ArticleSensors (Basel, Switzerland)2021
Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.
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 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Comparison of Machine Learning Approaches to Activity Classification Using IMU Data Collected in a Community-Based Setting.Sensors (Basel, Switzerland) · 2026Article
- Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach.Sensors (Basel, Switzerland) · 2025Article
- Quantifying Mild Cognitive Impairments in Older Adults Using Multi-modal Wearable Sensor Data in a Kitchen Environment.medRxiv : the preprint server for health sciences · 2025Article
- A Comprehensive Dataset for Activity of Daily Living (ADL) Research Compiled by Unifying and Processing Multiple Data Sources.Journal of personalized medicine · 2025Article
- Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.Sensors (Basel, Switzerland) · 2024Article
- A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.Sensors (Basel, Switzerland) · 2024Article
- Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.Sensors (Basel, Switzerland) · 2023Article
- Article
- A Study on the Influence of Sensors in Frequency and Time Domains on Context Recognition.Sensors (Basel, Switzerland) · 2023Article
- Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.Sensors (Basel, Switzerland) · 2023Article
- Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.Sensors (Basel, Switzerland) · 2023Article
- Human Motion Pattern Recognition and Feature Extraction: An Approach Using Multi-Information Fusion.Micromachines · 2022Article
- Machine Learning Strategies for Low-Cost Insole-Based Prediction of Center of Gravity during Gait in Healthy Males.Sensors (Basel, Switzerland) · 2022Article
- SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies.Sensors (Basel, Switzerland) · 2022Article
- Human activity recognition in artificial intelligence framework: a narrative review.Artificial intelligence review · 2022Article
- Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.
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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.