Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks

Huang, Bo‐Wei; Li, Xinde

doi:10.3390/s23125653

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…The intelligent analysis of motion sequences has also attracted widespread attention from the healthcare research field in recent years [2,8]. However, motion sequence data exhibit characteristics such as high dimensionality and strong coupling, and they are highly susceptible to noise caused by environmental interference [10,11]; all of these factors pose difficulties for the detection and analysis of motion signals manually. Therefore, a reliable approach should be developed to accurately and autonomously process complicated motion data.…”

Section: Introductionmentioning

confidence: 99%

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Kong,

Liu,

Chen

et al. 2024

Mathematics

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Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis.

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Section: Introductionmentioning

confidence: 99%

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Kong,

Liu,

Chen

et al. 2024

Mathematics

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A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition

Hollinger,

Pollard,

Schall

et al. 2024

Sensors

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Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel hierarchical-based method combining action-level classification and subsequent joint-level regression to predict joint angles 100 ms into the future. K-nearest neighbors (KNN), bidirectional long short-term memory (BiLSTM), and temporal convolutional network (TCN) models were employed for action-level classification, and a random forest model trained on action-specific IMU data was used for joint-level prediction. A joint-level action-generic model trained on multiple actions (e.g., backward walking, kneeling down, kneeling up, running, and walking) was also used for predicting the joint angle. Compared with a hierarchical-based approach, the action-generic model had lower prediction error for backward walking, kneeling down, and kneeling up. Although the TCN and BiLSTM classifiers achieved classification accuracies of 89.87% and 89.30%, respectively, they did not surpass the performance of the action-generic random forest model when used in combination with an action-specific random forest model. This may have been because the action-generic approach was trained on more data from multiple actions. This study demonstrates the advantage of leveraging large, disparate data sources over a hierarchical-based approach for joint-level prediction. Moreover, it demonstrates the efficacy of an IMU-driven, task-agnostic model in predicting future joint angles across multiple actions.

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Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks

Cited by 2 publications

References 48 publications

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition

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