Publications
Predicting ankle moment trajectory with adaptive weighted ensemble of LSTM network
November 1, 2022
Conference Paper
Published in:
2022 IEEE High Perf. Extreme Comp. Conf. (HPEC), 19-23 September 2022, DOI: 10.1109/HPEC55821.2022.9926370.
Summary
Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on wearable sEMG and IMU data. While machine learning eliminates many practical challenges that troubled more traditional human body models for this application, we aim to expand on prior work that showed the feasibility of using LSTM models by employing an ensemble of LSTM networks. We present an adaptive weighted LSTM ensemble network and demonstrate its performance during standing, walking, running, and sprinting. Our result show that the LSTM ensemble outperformed every single LSTM model component within the ensemble. Across every activity, the ensemble reduced median root mean squared error (RMSE) by 0.0017-0.0053 N. m/kg, which is 2.7 – 10.3% lower than the best performing single LSTM model. Hypothesis testing revealed that most reductions in RMSE were statistically significant between the ensemble and other single models across all activities and subjects. Future work may analyze different trajectory lengths and different combinations of LSTM submodels within the ensemble.
Summary
Estimations of ankle moments can provide clinically helpful information on the function of lower extremities and further lead to insight on patient rehabilitation and assistive wearable exoskeleton design. Current methods for estimating ankle moments leave room for improvement, with most recent cutting-edge methods relying on machine learning models trained on...
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Impact of haptic cues and an active ankle exoskeleton on gait characteristics
July 1, 2022
Journal Article
Published in:
Hum. Factors, Vol. 0, No. 0, July 2022, p. 1-12.
Summary
Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were provided combinations of torque assistance and vibrotactile cues at pre-specified time points in late swing and early stance while walking on a self-paced treadmill. Participants were either given explicit instructions (N = 8) or were allowed to freely interpret (N=8) how to coordinate with cues. Results For the free interpretation group, the data support an 8% increase in stride length and 14% increase in speed with exoskeleton torque across cue timing, as well as a 5% increase in stride length and 7% increase in speed with only vibrotactile cues. When given explicit instructions, participants modulated speed according to cue timing-increasing speed by 17% at cues in late swing and decreasing speed 11% at cues in early stance compared to no cue when exoskeleton torque was off. When torque was on, participants with explicit instructions had reduced changes in speed. Conclusion These findings support that the presence of torque mitigates how cues were used and highlights the importance of explicit instructions for haptic cuing. Interpreting cues while walking with an exoskeleton may increase cognitive load, influencing overall human-exoskeleton performance for novice users. Application Interactions between haptic feedback and exoskeleton use during gait can inform future feedback designs to support coordination between users and exoskeletons.
Summary
Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were...
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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models
October 31, 2021
Conference Paper
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.
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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A neural network estimation of ankle torques from electromyography and accelerometry
August 26, 2021
Journal Article
Published in:
IEEE Trans. Neural Syst. Rehabilitation Eng., Vol. 29, 2021, pp. 1624-33.
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.
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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Ablation analysis to select wearable sensors for classifying standing, walking, and running
December 30, 2020
Journal Article
Published in:
Sensors, Vol. 21, No. 1, 30 December 2020, 194.
Summary
The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.
Summary
The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable...
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Ankle torque estimation during locomotion from surface electromyography and accelerometry
November 29, 2020
Conference Paper
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.
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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