GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation

Endo, Mark; Poston, Kathleen L.; Sullivan, Edith V.; Li, Feifei; Pohl, Kilian M.; Adeli, Ehsan

doi:10.1007/978-3-031-16452-1_13

Cited by 15 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are few recent developments that have shown the potential of the SSL paradigm in engineering fields (Yengera et al, 2018;Endo et al, 2022;Yu et al, 2022;Shurrab and Duwairi, 2022), and several PHM researchers considered this approach to address the data scarcity in fault diagnostics problems, showing promising results (Hahn and Mechefske, 2021;Ding et al, 2022). However, to date, there is, to the best of the authors' knowledge, only a limited amount of existing research that focuses on the application of SSL to prognostic problems in PHM, for example for RUL estimation on the NASA C-MAPSS 1 dataset (Yoon et al, 2017;Ellefsen et al, 2019;Krokotsch et al, 2022;Guo et al, 2022).…”

Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Akrim

Gogu

Vingerhoeds

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Akrim

Gogu

Vingerhoeds

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…There are few recent developments that have shown the potential of the SSL paradigm in engineering fields [32][33][34][35], and several PHM researchers considered this approach to address the data scarcity in fault diagnostics problems, showing promising results [36,37]. However, to date, there is, to the best of the authors' knowledge, only a limited amount of existing research that focuses on the application of SSL to prognostic problems in PHM, for example for RUL estimation on the NASA C-MAPSS 1 dataset [39,40,21,22].…”

Section: Self-supervised Learningmentioning

confidence: 99%

Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem

Akrim¹,

Gogu²,

Vingerhoeds³

et al. 2023

Preprint

View full text Add to dashboard Cite

With the increasing availability of data for Prognostics and Health Management (PHM), Deep Learning (DL) techniques are now the subject of considerable attention for this application, often achieving more accurate Remaining Useful Life (RUL) predictions. However, one of the major challenges for DL techniques resides in the difficulty of obtaining large amounts of labelled data on industrial systems. To overcome this lack of labelled data, an emerging learning technique is considered in our work: Self-Supervised Learning, a sub-category of unsupervised learning approaches. This paper aims to investigate whether pre-training DL models in a self-supervised way on unlabelled sensors data can be useful for RUL estimation with only Few-Shots Learning, i.e. with scarce labelled data. In this research, a fatigue damage prognostics problem is addressed, through the estimation of the RUL of aluminum alloy panels (typical of aerospace structures) subject to fatigue cracks from strain gauge data. Synthetic datasets composed of strain data are used allowing to extensively investigate the influence of the dataset size on the predictive performance. Results show that the self-supervised pre-trained models are able to significantly outperform the non-pre-trained models in downstream RUL prediction task, and with less computational expense, showing promising results in prognostic tasks when only limited labelled data is available.

show abstract

“…This study also presents an approach to generate adaptive target/reference trajectories for children with CP, that vary from cycle-to-cycle, and take into account the asymmetry of the left and right joints, since a separate trajectory will be generated for each joint of the left and right sides. A similar approach has been done by Endo et al [37], which train the GaitForMer network based on healthy gait patterns for human motion forecasting, and then retrain the model that learned gait mappings to predict the severity of gait impairment of patients with Parkinson's disease, based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).…”

Section: Introductionmentioning

confidence: 99%

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

GaitForeMer: Self-supervised Pre-training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation

Cited by 15 publications

References 20 publications

Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Self-Supervised Learning for data scarcity in a fatigue damage prognostic problem

Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic Problem

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

Contact Info

Product

Resources

About