It's not all in your feet: Improving penalty kick performance with human-avatar interaction and machine learning.

Penalty kicks are increasingly decisive in major international football competitions. Yet, over 30% of shootout kicks are missed. The outcome of the kick often relies on the ability of the penalty taker to exploit anticipatory movements of the goalkeeper to redirect the kick toward the open side of the goal. Unfortunately, this ability is difficult to train using classical methods. We used an augmented reality simulator displaying an holographic goalkeeper to test and train penalty kick performance with 13 young elite players. Machine learning algorithms were used to optimize the learning rate by maintaining an optimal level of training difficulty. Ten training sessions of 20 kicks reduced the redirection threshold by 120 ms, which constituted a 28% reduction with respect to the baseline threshold. Importantly, redirection threshold reduction was observed for all trained players, and all things being equal, it corresponded to an estimated 35% improvement of the success rate.

Controlling the trajectory of a moving object substantially shortens the latency of motor responses to visual stimuli.

Motor responses to visual stimuli have shorter latencies for controlling than for initiating movement. The shorter latencies observed for movement control are notably believed to reflect the involvement of forward models when controlling moving limbs. We assessed whether controlling a moving limb is a "requisite" to observe shortened response latencies. The latency of button-press responses to a visual stimulus was compared between conditions involving or not involving the control of a moving object, but never involving any actual control of a body segment. When the motor response controlled a moving object, response latencies were significantly shorter and less variable, probably reflecting a faster sensorimotor processing (as assessed fitting a LATER model to our data). These results suggest that when the task at hand entails a control component, the sensorimotor processing of visual information is hastened, and this even if the task does not require to actually control a moving limb.

Model Based or Model Free? Comparing Adaptive Methods for Estimating Thresholds in Neuroscience.

The quantification of human perception through the study of psychometric functions Ψ is one of the pillars of experimental psychophysics. In particular, the evaluation of the threshold is at the heart of many neuroscience and cognitive psychology studies, and a wide range of adaptive procedures has been developed to improve its estimation. However, these procedures are often implicitly based on different mathematical assumptions on the psychometric function, and unfortunately, these assumptions cannot always be validated prior to data collection. This raises questions about the accuracy of the estimator produced using the different procedures. In the study we examine in this letter, we compare five adaptive procedures commonly used in psychophysics to estimate the threshold: Dichotomous Optimistic Search (DOS), Staircase, PsiMethod, Gaussian Processes, and QuestPlus. These procedures range from model-based methods, such as the PsiMethod, which relies on strong assumptions regarding the shape of Ψ, to model-free methods, such as DOS, for which assumptions are minimal. The comparisons are performed using simulations of multiple experiments, with psychometric functions of various complexity. The results show that while model-based methods perform well when Ψ is an ideal psychometric function, model-free methods rapidly outshine them when Ψ deviates from this model, as, for instance, when Ψ is a beta cumulative distribution function. Our results highlight the importance of carefully choosing the most appropriate method depending on the context.

Influence of mental workload on motion perception: A direct comparison of luminance-based and contrast-based stimuli.

In order to study the impact of increased mental workload on motion detection, twenty-four observers performed a motion discrimination task in which they had to detect odd moving patches. Two types of moving patches were used, namely luminance-based and contrast-based patches. For both types of patches, the motion discrimination task was performed with and without an additional N-Back task aimed at increasing the mental workload. The dual task decreased discrimination performance for both types of patches, but the difference was significantly larger for contrast-based patches, i.e., for second-order motion stimuli, both as an absolute and relative increment. This suggests that motion discrimination requires larger cognitive resources for contrast-based than for luminance-based stimuli, thereby hinting at the higher complexity of the cognitive mechanisms underlying second-order motion detection.

Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms.

Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients' movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians' assessment toolkit and improve fall prevention.

Influence of the Size of the Field of View on Visual Perception While Running in a Treadmill-Mediated Virtual Environment.

We investigated how the size of the horizontal field of view (FoV) affects visual speed perception with individuals running on a treadmill. Twelve moderately trained to trained participants ran on a treadmill at two different speeds (8 and 12 km/h) in front of a moving virtual scene. Different masks were used to manipulate the visible visual field, masking either the central or the peripheral area of the virtual scene or showing the full visual field. We asked participants to match the visual speed of the scene to their actual running speed. For each trial, participants indicated whether the scene was moving faster or slower than they were running. Visual speed was adjusted according to the responses using a staircase method until the Point of Subjective Equality was reached, that is until visual and running speed were perceived as matching. For both speeds and all FoV conditions, participants underestimated visual speed relative to the actual running speed. However, this underestimation was significant only when the peripheral FoV was masked. These results confirm that the size of the FoV should absolutely be taken into account for the design of treadmill-mediated virtual environments (VEs).

