Results 51 to 60 of about 2,707 (200)
Integrating multimodal data and machine learning for entrepreneurship research
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley +1 more source
Machine learning approaches in automated infant General Movements Assessment: A scoping review
Automated infant General Movements Assessment increasingly uses machine‐ and deep‐learning approaches to classify movement patterns and estimate cerebral palsy risk from video or sensor data. This scoping review highlights how dataset characteristics, recording environment, pose‐estimation accuracy, feature extraction, and model design influence system
Manpreet Kaur +4 more
wiley +1 more source
Butterworth filter in output from OpenPose.
Butterworth filter with 1 Hz, 2 Hz and 3 Hz cut off frequencies (c/o freq.) applied to the OpenPose signal of the thumb interphalangeal joint angle for one representative participant.
Letizia Gionfrida (14053664) +3 more
core +1 more source
Abstract Adopting physical expressions of emotion has feedback effects on individuals' mood and behaviour; for example, adopting the expansive and contractive body language of dominance and submission can affect individuals' feelings of power. However, postural feedback paradigms almost exclusively use deliberate, active posture manipulations, which ...
Soren Wainio‐Theberge, Jorge Armony
wiley +1 more source
Validation of two-dimensional video-based inference of finger kinematics with pose estimation.
Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant's hands.
Letizia Gionfrida +3 more
doaj +1 more source
Human action recognition (HAR) is one of the most active research topics in the field of computer vision. Even though this area is well-researched, HAR algorithms such as 3D Convolution Neural Networks (CNN), Two-stream Networks, and CNN-LSTM (Long Short-
Najeeb ur Rehman Malik +3 more
doaj +1 more source
OpenPose based Smoking Gesture Recognition System using Artificial Neural Network
Smoking is an extremely important health problem in modern society. This study focuses on a method for preventing smoking in non-smoking areas, such as public places, as well as the development of an artificial neural network based smoking motion ...
Tae-Yeong Jeong, Il-Kyu Ha
doaj +1 more source
Intelligent Performance Evaluation in Rowing Sport Using a Graph-Matching Network
Rowing competitions require consistent rowing strokes among crew members to achieve optimal performance. However, existing motion analysis techniques often rely on wearable sensors, leading to challenges in sporter inconvenience.
Chien-Chang Chen +5 more
doaj +1 more source
Overview of fall‐detection approaches and technologies in geriatric care. ABSTRACT This systematic review provides a critical examination of fall detection (FD) technologies in geriatric care, analyzing both technological innovations and implementation challenges across diverse healthcare environments.
Hamid Ali +6 more
wiley +1 more source
A multi-modal detection method for holding ladders in underground climbing operations
Currently, most research on recognizing unsafe behaviors of underground personnel focuses on improving precision through computer vision. However, underground areas are prone to occlusion, unstable lighting, and reflection, making it difficult to ...
SUN Qing, YANG Chaoyu
doaj +1 more source

