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Generalization in wearable-based human activity recognition: An explainable cross-dataset study of classical and deep learning models
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Histogram-Based Algorithm for Building Gradient Boosting Ensembles of Piecewise Linear Decision Trees
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Human Activity Recognition Using K-Nearest Neighbor Machine Learning Algorithm
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Preprocessing techniques for context recognition from accelerometer data
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Implications of the curse of dimensionality for supervised learning classifier systems: theoretical and empirical analyses
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A novel deep learning architecture and MINIROCKET feature extraction method for human activity recognition using ECG, PPG and inertial sensor dataset
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ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
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The no-free-lunch theorems of supervised learning
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Privacy-preserving human activity sensing: A survey
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Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors
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A comprehensive review on ensemble deep learning: Opportunities and challenges
inferredcites →https://doi.org/10.1016/j.neucom.2022.09.099openalex-snapshot
Two-stream transformer network for sensor-based human activity recognition
inferredcites →https://doi.org/10.1016/j.neucom.2025.131337openalex-snapshot
A comprehensive benchmark of machine and deep learning models on structured data for regression and classification
inferredcites →https://doi.org/10.1016/j.patrec.2021.02.024openalex-snapshot
KU-HAR: An open dataset for heterogeneous human activity recognition
inferredcites →https://doi.org/10.1016/j.pmcj.2024.101976openalex-snapshot
Energy-aware human activity recognition for wearable devices: A comprehensive review
inferredcites →https://doi.org/10.1038/s41597-024-03951-4openalex-snapshot
A benchmark for domain adaptation and generalization in smartphone-based human activity recognition
inferredcites →https://doi.org/10.1038/s42256-019-0048-xopenalex-snapshot
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
inferredcites →https://doi.org/10.1109/4235.585893openalex-snapshot
No free lunch theorems for optimization
inferredcites →https://doi.org/10.1109/access.2020.2982225openalex-snapshot
LSTM-CNN Architecture for Human Activity Recognition
inferredcites →https://doi.org/10.1109/access.2021.3078184openalex-snapshot
iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition
inferredcites →https://doi.org/10.1109/access.2022.3174124openalex-snapshot
ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors
inferredcites →https://doi.org/10.1109/access.2022.3185112openalex-snapshot
A Multichannel CNN-GRU Model for Human Activity Recognition
inferredcites →https://doi.org/10.1109/access.2023.3310269openalex-snapshot
Attention-Based Residual BiLSTM Networks for Human Activity Recognition
inferredcites →https://doi.org/10.1109/access.2024.3416838openalex-snapshot
A Survey of Decision Trees: Concepts, Algorithms, and Applications
inferredcites →https://doi.org/10.1109/icoias49312.2020.9081858openalex-snapshot
Performance Comparisson Activity Recognition using Logistic Regression and Support Vector Machine
inferredcites →https://doi.org/10.1109/jbhi.2012.2234129openalex-snapshot
A Survey on Ambient-Assisted Living Tools for Older Adults
inferredcites →https://doi.org/10.1109/jsen.2020.3015781openalex-snapshot
A Novel Multi-Stage Training Approach for Human Activity Recognition From Multimodal Wearable Sensor Data Using Deep Neural Network
inferredcites →https://doi.org/10.1109/jtehm.2022.3177710openalex-snapshot
Deep CNN-LSTM With Self-Attention Model for Human Activity Recognition Using Wearable Sensor
inferredcites →https://doi.org/10.1109/tce.2023.3266506openalex-snapshot
A Human Activity Recognition Method Based on Lightweight Feature Extraction Combined With Pruned and Quantized CNN for Wearable Device
inferredcites →https://doi.org/10.1109/tit.1968.1054102openalex-snapshot
On the mean accuracy of statistical pattern recognizers
inferredcites →https://doi.org/10.1111/insr.12016openalex-snapshot
Fifty Years of Classification and Regression Trees
inferredcites →https://doi.org/10.1145/1964897.1964918openalex-snapshot
Activity recognition using cell phone accelerometers
inferredcites →https://doi.org/10.1145/3542819openalex-snapshot
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-snapshot
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.3233/aic-170729openalex-snapshot
Linear discriminant analysis: A detailed tutorial
inferredcites →https://doi.org/10.3390/app132312728openalex-snapshot
A Comparative Study on Recent Progress of Machine Learning-Based Human Activity Recognition with Radar
inferredcites →https://doi.org/10.3390/app7101101openalex-snapshot
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
inferredcites →https://doi.org/10.3390/make6020040openalex-snapshot
A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition
inferredcites →https://doi.org/10.3390/make6020065openalex-snapshot
Cross-Validation Visualized: A Narrative Guide to Advanced Methods
inferredcites →https://doi.org/10.3390/s19030521openalex-snapshot
A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices
inferredcites →https://doi.org/10.3390/s19143213openalex-snapshot
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview
inferredcites →https://doi.org/10.3390/s20215993openalex-snapshot
Optimization of IMU Sensor Placement for the Measurement of Lower Limb Joint Kinematics
inferredcites →https://doi.org/10.3390/s21030768openalex-snapshot
Activity Recognition for Ambient Assisted Living with Videos, Inertial Units and Ambient Sensors
inferredcites →https://doi.org/10.3390/s21144669openalex-snapshot
Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification
inferredcites →https://doi.org/10.3390/s21237853openalex-snapshot
HARTH: A Human Activity Recognition Dataset for Machine Learning
inferredcites →https://doi.org/10.3390/s23052368openalex-snapshot
Validation of an Activity Type Recognition Model Classifying Daily Physical Behavior in Older Adults: The HAR70+ Model
inferredcites →https://doi.org/10.3390/s23146511openalex-snapshot
Exploring Regularization Methods for Domain Generalization in Accelerometer-Based Human Activity Recognition
inferredcites →https://doi.org/10.52202/068431-0037openalex-snapshot
Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?
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