AIMC Topic: Acceleration

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Internal sensory models allow for balance control using muscle spindle acceleration feedback.

Neural networks : the official journal of the International Neural Network Society
Motor control requires sensory feedback, and the nature of this feedback has implications for the tasks of the central nervous system (CNS): for an approximately linear mechanical system (e.g., a freely standing person, a rider on a bicycle), if the ...

Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques.

Journal of sports science & medicine
This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a ...

Effects of maximum thickness position on hydrodynamic performance for fish-like swimmers.

Bioinspiration & biomimetics
When designing the internals of robotic fish, variations in the internal arrangements of power and control systems cause differences in external morphological structures, particularly the positions of maximum thickness. These differences considerably...

Explaining deep learning models for age-related gait classification based on acceleration time series.

Computers in biology and medicine
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...

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis.

Journal of sports science & medicine
Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players fro...

Analysis of harsh braking and harsh acceleration occurrence via explainable imbalanced machine learning using high-resolution smartphone telematics and traffic data.

Accident; analysis and prevention
Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not bee...

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.

Research quarterly for exercise and sport
To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice ses...

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation.

PloS one
This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate da...

Accurate fall risk classification in elderly using one gait cycle data and machine learning.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...

Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals.

Sensors (Basel, Switzerland)
Currently, surface EMG signals have a wide range of applications in human-computer interaction systems. However, selecting features for gesture recognition models based on traditional machine learning can be challenging and may not yield satisfactory...