ArticleSensors (Basel, Switzerland)2019
A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Pedestrian navigation activity recognition method based on two-stream transformer and contrastive learning.iScience · 2026Article
- Deep Learning Algorithms for Human Activity Recognition in Manual Material Handling Tasks.Sensors (Basel, Switzerland) · 2025Article
- Effects of Sampling Frequency on Human Activity Recognition with Machine Learning Aiming at Clinical Applications.Sensors (Basel, Switzerland) · 2025Article
- Applying MLP-Mixer and gMLP to Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- A two-phase hybrid clustering framework exploring transitional activities in HAR.Discover artificial intelligence · 2025Article
- Robust Feature Representation Using Multi-Task Learning for Human Activity Recognition.Sensors (Basel, Switzerland) · 2024Article
- On enabling collaborative non-intrusive load monitoring for sustainable smart cities.Scientific reports · 2023Article
- Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances.Sensors (Basel, Switzerland) · 2022Review
- Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.Sensors (Basel, Switzerland) · 2021Article
- Unsupervised Learning for Product Use Activity Recognition: An Exploratory Study of a "Chatty Device".Sensors (Basel, Switzerland) · 2021Article
- Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor.Sensors (Basel, Switzerland) · 2021Article
- Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
- Mechanical Fault Diagnosis of a High Voltage Circuit Breaker Based on High-Efficiency Time-Domain Feature Extraction with Entropy Features.Entropy (Basel, Switzerland) · 2020Article
- A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory.Sensors (Basel, Switzerland) · 2020Article
- Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living.International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE.
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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.