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Assisting Gait Stability in Walking Aid Users Exploiting Biomechanical Variables Correlation

IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第2期10卷 2040-2047页

作者： Fortuna, Andrea Lorenzini, Marta Cho, Younggeol Arbaud, Robin Castiglia, Stefano Filippo Serrao, Mariano Ranavalo, Alberto De Momi, Elena Ajoudani, Arash Ist Italiano Tecnol Human Robot Interfaces & Interact Lab I-16163 Genoa Italy Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Sapienza Univ Rome Dept Med & Surg Sci & Biotechnol I-00185 Latina Italy Policlin Italia Movement Anal Lab I-00162 Rome Italy INAIL Dept Occupat & Environm Med Epidemiol & Hyg I-00040 Rome Italy

Walking aids for individuals with musculoskeletal frailty or motor disabilities must ensure adequate physical support and assistance to their users. To this end, sensor-enabled human state monitoring and estimation are crucial. This letter proposes an innovative approach to assessing users' stability while walking with WANDER, a novel gait assistive device, by exploiting the correlation between the eXtrapolated Center of Mass (XCoM) and the Base of Support (BoS) edges. First, the soundness of this metric in monitoring gait stability is proven. Experiments on 25 healthy individuals show that the median value of Pearson's correlation coefficient (p-value < 0.05) remained high during the forward walk for all subjects. Next, a correlation-based variable admittance (CVA) controller is implemented, whose parameters are tuned to physically support users when a gait perturbation is detected (i.e. low values of Pearson's correlation coefficient). To validate this approach, 13 healthy subjects were asked to compare our controller with a force threshold-based (FVA) one. The CVA controller's performance in discriminating stable and perturbed gait conditions showed a high sensitivity value, comparable to FVA, and improved performance in terms of specificity. The number of false and missed detections of gait perturbation was considerably reduced, independently of walking speed, exhibiting a higher level of safety and smoothness compared to the FVA controller. Overall, the outcome of this study gives promising evidence of the proposed metric capability in identifying user stability and triggering WANDER's assistance.

关键词： Legged locomotion Stability criteria Measurement Sensors Assistive devices Laser stability Laser radar Force Correlation Robot sensing systems Physically assistive devices human and humanoid motion analysis and synthesis sensor-based control

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Beyond Gait: Seamless Knee Angle Prediction for Lower Limb Prosthesis in Multiple Scenarios

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 406-413页

作者： Wang, Pengwei Chen, Yilong Su, Wan Wang, Jie Ma, Teng Yu, Haoyong Natl Univ Singapore Dept Mech Engn Singapore 117575 Singapore Natl Univ Singapore Dept Biomed Engn Singapore 119077 Singapore

Knee angle estimation plays a crucial role in the development of lower limb assistive devices, particularly prostheses. Current research in this area primarily focuses on stable gait movements, which limits applicability to real-world scenarios where human motion is far more complex. In this paper, we focus on estimating the knee angle in a broader range of activities beyond simple gait movements. By leveraging the synergy of whole-body dynamics, we propose a transformer-based probabilistic framework, the Angle Estimation Probabilistic Model (AEPM), which offers precise knee angle estimation across various daily movements. AEPM achieves an overall RMSE of 6.83 degrees, with an RMSE of 2.93 degrees in walking scenarios, outperforming the current state of the art with a 24.68% improvement in walking prediction accuracy. Additionally, our method can achieve seamless adaptation between different locomotion modes. Also, this model can be utilized to analyze the synergy between the knee and other joints. We reveal that the whole body movement has valuable information for knee movement, which can provide insights into designing sensors for prostheses.

