was "hands-on" sensory/facilitation techniques for smooth and accurate movement. OUTCOMES:The primary efficacy measure was the Action Research Arm Test (ARAT). Neural measures: fractional anisotropy (FA) corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT) of motor-evoked potentials. ANALYSIS:Covariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response. RESULTS:288 Participants had: mean age of 72.2 (SD 12.5) years and mean ARAT 25.5 (18.2). For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72) for FST + CPT and 7.90 (9.18) for MPT + CPT, which did not differ statistically (p = 0.298). Correlations between ARAT change scores and baseline neural values were between 0.199, p = 0.320 for MPT + CPT RMT (n = 27) and -0.147, p = 0.385 for asymmetry of corticospinal tracts FA (n = 37). Interaction effects between neural values and ARAT change between baseline and outcome were not statistically significant. CONCLUSIONS:There was no significant difference in upper limb improvement between FST and MPT. Baseline neural measures did not correlate with upper limb recovery or predict therapy response. TRIAL REGISTRATION:Current Controlled Trials: ISRCT 19090862, http://www.controlled-trials.com.

The way an object is released by the passer to a partner is fundamental for the success of the handover and for the experienced fluency and quality of the interaction. Nonetheless, although its apparent simplicity, object handover involves a complex combination of predictive and reactive control mechanisms that were not fully investigated so far. Here, we show that passers use visual-feedback based anticipatory control to trigger the beginning of the release, to launch the appropriate motor program, and adapt such predictions to different speeds of the receiver’s reaching out movements. In particular, the passer starts releasing the object in synchrony with the collision with the receiver, regardless of the receiver’s speed, but the passer’s speed of grip force release is correlated with receiver speed. When visual feedback is removed, the beginning of the passer’s release is delayed proportionally with the receiver’s reaching out speed; however, the correlation between the passer’s peak rate of change of grip force is maintained. In a second study with 11 participants receiving an object from a robotic hand programmed to release following stereotypical biomimetic profiles, we found that handovers are experienced as more fluent when they exhibit more reactive release behaviours, shorter release durations, and shorter handover durations. The outcomes from the two studies contribute understanding of the roles of sensory input in the strategy that empower humans to perform smooth and safe handovers, and they suggest methods for programming controllers that would enable artificial hands to hand over objects with humans in an easy, natural and efficient way.

Asymmetry in weight-bearing is a common feature in poststroke hemiparesis and is related to temporal asymmetry during walking. The aim of this study was to investigate the effect of an auditory cue for stepping in place on measures of temporal and weight-bearing asymmetry. A total of 10 community-dwelling adults (6 males and 4 females) with chronic poststroke hemiparesis performed 5 un-cued stepping trials and 5 stepping trials cued by an auditory metronome cue. A Vicon system was used to collect full body kinematic trajectories. Two force platforms were used to measure ground reaction forces. Step, swing, and stance times were used to calculate temporal symmetry ratios. Weight-bearing was assessed using the vertical component of the ground reaction force and center of mass-center of pressure separation at mid-stance. Weight-bearing asymmetry was significantly reduced during stepping with an auditory cue. Asymmetry values for step, swing, and stance times were also significantly reduced with auditory cueing. These findings show that auditory cueing when stepping in place produces immediate reductions in measures of temporal asymmetry and dynamic weight-bearing asymmetry.

Accurate timing of movement in the hundreds of milliseconds range is a hallmark of human activities such as music and dance. Its study requires accurate measurement of the times of events (often called responses) based on the movement or acoustic record. This chapter provides a comprehensive over - view of methods developed to capture, process, analyse, and model individual and group timing [...] This chapter is structured in five main sections, as follows. We start with a review of data capture methods, working, in turn, through a low cost system to research simple tapping, complex movements, use of video, inertial measurement units, and dedicated sensorimotor synchronisation software. This is followed by a section on music performance, which includes topics on the selection of music materials, sound recording, and system latency. The identification of events in the data stream can be challenging and this topic is treated in the next section, first for movement then for music. Finally, we cover methods of analysis, including alignment of the channels, computation of between channel asynchrony errors and modelling of the data set.

Lightly touching an external reference, whether a fixed point or another person, reliably improves postural stability.In hemiparetic stroke patients, however, the effect of fixed point light touch (LT) on balance is uncertain.Moreover, it is not clear whether stroke patients respond in the same manner as healthy controls to light interpersonal touch (IPT).In the present study, therefore, the effects of LT and IPT on balance were contrasted in older adults with and without chronic hemiparetic stroke.Participants stood with open eyes in comfortable, normal bipedal quiet stance and performed 4 contact conditions in random order: no contact, fingertip LT, active fingertip IPT and passive elbow IPT.Body sway varied in response to the contact condition in both groups.The hemiparetic patients, whose impairment was relatively mild, showed responsiveness to LT and IPT similar to the non-hemiparetic group in terms of proportional sway reduction in the anteroposterior but not in the mediolateral sway direction.This indicates that light touch effects are robust but cannot be generalized from healthy older adults to hemiparetic stroke patients without consideration of moderating functional constraints of the individual and the specific postural context.Future research should include hemiparetic individuals with moderate to severe postural deficits to determine possible limitations of light touch balance support in stroke.