Recognising and predicting gait cycle states for weight-reducing exoskeleton robots using deep learning

Hidetaka Nambo; Hanqing Zhao

doi:10.1504/ijma.2021.10043413

Recognising and predicting gait cycle states for weight-reducing exoskeleton robots using deep learning

International Journal of Mechatronics and Automation ◽

10.1504/ijma.2021.10043413 ◽

2021 ◽

Vol 1 (1) ◽

pp. 1

Author(s):

Hidetaka Nambo ◽

Hanqing Zhao

Keyword(s):

Deep Learning ◽

Gait Cycle

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Gait Energy Image Reconstruction from Degraded Gait Cycle Using Deep Learning

Lecture Notes in Computer Science - Computer Vision – ECCV 2018 Workshops ◽

10.1007/978-3-030-11018-5_52 ◽

2019 ◽

pp. 654-658

Author(s):

Maryam Babaee ◽

Linwei Li ◽

Gerhard Rigoll

Keyword(s):

Deep Learning ◽

Image Reconstruction ◽

Gait Cycle ◽

Gait Energy Image

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A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification

Sensors ◽

10.3390/s21186202 ◽

2021 ◽

Vol 21 (18) ◽

pp. 6202

Author(s):

Pedro Albuquerque ◽

Tanmay Tulsidas Verlekar ◽

Paulo Lobato Correia ◽

Luís Ducla Soares

Keyword(s):

Deep Learning ◽

Gait Analysis ◽

Gait Cycle ◽

State Of The Art ◽

Objective Assessment ◽

Human Motion ◽

Human Motion Analysis ◽

Learning Approach ◽

Current Standard ◽

Key Frames

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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