AIMC Topic: Walking
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Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults.
PloS one
Feb 11, 2021
Previous studies have shown that functional mobility, along with other physical functions, decreases with advanced age. However, it is still unclear which domains of functioning (body structures, body functions, and activities) are most closely relat...
Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks.
Sensors (Basel, Switzerland)
Feb 5, 2021
Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person's state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and f...
Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning.
Scientific reports
Feb 4, 2021
Pain is an undesirable sensory experience that can induce depression and limit individuals' activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective an...
Robot-Aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 28, 2021
We sought to evaluate the effects of the application of torque pulses to the hip and knee joint via a robotic exoskeleton in the context of training propulsion during walking. Based on our previous study, we formulated a set of conditions of torque p...
Smartphone-based human fatigue level detection using machine learning approaches.
Ergonomics
Jan 20, 2021
Human muscle fatigue is the main result of diminishing muscle capability, leading to reduced performance and increased risk of falls and injury. This study provides a classification model to identify the human fatigue level based on the motion signal...
Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments.
Sensors (Basel, Switzerland)
Jan 19, 2021
GOAL: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model.
Estimation of kinematics from inertial measurement units using a combined deep learning and optimization framework.
Journal of biomechanics
Jan 8, 2021
The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in un...
The impact of errors in infant development: Falling like a baby.
Developmental science
Jan 4, 2021
What is the role of errors in infants' acquisition of basic skills such as walking, skills that require immense amounts of practice to become flexible and generative? Do infants change their behaviors based on negative feedback from errors, as sugges...
Fundamental understanding of millipede morphology and locomotion dynamics.
Bioinspiration & biomimetics
Dec 21, 2020
A detailed model for the locomotory mechanics used by millipedes is provided here through systematic experimentation on the animal and validation of observations through a biomimetic robotic platform. Millipedes possess a powerful gait that is necess...
Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone.
Sensors (Basel, Switzerland)
Dec 15, 2020
Activity recognition can provide useful information about an older individual's activity level and encourage older people to become more active to live longer in good health. This study aimed to develop an activity recognition algorithm for smartphon...
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