A Biologically-Inspired Model to Predict Perceived Visual Speed as a Function of the Stimulated Portion of the Visual Field.

Spatial orientation relies on a representation of the position and orientation of the body relative to the surrounding environment. When navigating in the environment, this representation must be constantly updated taking into account the direction, speed, and amplitude of body motion. Visual information plays an important role in this updating process, notably via optical flow. Here, we systematically investigated how the size and the simulated portion of the field of view (FoV) affect perceived visual speed of human observers. We propose a computational model to account for the patterns of human data. This model is composed of hierarchical cells' layers that model the neural processing stages of the dorsal visual pathway. Specifically, we consider that the activity of the MT area is processed by populations of modeled MST cells that are sensitive to the differential components of the optical flow, thus producing selectivity for specific patterns of optical flow. Our results indicate that the proposed computational model is able to describe the experimental evidence and it could be used to predict expected biases of speed perception for conditions in which only some portions of the visual field are visible.

Fast and automatic assessment of fall risk by coupling machine learning algorithms with a depth camera to monitor simple balance tasks.

BACKGROUND: Falls in the elderly constitute a major health issue associated to population ageing. Current clinical tests evaluating fall risk mostly consist in assessing balance abilities. The devices used for these tests can be expensive or inconvenient to set up. We investigated whether, how and to which extent fall risk could be assessed using a low cost ambient sensor to monitor balance tasks. METHOD: Eighty four participants, forty of which were 65 or older, performed eight simple balance tasks in front of a Microsoft Kinect sensor. Custom-made algorithms coupled to the Kinect sensor were used to automatically extract body configuration parameters such as body centroid and dispersion. Participants were then classified in two groups using a clustering method. The clusters were formed based on the parameters measured by the sensor for each balance task. For each participant, fall risk was independently assessed using known risk factors as age and average physical activity, as well as the participant's performance on the Timed Up and Go clinical test. RESULTS: Standing with a normal stance and the eyes closed on a foam pad, and standing with a narrow stance and the eyes closed on regular ground were the two balance tasks for which the classification's outcome best matched fall risk as assessed by the three known risk factors. Standing on a foam pad with eyes closed was the task driving to the most robust results. CONCLUSION: Our method constitutes a simple, fast, and reliable way to assess fall risk more often with elderly people. Importantly, this method requires very little space, time and equipment, so that it could be easily and frequently used by a large number of health professionals, and in particular by family physicians. Therefore, we believe that the use of this method would substantially contribute to improve fall prevention. TRIAL REGISTRATION: CER-VD 2015-00035. Registered 7 December 2015.

Regular physical activity modulates perceived visual speed when running in treadmill-mediated virtual environments.

In virtual reality, visual speed is usually underestimated relative to locomotor speed. Here we investigated how physical activity and fitness affect perceived visual speed when running in a treadmill-mediated virtual environment. Thirty healthy participants (ten sedentary individuals, ten team sport players and ten expert runners) ran on a treadmill at two different speeds (8, 12km/h) in front of a moving virtual scene. Participants were asked to match the speed of the visual scene to their running speed (i.e. treadmill speed), indicating for each trial whether the scene was moving slower or faster than the treadmill. The speed of the visual scene was adjusted according to the participant's response using a staircase until visual and running speeds were perceived as equivalent. More sedentary participants underestimated visual speed relative to their actual running speed. Specifically, visual speed had to exceed running speed to be perceived as equivalent. The underestimation of visual speed was speed-dependent, and it was significantly larger for sedentary participants than for team sports players and expert runners. The volume of physical activity per week was found to be the best predictor of visual speed perception for both running speeds, while the perceived effort constituted a good predictor only at 8km/h. Physical fitness, on the other hand turned out to be a poor predictor of visual speed perception. Therefore, in order to enhance users' engagement and their adherence to physical activity programs, the development of "personalized" treadmill-mediated virtual environments should take into account users' personal characteristics to provide the most natural and engaging feedback possible.

3D feedback and observation for motor learning: Application to the roundoff movement in gymnastics.

In this paper, we assessed the efficacy of different types of visual information for improving the execution of the roundoff movement in gymnastics. Specifically, two types of 3D feedback were compared to a 3D visualization only displaying the movement of the expert (observation) as well as to a more 'traditional' video observation. The improvement in movement execution was measured using different methods, namely subjective evaluations performed by official judges, and more 'quantitative appraisals based on time series analyses. Video demonstration providing information about the expert and 3D feedback (i.e., using 3D representation of the movement in monoscopic vision) combining information about the movement of the expert and the movement of the learner were the two types of feedback giving rise to the best improvement of movement execution, as subjectively evaluated by judges. Much less conclusive results were obtained when assessing movement execution using quantification methods based on time series analysis. Correlation analyses showed that the subjective evaluation performed by the judges can hardly be predicted/ explained by the 'more objective' results of time series analyses.

