ArticleSensors (Basel, Switzerland)2021
Human Activity Recognition: A Comparative Study to Assess the Contribution Level of Accelerometer, ECG, and PPG Signals.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed, 33 citations in OpenAlex.
- Experimental Analysis on the Effect of Contact Pressure and Activity Level as Influencing Factors in PPG Sensor Performance.Sensors (Basel, Switzerland) · 2025Trial
- Research on Two-Stream Networks Integrating Physiological Features and Attention Mechanisms for Motion Classification in Visually Impaired Individuals.Sensors (Basel, Switzerland) · 2026Article
- Emotion Recognition Using PPG Signals of Smartwatch on Purpose of Threat Detection.Sensors (Basel, Switzerland) · 2024Article
- Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition.Sensors (Basel, Switzerland) · 2024Article
- Human Activity Recognition Algorithm with Physiological and Inertial Signals Fusion: Photoplethysmography, Electrodermal Activity, and Accelerometry.Sensors (Basel, Switzerland) · 2024Article
- Exploring the Possibility of Photoplethysmography-Based Human Activity Recognition Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- Deep learning framework for sensor array precision and accuracy enhancement.Scientific reports · 2023Article
- Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.Digital healthArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Inertial sensors are widely used in the field of human activity recognition (HAR), since this source of information is the most informative time series among non-visual datasets. HAR researchers are actively exploring other approaches and different sources of signals to improve the performance of HAR systems. In this study, we investigate the impact of combining bio-signals with a dataset acquired from inertial sensors on recognizing human daily activities. To achieve this aim, we used the PPG-DaLiA dataset consisting of 3D-accelerometer (3D-ACC), electrocardiogram (ECG), photoplethysmogram (PPG) signals acquired from 15 individuals while performing daily activities. We extracted hand-crafted time and frequency domain features, then, we applied a correlation-based feature selection approach to reduce the feature-set dimensionality. After introducing early fusion scenarios, we trained and tested random forest models with subject-dependent and subject-independent setups. Our results indicate that combining features extracted from the 3D-ACC signal with the ECG signal improves the classifier's performance F1-scores by 2.72% and 3.00% (from 94.07% to 96.80%, and 83.16% to 86.17%) for subject-dependent and subject-independent approaches, respectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.