关键词： Knee Transformers Estimation Decoding Sensors Probabilistic logic Prosthetics Feature extraction Accuracy Legged locomotion Deep learning methods human and humanoid motion analysis and synthesis knee angle estimation prosthetics and exoskeletons

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Trajectory Prediction for Heterogeneous Agents: A Performance analysis on Small and Imbalanced Datasets

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第7期9卷 6576-6583页

作者： de Almeida, Tiago Rodrigues Zhu, Yufei Rudenko, Andrey Kucner, Tomasz P. Stork, Johannes A. Magnusson, Martin Lilienthal, Achim J. Orebro Univ Ctr Appl Autonomous Sensor Syst S-70182 Orebro Sweden Robert Bosch GmbH Corp Res D-71272 Stuttgart Germany Aalto Univ Sch Elect Engn Espoo 02150 Finland Finnish Ctr Artificial Intelligence FI-00076 Aalto Finland Tech Univ Munich D-80333 Munich Germany

Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to reduce forecast uncertainty and get more accurate predictions for heterogeneous agents. However, this is hardly explored in the prior art, especially for mobile robots and in limited data applications. In this paper, we analyse different class-conditioned trajectory prediction methods on two datasets. We propose a set of conditional pattern-based and efficient deep learning-based baselines, and evaluate their performance on robotics and outdoors datasets (TH & Ouml;R-MAGNI and Stanford Drone Dataset). Our experiments show that all methods improve accuracy in most of the settings when considering class labels. More importantly, we observe that there are significant differences when learning from imbalanced datasets, or in new environments where sufficient data is not available. In particular, we find that deep learning methods perform better on balanced datasets, but in applications with limited data, e.g., cold start of a robot in a new environment, or imbalanced classes, pattern-based methods may be preferable.

关键词： Datasets for human motion deep learning methods human and humanoid motion analysis and synthesis human detection and tracking

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Fast Direct Optimal Control for humanoids Based on Dynamics Representation in FPC Latent Space

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第4期9卷 3823-3830页

作者： Shimizu, Soya Ayusawa, Ko Venture, Gentiane Tokyo Univ Agr & Technol Grad Sch Engn Dept Mech Syst Engn Koganei Tokyo 1848588 Japan Natl Inst Adv Ind Sci & Technol Human Augmentat Res Ctr Kashiwa Chiba 2770082 Japan CNRS AIST JRL Joint Robot Lab UMI3218 RL Tsukuba Ibaraki 3058560 Japan Univ Tokyo Dept Mech Engn Bunkyo ku Tokyo 1138656 Japan

This study introduces a novel approach to humanoid Robot motion Generation using Functional Principal Component analysis (FPCA) within the framework of Direct Optimal Control (DOC). FPCA efficiently compresses high-dimensional motion data, including ground reaction forces, into a low-dimensional space known as the FPC space. These low-dimensional elements preserve the essential characteristics of the original motions and allow for the synthesis of specific elements to create entirely new representations. By using these low-dimensional elements as optimization variables, we anticipated a significant reduction in computational time for motion generation. Experiments on the humanoid robot HRP-4 J, considering various objective functions, demonstrated the method's applicability for generating whole-body motions. The Root Mean Square Error (RMSE) for angle data between the proposed method and conventional methods yielded an average RMSE of 0.007 rd, indicating precise motion generation without compromising data characteristics. Moreover, the proposed method's computation time was 35.8 times faster than the conventional approach.

关键词： human and humanoid motion analysis and synthesis whole-body motion planning and control optimization and optimal control

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human - the human motion Anticipation Algorithm Based on Recurrent Neural Networks

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第12期9卷 11521-11528页

作者： Noppeney, Victor Escalante, Felix M. Maggi, Lucas Boaventura, Thiago Univ Sao Paulo Sao Carlos Sch Engn BR-13566590 Sao Carlos Brazil Sao Paulo State Univ Inst Sci & Technol BR-18087180 Sorocaba Brazil

Predicting human motion may lead to considerable advantages for human-robot interaction, particularly when precise synchronization between the robot's motion and the user's movement is imperative. The inherent stochastic nature of human behavior, combined with the restricted window of response, can give rise to residual and undesirable forces during interactions, potentially harming the user. Therefore, efficient prediction of human joint movements may enhance the performance of various interaction control frameworks used in wearable robots. This paper proposes the human algorithm for predicting human joint motion based on a recurrent neural network. This algorithm consists of a long-term memory network, used to interpret sequences of poses, and a prediction layer, employed to build the most likely future user poses within a specified time horizon. Network training was performed using datasets encompassing various subjects and types of motion. The results demonstrate the effectiveness of the proposed algorithm, as evidenced by average general prediction errors below 0.1 radians for predictive horizons of up to 500 milliseconds. Furthermore, a mean absolute error of 0.026 radians was achieved for a periodic treadmill walk. Simulation results demonstrate a large improvement in transparency control performance in a case study with an upper limb exoskeleton robot.

