Classic fault detection and classification has some classic problems. It’s reactive, time-consuming to set up, and any ...

Wearable sensor data combined with machine learning predicts fall risk in Parkinson's patients, enhancing preventive care and clinical outcomes over five years.

Overview This project provides the implementation for the paper, Federated Learning for Hierarchical Fall Detection and Human Activity Recognition, which presents a federated learning framework for ...

Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over ...

Background: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable ...

ALERT is an innovative machine learning initiative focused on advancing fall detection capabilities within smart wearable technologies.Its primary mission is to develop a highly accurate, real-time ...

We investigated different machine learning models for the near-fall detection including support vector machines, AdaBoost, convolutional neural networks, and bidirectional long short-term memory ...

This is particularly illustrated by a lack of discussion on machine learning choices and issues one must face when developing a time series classifier for fall detection. Second, we also would like to ...