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

Published in:
Appl. Ergon., Vol. 100, 2022, 103648.

Summary

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.
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Summary

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...

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Relationships between cognitive factors and gait strategy during exoskeleton-augmented walking

Published in:
Proc. Human Factors and Ergonomics Society Annual Mtg, HFES, Vol. 65, No. 1, 2021.

Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like normalized stride length or width. Changes indicate shifting priorities like increasing stability or coordination with an exoskeleton. Relationships between cognitive factors and exoskeleton gait characteristics were assessed. Cognitive factors were quantified using a modified Simon task and a speed achievement task on a self-paced treadmill with and without a secondary go/no-go task. Individuals with faster reaction times and decreased ability to maintain a given speed tended to prioritize coordination with an exoskeleton over gait stability. These correlations indicate relationships between cognitive factors and individual exoskeleton-augmented gait strategy that should be further investigated to understand variation in exoskeleton use.
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Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like...

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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models

Published in:
43rd Annual Intl. Conf. of the IEEE Engineering in Medicine & Biology, 31 October 2021-4 November 2021.

Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
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Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning...

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Metrics for quantifying cognitive factors that may underlie individual variation in exoskeleton use

Published in:
Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol. 65, No. 1, 2021, pp. 216-20.

Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and gait strategy. Parameters were measured in 15 subjects via two tasks: (1) a modified Simon task and (2) a speed-achievement task with secondary go/no-go cues on a self-paced treadmill. Outcome metrics were assessed for significantly different intra- versus inter-subject variability. Reaction time measures from the modified Simon task, as well two speed-achievement metrics and one gait-strategy characteristic were found to show significant differences in intra- versus inter-subject variability. These results suggest that select cognitive factors may differentiate between individuals and be potential predictors for individual variation during exoskeleton system operation.
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Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and...

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A neural network estimation of ankle torques from electromyography and accelerometry

Summary

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.
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Summary

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...

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Ankle torque estimation during locomotion from surface electromyography and accelerometry

Published in:
2020 8th IEEE Intl. Conf. on Biomedical Robotics and Biomechatronics, BioRob, 29 November - 1 December 2020.

Summary

Estimations of human joint torques can provide quantitative, clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Standard methods for estimating joint torques are limited to laboratory or clinical settings since they require expensive equipment to measure joint kinematics and ground reaction forces. Wearable sensor data combined with neural networks may offer a less expensive and obtrusive estimation method.We present a method of mapping joint torque estimates obtained from motion capture and ground reaction forces to wearable sensor data. We use several different neural networks to learn the torque mapping for the ankle joints during standing, walking, running, and sprinting. Our results show that neural networks that consider time (recurrent and long short-term memory networks) outperform feedforward network architectures, producing results in the range of 0.005-0.008 N m/kg mean squared error (MSE) when compared to the inverse dynamics model on which it was trained. As a point of reference, the typical measurement errors from inverse dynamics models are in the range of 0.0004-0.0064 N m/kg MSE. Errors tended to increase with locomotion speed, with the highest errors during sprinting and the lowest during standing or walking. Future work may investigate model generalizability across sensor placements, subjects, locomotion variants, and usage duration. 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. These methods also have potential uses for the design and testing of wearable robotic systems outside of a laboratory environment.
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Summary

Estimations of human joint torques can provide quantitative, clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Standard methods for estimating joint torques are limited to laboratory or clinical settings since they require expensive equipment to measure joint kinematics and ground reaction...

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Sensorimotor conflict tests in an immersive virtual environment reveal subclinical impairments in mild traumatic brain injury

Summary

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally. The results show that, as compared to standard clinical tests, the provocative perturbations illuminate balance impairments in subjects who have had mild traumatic brain injuries. Perturbations delivered while subjects were walking provided greater discriminability (average accuracy ≈ 0.90) than those delivered during standing (average accuracy ≈ 0.65) between mTBI subjects and healthy controls. Of the categories of features extracted to characterize balance, the lower limb accelerometry-based metrics proved to be most informative. Further, in response to perturbations, subjects with an mTBI utilized hip strategies more than ankle strategies to prevent loss of balance and also showed less variability in gait patterns. We have shown that sensorimotor conflicts illuminate otherwise-hidden balance impairments, which can be used to increase the sensitivity of current clinical procedures. This augmentation is vital in order to robustly detect the presence of balance impairments after mTBI and potentially define a phenotype of balance dysfunction that enhances risk of injury.
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Summary

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor...

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