Alternative cue and response modalities maintain the Simon effect but impact task performance.

Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational applicability of the Simon task beyond finger/hand manipulations and visual/auditory cues is unclear, but important to consider as new technologies provide tactile cues and require motor responses from the lower extremity (e.g., exoskeletons). In this study, twenty participants completed the Simon task under four conditions, each combination of two cue (visual/tactile) and response (upper/lower-extremity) modalities. RT were significantly longer for incongruent than congruent cues across cue-response pairs. However, alternative cue-response pairs yielded slower RT and decreased accuracy for tactile cues and lower-extremity responses. Results support operational usage of the Simon task to probe inhibition using tactile cues and lower-extremity responses relevant for new technologies like exoskeletons and immersive environments.

Effects of Concurrent and Terminal Visual Feedback on Ankle Co-Contraction in Older Adults during Standing Balance.

This preliminary investigation studied the effects of concurrent and terminal visual feedback during a standing balance task on ankle co-contraction, which was accomplished via surface electromyography of an agonist-antagonist muscle pair (medial gastrocnemius and tibialis anterior muscles). Two complementary mathematical definitions of co-contraction indices captured changes in ankle muscle recruitment and modulation strategies. Nineteen healthy older adults received both feedback types in a randomized order. Following an analysis of co-contraction index reliability as a function of surface electromyography normalization technique, linear mixed-effects regression analyses revealed participants learned or utilized different ankle co-contraction recruitment (i.e., relative muscle pair activity magnitudes) and modulation (i.e., absolute muscle pair activity magnitudes) strategies depending on feedback type and following the cessation of feedback use. Ankle co-contraction modulation increased when concurrent feedback was used and significantly decreased when concurrent feedback was removed. Ankle co-contraction recruitment and modulation did not significantly change when terminal feedback was used or when it was removed. Neither ankle co-contraction recruitment nor modulation was significantly different when concurrent feedback was used compared to when terminal feedback was used. The changes in ankle co-contraction recruitment and modulation were significantly different when concurrent feedback was removed as compared to when terminal feedback was removed. Finally, this study found a significant interaction between feedback type, removal of feedback, and order of use of feedback type. These results have implications for the design of balance training technologies using visual feedback.

A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry.

Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque estimates and sequences of torque predictions from motion capture and ground reaction forces to wearable sensor data using several modern types of neural networks. We use dense feedforward, convolutional, neural ordinary differential equation, and long short-term memory neural networks to learn the mapping for ankle plantarflexion and dorsiflexion torque during standing, walking, running, and sprinting, and consider both single-point torque estimation, as well as the prediction of a sequence of future torques. Our results show that long short-term memory neural networks, which consider incoming data sequentially, outperform dense feedforward, neural ordinary differential equation networks, and convolutional neural networks. Predictions of future ankle torques up to 0.4 s ahead also showed strong positive correlations with the actual torques. The proposed method relies on learning from a motion capture dataset, but once the model is built, the method uses wearable sensors that enable torque estimation without the motion capture data.

Quantifying warfighter performance during a bounding rush (prone-sprinting-prone) maneuver.

A single sacrum mounted inertial measurement unit (IMU) was employed to analyze warfighter performance on a bounding rush (prone-sprinting-prone) task. Thirty-nine participants (23M/16F) performed a bounding rush task consisting of four bounding rush cycles. The sacrum mounted IMU recorded angular velocity and acceleration data were used to provide estimates of sacral velocity and position. Individual rush cycles were parsed into three principal movement phases; namely, the get up, sprint, and get down phases. The timing of each phase was analyzed, averaged for each participant, and compared to the overall rush cycle time using regression analysis. A cluster analysis further reveals differences between high and low performers. Get down time was most predictive of bounding rush performance (R2 = 0.75) followed by get up time (R2 = 0.58) and sprint time (R2 = 0.40). Comparing high and low performers, the get down time exhibited nearly twice the effect on mean rush cycle time compared to get up time (effect size of -2.61 to -1.46, respectively). Overall, this IMU-based method reveals key features of the bounding rush that govern performance. Consequently, this objective method may support future training regimens and performance standards for military recruits, and parallel applications for athletes.

Body-worn IMU array reveals effects of load on performance in an outdoor obstacle course.

This study introduces a new method to understand how added load affects human performance across a broad range of athletic tasks (ten obstacles) embedded in an outdoor obstacle course. The method employs an array of wearable inertial measurement units (IMUs) to wirelessly record the movements of major body segments to derive obstacle-specific metrics of performance. The effects of load are demonstrated on (N = 22) participants who each complete the obstacle course under four conditions including unloaded (twice) and with loads of 15% and 30% of their body weight (a total of 88 trials across the group of participants). The IMU-derived performance metrics reveal marked degradations in performance with increasing load across eight of the ten obstacles. Overall, this study demonstrates the significant potential in using this wearable technology to evaluate human performance across multiple tasks and, simultaneously, the adverse effects of body-borne loads on performance. The study addresses a major need of military organizations worldwide that frequently employ standardized obstacle courses to understand how added loads influence warfighter performance. Importantly, the findings and conclusions drawn from IMU data would not be possible using traditional timing metrics used to evaluate task performance.

