[HTML][HTML] HF-SPHR: Hybrid features for sustainable physical healthcare pattern recognition using deep belief networks
The daily life-log routines of elderly individuals are susceptible to numerous complications in
their physical healthcare patterns. Some of these complications can cause injuries, followed …
their physical healthcare patterns. Some of these complications can cause injuries, followed …
[HTML][HTML] Wrapper-based deep feature optimization for activity recognition in the wearable sensor networks of healthcare systems
Abstract The Human Activity Recognition (HAR) problem leverages pattern recognition to
classify physical human activities as they are captured by several sensor modalities. Remote …
classify physical human activities as they are captured by several sensor modalities. Remote …
A review of classification algorithms in machine learning for medical IOT.
R Prabha, S Deivanayagi… - International …, 2021 - search.ebscohost.com
Most of the works developed are now related to health issues because of the increase in
dangerous diseases and importance of every human life. This is also one of those models …
dangerous diseases and importance of every human life. This is also one of those models …
An artificial neural network framework for gait-based biometrics
As the popularity of wearable and the implantable body sensor network (BSN) devices
increases, there is a growing concern regarding the data security of such power-constrained …
increases, there is a growing concern regarding the data security of such power-constrained …
[HTML][HTML] Gait trajectory prediction on an embedded microcontroller using deep learning
Achieving a normal gait trajectory for an amputee's active prosthesis is challenging due to its
kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase …
kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase …
Wearable activity recognition for robust human-robot teaming in safety-critical environments via hybrid neural networks
In this work, we present a novel non-visual HAR system that achieves state-of-the-art
performance on realistic SCE tasks via a single wearable sensor. We leverage surface …
performance on realistic SCE tasks via a single wearable sensor. We leverage surface …
Foot2hip: A deep neural network model for predicting lower limb kinematics from foot measurements
Objective: This study aims to develop a neural network (foot2hip) for long-term recording of
gait kinematics with improved user comfort. Methods: Foot2hip predicts ankle, knee, and hip …
gait kinematics with improved user comfort. Methods: Foot2hip predicts ankle, knee, and hip …
Classifying the human activities of sensor data using deep neural network
HAA Al-Khamees, N Al-A'araji… - … Conference on Intelligent …, 2022 - Springer
Today sensors represent one of the most important applications for generating data stream.
This data has a number of unique characteristics, including fast data access, huge volume …
This data has a number of unique characteristics, including fast data access, huge volume …
Enhancing the stability of the deep neural network using a non-constant learning rate for data stream.
HAAA Al-Khamees, N Al-A'araji… - … Journal of Electrical …, 2023 - search.ebscohost.com
The data stream is considered the backbone of many real-world applications. These
applications are most effective when using modern techniques of machine learning like …
applications are most effective when using modern techniques of machine learning like …
[图书][B] Enabling Longitudinal Personalized Behavior Adaptation for Cognitively Assistive Robots
A Kubota - 2023 - search.proquest.com
Cognitively assistive robots have great potential to improve the accessibility of healthcare
services by extending existing clinical interventions to a person's home. This provides a …
services by extending existing clinical interventions to a person's home. This provides a …