Learning Gait Representations with Noisy Multi-Task Learning

Cosma, Adrian; Radoi, Ion Emilian

doi:10.3390/s22186803

Cited by 9 publications

(2 citation statements)

References 79 publications

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“…In the work of Cheriet et al [33], they applied a Multi-Speed Transformer Network in video data to classify stages of neurodegenerative diseases and achieved an accuracy of 96.9%. Finally, Cosma, Catruna, and Radoi [34] published a paper where they used Self-Supervised Vision Transformers in human gait video data as a biometric authentication method.…”

Section: B Activity Recognition Using Movement Analysis Systemmentioning

confidence: 99%

Accuracy Comparison of CNN, LSTM, and Transformer for Activity Recognition Using IMU and Visual Markers

Trujillo-Guerrero,

Román-Niemes,

Jaén-Vargas

et al. 2023

IEEE Access

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Human activity recognition (HAR) has applications ranging from security to healthcare. Typically these systems are composed of data acquisition and activity recognition models. In this work, we compared the accuracy of two acquisition systems: Inertial Measurement Units (IMUs) vs Movement Analysis Systems (MAS). We trained models to recognize arm exercises using state-of-the-art deep learning architectures and compared their accuracy. MAS uses a camera array and reflective markers. IMU uses accelerometers, gyroscopes, and magnetometers. Sensors of both systems were attached to different locations of the upper limb. We captured and annotated 3 datasets, each one using both systems simultaneously. For activity recognition, we trained 8 architectures, each one with different operations and layers configurations. The best architectures were a combination of CNN, LSTM, and Transformer achieving test accuracy from 89% to 99% on average. We evaluated how feature selection reduced the sensors required. We found IMU and MAS data were able to distinguish correctly the arm exercises. CNN layers at the beginning produced better accuracy on challenging datasets. IMU had advantages over other aquisition systems for activity recognition. We analyzed the relations between models accuracy, signal waveforms, signals correlation, sampling rate, exercise duration, and window size. Finally, we proposed the use of a single IMU located at the wrist and a variable-size window extraction.

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Section: B Activity Recognition Using Movement Analysis Systemmentioning

confidence: 99%

Accuracy Comparison of CNN, LSTM, and Transformer for Activity Recognition Using IMU and Visual Markers

Trujillo-Guerrero,

Román-Niemes,

Jaén-Vargas

et al. 2023

IEEE Access

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“…For commercial applications, gaze estimation in unconstrained environments can be essential for gathering actionable insights into customer behaviour [2]. Coupled with other uninstrusive soft-biometrics such as face and gait analysis [4], [8], gaze estimation from existing CCTV infrastructure enables a wide range of analytics which can be used to optimize customer experience and satisfaction.…”

Section: Introductionmentioning

confidence: 99%

From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns

Catruna

Cosma

Radoi

2021

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

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Gaze estimation, the task of predicting where an individual is looking, is a critical task with direct applications in areas such as human-computer interaction and virtual reality. Estimating the direction of looking in unconstrained environments is difficult, due to the many factors that can obscure the face and eye regions. In this work we propose CrossGaze, a strong baseline for gaze estimation, that leverages recent developments in computer vision architectures and attention-based modules. Unlike previous approaches, our method does not require a specialized architecture, utilizing already established models that we integrate in our architecture and adapt for the task of 3D gaze estimation. This approach allows for seamless updates to the architecture as any module can be replaced with more powerful feature extractors. On the Gaze360 benchmark, our model surpasses several state-of-the-art methods, achieving a mean angular error of 9.94 • . Our proposed model serves as a strong foundation for future research and development in gaze estimation, paving the way for practical and accurate gaze prediction in real-world scenarios.

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