Objective Metrics Quantifying Fit and Performance in Spacesuit Assemblies.

INTRODUCTION: Human-spacesuit fit is not well understood, especially in relation to operational performance and injury risk. Current fit decisions use subjective feedback. This work developed and evaluated new metrics for quantifying fit and assessed metric sensitivity to changes in padding between the human and hip brief assembly (HBA).METHODS: Three subjects donned the Mark III (MKIII) spacesuit with three padding thicknesses between the lower body and HBA. Subjects performed a walking task with inertial measurement units on the thigh and shin of both the human and suit. For each step, cadence, human knee task range of motion (tRoM), difference in human and suit tROM (ΔtRoM), and the relative coordination metric (ρ) between the human-suit femur and tibia were computed.RESULTS: The MKIII significantly reduced user cadence by 20.4% and reduced tRoM by 16.5% during walking with subject-dependent changes due to added padding. In general, the addition of padding significantly altered ΔtRoM; however, variability did exist between subjects. Mixed-effect regressions of dynamic fit (ρ) reflect distinct positive spikes in ρ around heel strike (human-dominated motion) and negative dips following toe off (suit-dominated motion).DISCUSSION: There were mixed effects of padding on gait performance and dynamic fit measures. Differences in dynamic fit between subjects may be more reliant on alternate aspects of fit, such as suit component sizes and designs, than padding level. Subjective feedback supported quantitative observations, highlighting metric utility. Future work will explore the effects of suit sizing components on measures of fit and performance.Fineman RA, McGrath TM, Kelty-Stephen DG, Abercromby AFJ, Stirling LA. Objective metrics quantifying fit and performance in spacesuit assemblies. Aerosp Med Hum Perform. 2018; 89(11):985-995.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

Estimating Stair Running Performance Using Inertial Sensors.

Stair running, both ascending and descending, is a challenging aerobic exercise that many athletes, recreational runners, and soldiers perform during training. Studying biomechanics of stair running over multiple steps has been limited by the practical challenges presented while using optical-based motion tracking systems. We propose using foot-mounted inertial measurement units (IMUs) as a solution as they enable unrestricted motion capture in any environment and without need for external references. In particular, this paper presents methods for estimating foot velocity and trajectory during stair running using foot-mounted IMUs. Computational methods leverage the stationary periods occurring during the stance phase and known stair geometry to estimate foot orientation and trajectory, ultimately used to calculate stride metrics. These calculations, applied to human participant stair running data, reveal performance trends through timing, trajectory, energy, and force stride metrics. We present the results of our analysis of experimental data collected on eleven subjects. Overall, we determine that for either ascending or descending, the stance time is the strongest predictor of speed as shown by its high correlation with stride time.

Strategy quantification using body worn inertial sensors in a reactive agility task.

Agility performance is often evaluated using time-based metrics, which provide little information about which factors aid or limit success. The objective of this study was to better understand agility strategy by identifying biomechanical metrics that were sensitive to performance speed, which were calculated with data from an array of body-worn inertial sensors. Five metrics were defined (normalized number of foot contacts, stride length variance, arm swing variance, mean normalized stride frequency, and number of body rotations) that corresponded to agility terms defined by experts working in athletic, clinical, and military environments. Eighteen participants donned 13 sensors to complete a reactive agility task, which involved navigating a set of cones in response to a vocal cue. Participants were grouped into fast, medium, and slow performance based on their completion time. Participants in the fast group had the smallest number of foot contacts (normalizing by height), highest stride length variance (normalizing by height), highest forearm angular velocity variance, and highest stride frequency (normalizing by height). The number of body rotations was not sensitive to speed and may have been determined by hand and foot dominance while completing the agility task. The results of this study have the potential to inform the development of a composite agility score constructed from the list of significant metrics. By quantifying the agility terms previously defined by expert evaluators through an agility score, this study can assist in strategy development for training and rehabilitation across athletic, clinical, and military domains.

Quantification and visualization of coordination during non-cyclic upper extremity motion.

There are many design challenges in creating at-home tele-monitoring systems that enable quantification and visualization of complex biomechanical behavior. One such challenge is robustly quantifying joint coordination in a way that is intuitive and supports clinical decision-making. This work defines a new measure of coordination called the relative coordination metric (RCM) and its accompanying normalization schemes. RCM enables quantification of coordination during non-constrained discrete motions. Here RCM is applied to a grasping task. Fifteen healthy participants performed a reach, grasp, transport, and release task with a cup and a pen. The measured joint angles were then time-normalized and the RCM time-series were calculated between the shoulder-elbow, shoulder-wrist, and elbow-wrist. RCM was normalized using four differing criteria: the selected joint degree of freedom, angular velocity, angular magnitude, and range of motion. Percent time spent in specified RCM ranges was used asa composite metric and was evaluated for each trial. RCM was found to vary based on: (1) chosen normalization scheme, (2) the stage within the task, (3) the object grasped, and (4) the trajectory of the motion. The RCM addresses some of the limitations of current measures of coordination because it is applicable to discrete motions, does not rely on cyclic repetition, and uses velocity-based measures. Future work will explore clinically relevant differences in the RCM as it is expanded to evaluate different tasks and patient populations.