Automatic measurement of fall risk indicators in timed up and go test.

Fall risk assessment is usually conducted in specialized centers using clinical tests. Most of the time, these tests are performed only after the occurrence of health problems potentially affecting gait and posture stability. Our aim is to define fall risk indicators that could routinely be used at home to automatically monitor the evolution of fall risk over time. We used the standard Timed Up and Go (T.U.G.) test to classify 43 individuals into two classes of fall risk, namely high- vs low- risk. Several parameters related to the gait pattern and the sitting position included in the T.U.G. test were automatically extracted using an ambient sensor (Microsoft Kinect sensor). We were able to correctly classify all individuals using machine learning on the combination of two parameters among gait speed, step length and speed to sit down. Coupled to an ambient sensor installed at home to monitor the relevant parameters in daily activities, these algorithms could therefore be used to assess the evolution of fall risk, thereby improving fall prevention.

Matching optical flow to motor speed in virtual reality while running on a treadmill.

We investigated how visual and kinaesthetic/efferent information is integrated for speed perception in running. Twelve moderately trained to trained subjects ran on a treadmill at three different speeds (8, 10, 12 km/h) in front of a moving virtual scene. They were asked to match the visual speed of the scene to their running speed-i.e., treadmill's speed. For each trial, participants indicated whether the scene was moving slower or faster than they were running. Visual speed was adjusted according to their response using a staircase until the Point of Subjective Equality (PSE) was reached, i.e., until visual and running speed were perceived as equivalent. For all three running speeds, participants systematically underestimated the visual speed relative to their actual running speed. Indeed, the speed of the visual scene had to exceed the actual running speed in order to be perceived as equivalent to the treadmill speed. The underestimation of visual speed was speed-dependent, and percentage of underestimation relative to running speed ranged from 15% at 8km/h to 31% at 12km/h. We suggest that this fact should be taken into consideration to improve the design of attractive treadmill-mediated virtual environments enhancing engagement into physical activity for healthier lifestyles and disease prevention and care.

Validation of an ambient system for the measurement of gait parameters.

Fall risk in elderly people is usually assessed using clinical tests. These tests consist in a subjective evaluation of gait performed by healthcare professionals, most of the time shortly after the first fall occurrence. We propose to complement this one-time, subjective evaluation, by a more quantitative analysis of the gait pattern using a Microsoft Kinect. To evaluate the potential of the Kinect sensor for such a quantitative gait analysis, we benchmarked its performance against that of a gold-standard motion capture system, namely the OptiTrack. The "Kinect" analysis relied on a home-made algorithm specifically developed for this sensor, whereas the OptiTrack analysis relied on the "built-in" OptiTrack algorithm. We measured different gait parameters as step length, step duration, cadence, and gait speed in twenty-five subjects, and compared the results respectively provided by the Kinect and OptiTrack systems. These comparisons were performed using Bland-Altman plot (95% bias and limits of agreement), percentage error, Spearman's correlation coefficient, concordance correlation coefficient and intra-class correlation. The agreement between the measurements made with the two motion capture systems was very high, demonstrating that associated with the right algorithm, the Kinect is a very reliable and valuable tool to analyze gait. Importantly, the measured spatio-temporal parameters varied significantly between age groups, step length and gait speed proving the most effective discriminating parameters. Kinect-monitoring and quantitative gait pattern analysis could therefore be routinely used to complete subjective clinical evaluation in order to improve fall risk assessment during rehabilitation.

Automating the Timed Up and Go Test Using a Depth Camera.

Fall prevention is a human, economic and social issue. The Timed Up and Go (TUG) test is widely used to identify individuals with a high fall risk. However, this test has been criticized because its "diagnostic" is too dependent on the conditions in which it is performed and on the healthcare professionals running it. We used the Microsoft Kinect ambient sensor to automate this test in order to reduce the subjectivity of outcome measures and to provide additional information about patient performance. Each phase of the TUG test was automatically identified from the depth images of the Kinect. Our algorithms accurately measured and assessed the elements usually measured by healthcare professionals. Specifically, average TUG test durations provided by our system differed by only 0.001 s from those measured by clinicians. In addition, our system automatically extracted several additional parameters that allowed us to accurately discriminate low and high fall risk individuals. These additional parameters notably related to the gait and turn pattern, the sitting position and the duration of each phase. Coupling our algorithms to the Kinect ambient sensor can therefore reliably be used to automate the TUG test and perform a more objective, robust and detailed assessment of fall risk.

