AIMC Topic: Wrist

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Machine Learning Models for Classifying Physical Activity in Free-Living Preschool Children.

Sensors (Basel, Switzerland)
Machine learning (ML) activity classification models trained on laboratory-based activity trials exhibit low accuracy under free-living conditions. Training new models on free-living accelerometer data, reducing the number of prediction windows compr...

Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data.

European journal of sport science
This study examined a series of machine learning models, evaluating their effectiveness in assessing children's energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also de...

Characterizing forearm muscle activity in young adults during dynamic wrist flexion-extension movement using a wrist robot.

Journal of biomechanics
Current research suggests that the wrist extensor muscles function as the primary stabilizers of the wrist-joint complex. However, most investigations have utilized isometric study designs, with little consideration for wrist dynamics or changes in p...

Characterizing forearm muscle activity in university-aged males during dynamic radial-ulnar deviation of the wrist using a wrist robot.

Journal of biomechanics
Functioning as wrist stabilizers, the wrist extensor muscles exhibit higher levels of muscle activity than the flexors in most distal upper-limb tasks. However, this finding has been derived mostly from isometric or wrist flexion-extension protocols,...

Binary CorNET: Accelerator for HR Estimation From Wrist-PPG.

IEEE transactions on biomedical circuits and systems
Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly su...

A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors.

Sensors (Basel, Switzerland)
Here we propose a new machine learning algorithm for classification of human activities by means of accelerometer and gyroscope signals. Based on a novel hierarchical system of logistic regression classifiers and a relatively small set of features ex...

Application of deep learning to improve sleep scoring of wrist actigraphy.

Sleep medicine
BACKGROUND: Estimation of sleep parameters by wrist actigraphy is highly dependent on performance of the interpretative algorithm (IA) that converts movement data into sleep/wake scores.

Deep learning-based fully automatic segmentation of wrist cartilage in MR images.

NMR in biomedicine
The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensu...

Design of a tether-driven minimally invasive robotic surgical tool with decoupled degree-of-freedom wrist.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Dexterous surgical tool wrists used in tele-operated robotic surgery typically have mechanically coupled pitch and yaw degree of freedom (DoF). This leads to complex control requirements.

Identification of the best strategy to command variable stiffness using electromyographic signals.

Journal of neural engineering
OBJECTIVE: In the last decades, many EMG-controlled robotic devices were developed. Since stiffness control may be required to perform skillful interactions, different groups developed devices whose stiffness is real-time controlled based on EMG sign...