2024年7月15日月曜日
A Robust Deep Feature Extraction Method for #Human_Activity_Recognition Using a #Wavelet Based Spectral Visualisation Technique
A Robust Deep Feature Extraction Method for Human Activity Recognition
Using a Wavelet Based Spectral Visualisation Technique
by Nadeem Ahmed 1ORCID,
Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1216, Bangladesh
Department of Computer Science and Engineering, #University_of_Aizu, Aizu-Wakamatsu 965-8580, Japan
/ Published: 4 July 2024
Abstract
Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL),
are integral components of smart homes, sports, surveillance,
and investigation activities. To recognize daily activities, researchers are focusing
on lightweight, cost-effective, wearable sensor-based technologies
as traditional vision-based technologies lack elderly privacy,
a fundamental right of every human. However,
it is challenging to extract potential features from 1D multi-sensor data.
Thus, this research focuses on extracting distinguishable patterns and deep features
from spectral images by time-frequency-domain analysis of 1D multi-sensor data.
Wearable sensor data, particularly accelerator and gyroscope data,
act as input signals of different daily activities, and provide potential information
using time-frequency analysis.
This potential time series information is mapped into spectral images
through a process called use of ’#scalograms’, derived from the #continuous_wavelet_transform.
The #deep_activity_features are extracted from the activity image using deep learning models
such as CNN, MobileNetV3, ResNet, and GoogleNet
and subsequently classified using a conventional classifier.
To validate the proposed model, SisFall and PAMAP2 benchmark datasets are
Based on the experimental results, this proposed model shows the optimal performance
for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2,
using Morlet as the mother wavelet with ResNet-101 and a softmax classifier,
and outperforms state-of-the-art algorithms.
#Labtech_Holter #VitalStream_caretaker #Arteriograph_Tensiomed #Pedcath8_Mennen_Medical #Heart_Vest_gTec
#VitalStream_caretaker #Cardiac_Output #Artificial_Stiffness #Blood_Flow #Stroke_Volume #Care_during_after_Cardiovascular_Surgery #Labtech_Holter #Arteriograph_ArtificialStiffness #Pedcath8_MennenMedical #Cardionics_Stethoscope_SingalOutput #Nevrokard_AutonomicNerves #wavelet_algorithm
#メディカルテクニカ #Labtech_Holter #生体情報 #Heart_vest_gTec #Pedcath8 #Mennen_Medical #Vectorcardiography_Labtech_Holter #VitalStream_Caretaker_Medical #Pedcath8_Mennen_Medical #wvelet_algorithm #Piston_Medical_COanalysi
#西陣心電布 #VitalStream_type1_type2_type3 #VitalStream_血行動態 #Labtech_Holter_VectorECG, #Pedcath8_Mennen_Medical #VitalStream_Medical_Teknika #VitalStream_メディカルテクニカ