关键词： Prediction algorithms Training Robots Long short term memory Recurrent neural networks Vectors Computer architecture Wearable robots Transfer learning Time measurement Modeling and simulating humans human and humanoid motion analysis and synthesis intention recognition

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Dual-Alignment Domain Adaptation for Pedestrian Trajectory Prediction

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第12期9卷 10962-10969页

作者： Li, Wenzhan Li, Fuhao Jing, Xinghui Feng, Pingfa Zeng, Long Tsinghua Univ Shenzhen Int Grad Sch Dept Adv Mfg Shenzhen 518000 Peoples R China Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China

Predicting the plausible future paths of pedestrians is essential for human-involved applications (e.g., autonomous driving and service robotics). Existing pedestrian trajectory prediction methods mainly focus on the performance of multi-scene trained models in single-scene tests, neglecting the cross-scene knowledge differences in practice. To address this issue, we propose a generic dual-alignment framework for pedestrian trajectory prediction. Concretely, we analyze the domain difference at macro and micro scales and mitigate them respectively: at macro scale, an attention-based temporal convolutional generative model transfers the paths of pedestrians and their interaction information from the source domain to the target domain to align the data-level distributions;at micro scale, an auxiliary adversarial network is integrated to assist in training the prediction network to align the feature-level domain-invariant knowledge. Cross-domain experiments demonstrate that our approach significantly improves the performance of existing pedestrian trajectory prediction benchmarks (up to 53.5%) and outperforms previous domain adaptive works (up to 41.7%).

关键词： Trajectory Pedestrians Predictive models Generative adversarial networks Training Adaptation models Generators Feature extraction Data models Convolutional neural networks Deep learning methods human and humanoid motion analysis and synthesis social HRI pedestrian trajectory prediction

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STAGP: Spatio-Temporal Adaptive Graph Pooling Network for Pedestrian Trajectory Prediction

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第3期9卷 2001-2007页

作者： Liu, Zhening He, Li Yuan, Liang Lv, Kai Zhong, Runhao Chen, Yaohua Xinjiang Univ Intelligent Mfg Modern Ind Coll Sch Mech Engn Urumqi 830017 Peoples R China Beijing Univ Chem Technol Sch Informat Sci & Technol Beijing 100029 Peoples R China

Predicting how pedestrians will move in the future is crucial for robot navigation, autonomous driving, and video surveillance. The complex interactions among pedestrians make it difficult to predict their future trajectory. Previous studies have primarily focused on modeling the interaction features of all pedestrians in the scene. However, this approach often results in an abundance of irrelevant interactions and overlooks the time-dependent characteristics. To address these issues, we propose a spatio-temporal adaptive graph pooling network (STAGP) for pedestrian trajectory prediction. STAGP adopts adaptive graph pooling to explicitly model interactions between pedestrians, redundant interactions and establishing directed interactions. In addition, we utilize spatio-temporal attention to extract temporal features of pedestrian interactions. For the prediction of future trajectories, we use a time-extrapolator convolutional neural network (TXP-CNN). ETH and UCY datasets were used to evaluate STAGP. Comparing STAGP to the values, the experimental results indicate that it is competitive in terms of ADE and FDE metrics.