Mobility and Agility During Locomotion in the Mark III Space Suit.

INTRODUCTION: The Mark III (MIII) space suit assembly (SSAs) implements a multibearing, hard-material hip brief assembly (HBA). We hypothesize that: 1) the MIII HBA restricts operator mobility and agility which manifests in effects to gait parameters; 2) the waist bearing provides rotational motion, partially alleviating the restrictions; and 3) there are resistive, speed-dependent torques associated with the spinning bearings which further diminish mobility and agility. METHODS: A subject (Suited and Unsuited) performed two planetary tasks-walking forward (WF) and backward (WB). An analysis of variance (ANOVA) and post hoc comparisons were performed to determine interaction effects. Motion capture data was processed to obtain gait parameters: static base (m), dynamic base (m), step length (m), stride length (m), cadence (steps/min), center of mass speed (m · s-1), foot clearance (toe and heel) (m), and bearing angular velocities (° · s-1). RESULTS: The static base when Suited (0.355 m) was larger than Unsuited (0.263 m). The Suited dynamic base (pooled, 0.200 m) was larger than both Unsuited WF (0.081 m) and WB (0.107 m). When Suited, the operator had lower clearance heights. The waist bearings provided about 7.2° of rotation when WB and WF. The maximum torque, while WF, in the right upper and mid bearings was 15.6 ± 1.35 Nm and 16.3 ± 1.28 Nm. DISCUSSION: This study integrated suit component properties and the emergent biomechanics of the operator to investigate how biomechanics are affected. The human hip has three collocated degrees of freedom (DOFs), whereas the HBA has a single DOF per bearing. The results can inform requirements for future SSA and other wearable system designs and evaluations.Cullinane CR, Rhodes RA, Stirling LA. Mobility and agility during locomotion in the Mark III space suit. Aerosp Med Hum Perform. 2017; 88(6):589-596.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Multifractal temporal correlations in circle-tracing behaviors are associated with the executive function of rule-switching assessed by the Trail Making Test.

Rule switching is 1 among a diverse set of executive functions whose delicate interactions allows us to coordinate behavior appropriately to changing contexts and demands. Clinical assessments such as the Trail Making Test (TMT) estimate flexibility of rule switching, but such assessments can be challenging to interpret. TMT scores are sums of many choice response times (RTs): More time spent reflects not simply manual motor speed and visual scanning but also fluctuation of attention to sequence and less flexible switching among rules to reject many inappropriate targets and instead select the single next appropriate target. A growing consensus recognizes that the aggregate of many choice RTs reflect multiplicative interaction of factors across multiple scales, among which manual motor speed, counting up sequence, and rule-switching are just a few. Multiplicative interactions entail first, fractal temporal correlations and, more importantly, variability of fractality within the same series, that is, "multifractality," The authors analyzed circle-tracing data to test whether tracing variance, degree of fractal temporal correlations, and multifractality correlate with TMT scores. Despite the absence of effects of variance, stronger temporal correlations indicated poorer Trails B performance, but multifractality moderated this relationship. These results suggest potential markers for predicting rule-switching ability from motor behavior. (PsycINFO Database Record

Use of a tracing task to assess visuomotor performance: effects of age, sex, and handedness.

BACKGROUND: Visuomotor abnormalities are common in aging and age-related disease, yet difficult to quantify. This study investigated the effects of healthy aging, sex, and handedness on the performance of a tracing task. Participants (n = 150, aged 21-95 years, 75 females) used a stylus to follow a moving target around a circle on a tablet computer with their dominant and nondominant hands. Participants also performed the Trail Making Test (a measure of executive function). METHODS: Deviations from the circular path were computed to derive an "error" time series. For each time series, absolute mean, variance, and complexity index (a proposed measure of system functionality and adaptability) were calculated. Using the moving target and stylus coordinates, the percentage of task time within the target region and the cumulative micropause duration (a measure of motion continuity) were computed. RESULTS: All measures showed significant effects of aging (p < .0005). Post hoc age group comparisons showed that with increasing age, the absolute mean and variance of the error increased, complexity index decreased, percentage of time within the target region decreased, and cumulative micropause duration increased. Only complexity index showed a significant difference between dominant versus nondominant hands within each age group (p < .0005). All measures showed relationships to the Trail Making Test (p < .05). CONCLUSIONS: Measures derived from a tracing task identified performance differences in healthy individuals as a function of age, sex, and handedness. Studies in populations with specific neuromotor syndromes are warranted to test the utility of measures based on the dynamics of tracking a target as a clinical assessment tool.