A skeleton-based approach to analyzing oculomotor behavior when viewing animated characters.

Knowing what people look at and understanding how they analyze the dynamic gestures of their peers is an exciting challenge. In this context, we propose a new approach to quantifying and visualizing the oculomotor behavior of viewers watching the movements of animated characters in dynamic sequences. Using this approach, we were able to illustrate, on a 'heat mesh', the gaze distribution of one or several viewers, i.e., the time spent on each part of the body, and to visualize viewers' timelines, which are linked to the heat mesh. Our approach notably provides an 'intuitive' overview combining the spatial and temporal characteristics of the gaze pattern, thereby constituting an efficient tool for quickly comparing the oculomotor behaviors of different viewers. The functionalities of our system are illustrated through two use case experiments with 2D and 3D animated media sources, respectively.

Not all memories are the same: Situational context influences spatial recall within one's city of residency.

Reference frames in spatial memory encoding have been examined intensively in recent years. However, their importance for recall has received considerably less attention. In the present study, passersby used tags to arrange a configuration map of prominent city center landmarks. It has been shown that such configurational knowledge is memorized within a north-up reference frame. However, participants adjusted their maps according to their body orientations. For example, when participants faced south, the maps were likely to face south-up. Participants also constructed maps along their location perspective-that is, the self-target direction. If, for instance, they were east of the represented area, their maps were oriented west-up. If the location perspective and body orientation were in opposite directions (i.e., if participants faced away from the city center), participants relied on location perspective. The results indicate that reference frames in spatial recall depend on the current situation rather than on the organization in long-term memory. These results cannot be explained by activation spread within a view graph, which had been used to explain similar results in the recall of city plazas. However, the results are consistent with forming and transforming a spatial image of nonvisible city locations from the current location. Furthermore, prior research has almost exclusively focused on body- and environment-based reference frames. The strong influence of location perspective in an everyday navigational context indicates that such a reference frame should be considered more often when examining human spatial cognition.

Reaching with the sixth sense: Vestibular contributions to voluntary motor control in the human right parietal cortex.

The vestibular system constitutes the silent sixth sense: It automatically triggers a variety of vital reflexes to maintain postural and visual stability. Beyond their role in reflexive behavior, vestibular afferents contribute to several perceptual and cognitive functions and also support voluntary control of movements by complementing the other senses to accomplish the movement goal. Investigations into the neural correlates of vestibular contribution to voluntary action in humans are challenging and have progressed far less than research on corresponding visual and proprioceptive involvement. Here, we demonstrate for the first time with event-related TMS that the posterior part of the right medial intraparietal sulcus processes vestibular signals during a goal-directed reaching task with the dominant right hand. This finding suggests a qualitative difference between the processing of vestibular vs. visual and proprioceptive signals for controlling voluntary movements, which are pre-dominantly processed in the left posterior parietal cortex. Furthermore, this study reveals a neural pathway for vestibular input that might be distinct from the processing for reflexive or cognitive functions, and opens a window into their investigation in humans.

Prediction in the Vestibular Control of Arm Movements.

The contribution of vestibular signals to motor control has been evidenced in postural, locomotor, and oculomotor studies. Here, we review studies showing that vestibular information also contributes to the control of arm movements during whole-body motion. The data reviewed suggest that vestibular information is used by the arm motor system to maintain the initial hand position or the planned hand trajectory unaltered during body motion. This requires integration of vestibular and cervical inputs to determine the trunk motion dynamics. These studies further suggest that the vestibular control of arm movement relies on rapid and efficient vestibulomotor transformations that cannot be considered automatic. We also reviewed evidence suggesting that the vestibular afferents can be used by the brain to predict and counteract body-rotation-induced torques (e.g., Coriolis) acting on the arm when reaching for a target while turning the trunk.

Asymmetric saccade reaction times to smooth pursuit.

Before initiating a saccade to a moving target, the brain must take into account the target's eccentricity as well as its movement direction and speed. We tested how the kinematic characteristics of the target influence the time course of this oculomotor response. Participants performed a step-ramp task in which the target object stepped from a central to an eccentric position and moved at constant velocity either to the fixation position (foveopetal) or further to the periphery (foveofugal). The step size and target speed were varied. Of particular interest were trials that exhibited an initial saccade prior to a smooth pursuit eye movement. Measured saccade reaction times were longer in the foveopetal than in the foveofugal condition. In the foveopetal (but not the foveofugal) condition, the occurrence of an initial saccade, its reaction time as well as the strength of the pre-saccadic pursuit response depended on both the target's speed and the step size. A common explanation for these results may be found in the neural mechanisms that select between oculomotor response alternatives, i.e., a saccadic or smooth response.