关键词： Pedestrians Trajectory Adaptation models Feature extraction Video surveillance Transformers Stars Deep learning methods human and humanoid motion analysis and synthesis social HRI

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Dynamic Mobile Manipulation via Whole-Body Bilateral Teleoperation of a Wheeled humanoid

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第2期9卷 1214-1221页

作者： Purushottam, Amartya Xu, Christopher Jung, Yeongtae Ramos, Joao Univ Illinois Dept Elect & Comp Engn Champaign IL 61820 USA Jeonbuk Natl Univ Dept Mech Syst Engn Jeonrabug Do 54896 South Korea Jeonbuk Natl Univ Adv Transportat Machinery Res Ctr Jeonrabug Do 54896 South Korea Univ Illinois Dept Mech Sci & Engn Champaign IL USA

humanoid robots have the potential to help human workers by realizing physically demanding manipulation tasks such as moving large boxes within warehouses. We define such tasks as Dynamic Mobile Manipulation (DMM). This letter presents a framework for DMM via whole-body teleoperation, built upon three key contributions: Firstly, a teleoperation framework employing a human Machine Interface (HMI) and a bi-wheeled humanoid, SATYRR, is proposed. Secondly, the study introduces a dynamic locomotion mapping, utilizing human-robot reduced order models, and a kinematic retargeting strategy for manipulation tasks. Additionally, the letter discusses the role of whole-body haptic feedback for wheeled humanoid control. Finally, the system's effectiveness and mappings for DMM are validated through locomanipulation experiments and heavy box pushing tasks. Here we show two forms of DMM: grasping a target moving at an average speed of 0.4 m/s, and pushing boxes weighing up to 105% of the robot's weight. By simultaneously adjusting their pitch and using their arms, the pilot adjusts the robot pose to apply larger contact forces and move a heavy box at a constant velocity of 0.2 m/s.

关键词： Robots Robot kinematics humanoid robots Dynamics Task analysis Robot sensing systems Mobile robots humanoid whole-body control telerobotics and teleoperation human and humanoid motion analysis and synthesis human-in-the-loop motion retargeting

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Workstation Suitability Maps: Generating Ergonomic Behaviors on a Population of Virtual humans With Multi-Task Optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第11期8卷 7384-7391页

作者： Zhong, Jacques Weistroffer, Vincent Mouret, Jean-Baptiste Colas, Francis Maurice, Pauline Univ Paris Saclay CEA List F-91120 Palaiseau France Univ Lorraine CNRS Inria LORIA F-54000 Nancy France

In industrial workstations, the morphology of the worker is a key factor for the feasibility and the ergonomics of an activity. Existing digital human modeling tools can simulate different morphologies at work, but hardly scale to a large population of workers because of limited consideration of morphology-specific behaviors and computational cost. This letter presents a framework to efficiently evaluate the suitability of a workstation over a large population of workers in a physics-based simulation. Activities are simulated through a two-step optimization process, involving a quadratic-programming-based whole-body controller and a multi-task optimizer for behavioral adaptation. On a screwdriving scenario, we demonstrate how our framework can help ergonomists improve workstation designs thanks to the resulting suitability maps where generated behaviors are optimized for each morphology w.r.t. ergonomics and performance.

关键词： Modeling and simulating humans human and humanoid motion analysis and synthesis ergonomics multi-task optimization

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Synchronized human-humanoid motion Imitation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第7期8卷 4155-4162页

作者： Dallard, Antonin Benallegue, Mehdi Kanehiro, Fumio Kheddar, Abderrahmane CNRS AIST Joint Robot Lab IRL3218 Tsukuba Japan Univ Montpellier CNRS LIRMM UMR5506 Montpellier France

We present a tele-operation control framework that (i) enhances the upper motion synchrony between a user and a robot using the minimum-jerk model coupled with a recursive least-square filter, and (ii) synchronizes the walking pace by predicting user's stepping frequency using motion capture data and a deep learning model. By integrating (i) and (ii) in a task-space whole-body controller, we achieve full-body synchronization. We assess our humanoid-to-human whole-body synchronized motion model on the HRP-4 humanoid robot in forward, lateral and backward walks with concurrent upper limbs motions experiments.

关键词： Synchronization Robots Trajectory Legged locomotion humanoid robots Predictive models Estimation Telerobotics and teleoperation intention recognition human and humanoid motion analysis and synthesis

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