Subject-Independent, Biological Hip Moment Estimation During Multimodal Overground Ambulation Using Deep Learning

Molinaro, Dean D.; Kang, Inseung; Camargo, Jonathan; Gombolay, Matthew; Young, Aaron

doi:10.1109/tmrb.2022.3144025

Cited by 42 publications

(35 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They didn't however compare the performance to FCN or transformers. On the other hand, a study by Molinaro et al [46] found that the LSTM outperforms the FCN in joint moment prediction, which is similar to the result of our study, where the LSTM outperforms the FCN in one-step-ahead predictions.…”

Section: Discussionsupporting

confidence: 92%

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

2023

View full text Add to dashboard Cite

Gait trajectory prediction models have several applications in exoskeleton control; they can be used as feed-forward input to low-level controllers and to generate reference/target trajectories for position-controlled exoskeletons. In our study, we implement four deep learning models (LSTM, FCN, CNN and Transformer) that perform one-step-ahead gait trajectory prediction after training on gait patterns of typically developing children. We propose a methodology that optimises for stability in long-term forecasts, and evaluate the performance of the models on typically developing (TD) and Cerebral Palsy (CP) gait during recursive prediction of 200 time-steps in the future (which may lead to propagation of errors) and in the presence of varying levels of Gaussian noise (1%-5%). Results on TD gait show that the FCN and Transformer, with mean absolute errors (MAEs) for one-step-ahead predictions between 1.17°-1.63°, are the most suitable for the intended application. We also proposed an approach for generating adaptive trajectories that can be used as reference trajectories for position-controlled exoskeletons. Gait patterns from children with Cerebral Palsy were fed into gait trajectory prediction models trained on typically developing gait only, to generate corrective patterns. Preliminary results show that the gait patterns of typically developing children were introduced onto the generated trajectories.

show abstract

Section: Discussionsupporting

confidence: 92%

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

2023

View full text Add to dashboard Cite

show abstract

“…Although limited to an instrumented treadmill and not yet suitable for autonomous exoskeletons, similar approaches may increase the understanding of how exoskeletons, which often augment the production of ankle power, interact with the body. Recent work has also used machine-learning techniques to estimate hip moments without requiring an instrumented treadmill 239 .…”

Section: Unimpaired Usersmentioning

confidence: 99%

Opportunities and challenges in the development of exoskeletons for locomotor assistance

et al. 2022

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

“…Recently, researchers are focusing on acquiring kinetics parameters through data-driven method [34]- [43], [43]- [46]. However, these studies are still relying on a large number of sensors [35], reversely acquired simulated IMU data from the retrospective motion capture data [34], [37], and highly repetitive treadmill/level-ground walking [36]- [43], [43]- [46]. Since experimentally collected IMU data contains noise induced from multi-frequncy vibration during the impact of the limb on the ground, the simulated IMU data that was acquired from retrospectively captured motion capture data may not provide a reliable kinetics estimation during real application.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach

Hossain

Guo

Choi

2023

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they were limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, Kinetics-FM-DLR-Ensemble-Net to estimate the hip, knee, and ankle joint moments in the sagittal plane and 3 dimensional ground reaction forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representative walking conditions in daily living, such as treadmill, level-ground, stair, and ramp with different walking speeds. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

show abstract

Subject-Independent, Biological Hip Moment Estimation During Multimodal Overground Ambulation Using Deep Learning

Cited by 42 publications

References 37 publications

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

Deep Learning Models for Stable Gait Prediction Applied to Exoskeleton Reference Trajectories for Children With Cerebral Palsy

Opportunities and challenges in the development of exoskeletons for locomotor assistance

Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach

Contact Info

Product

Resources

About