AIMC Topic: Walking
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Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults.
Sensors (Basel, Switzerland)
Nov 29, 2024
UNLABELLED: Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in...
Motion Planning and Control with Environmental Uncertainties for Humanoid Robot.
Sensors (Basel, Switzerland)
Nov 29, 2024
Humanoid robots are typically designed for static environments, but real-world applications demand robust performance under dynamic, uncertain conditions. This paper introduces a perceptive motion planning and control algorithm that enables humanoid ...
Predicting executive functioning from walking features in Parkinson's disease using machine learning.
Scientific reports
Nov 27, 2024
Parkinson's disease is characterized by motor and cognitive deficits. While previous work suggests a relationship between both, direct empirical evidence is scarce or inconclusive. Therefore, we examined the relationship between walking features and ...
Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach.
Accident; analysis and prevention
Nov 19, 2024
Jaywalking, as a hazardous crossing behavior, leaves little time for drivers to anticipate and respond promptly, resulting in high crossing risks. The prevalence of Autonomous Vehicle (AV) technologies has offered new solutions for mitigating jaywalk...
Analysis of impact of limb segment length variations during reinforcement learning in four-legged robot.
Scientific reports
Nov 14, 2024
Crawling robots are becoming increasingly prevalent in both industrial and private applications. Despite their many advantages over other robot types, they have complex movement mechanics. Artificial intelligence can simplify this by reinforcement le...
Task-agnostic exoskeleton control via biological joint moment estimation.
Nature
Nov 13, 2024
Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We i...
Explaining deep learning models for age-related gait classification based on acceleration time series.
Computers in biology and medicine
Nov 12, 2024
BACKGROUND: Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, no...
Predicting Continuous Locomotion Modes via Multidimensional Feature Learning From sEMG.
IEEE journal of biomedical and health informatics
Nov 6, 2024
Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving ...
Lower Limb Torque Prediction for Sit-To-Walk Strategies Using Long Short-Term Memory Neural Networks.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Nov 6, 2024
Joint torque prediction is crucial when investigating biomechanics, evaluating treatments, and designing powered assistive devices. Controllers in assistive technology require reference torque trajectories to set the level of assistance for a patient...
Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model.
Sensors (Basel, Switzerland)
Nov 4, 2024
To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM...
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