ArticleSensors (Basel, Switzerland)2022
Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- A systematic review of prolonged posture monitoring with accelerometer-based wearable sensors.PLOS digital health · 2026Article
- State-Dependent CNN-GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Near-Field Radio-Frequency for Real-Time Facial Expression Recognition.IEEE access : practical innovations, open solutions · 2026Article
- PUNet: a lightweight parallel U-Net architecture integrating Mamba-CNN for high-precision image segmentation.Scientific reports · 2025Article
- An anthropomorphic diagnosis system of pulmonary nodules using weak annotation-based deep learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Exploring Regularization Methods for Domain Generalization in Accelerometer-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.Sensors (Basel, Switzerland) · 2023Article
- Sensor-Based Human Activity and Behavior Research: Where Advanced Sensing and Recognition Technologies Meet.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.
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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.