ArticleSensors (Basel, Switzerland)2020
Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- A machine learning study highlighting the challenges of fidgety movement recognition using vision and inertial sensors.Scientific reports · 2026Article
- A comprehensive survey and comparative analysis of time series data augmentation in medical wearable computing.PloS one · 2025Article
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- Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.Sensors (Basel, Switzerland) · 2024Review
- A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation.Sensors (Basel, Switzerland) · 2023Article
- Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data.Sensors (Basel, Switzerland) · 2023Article
- Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Transfer Learning on Small Datasets for Improved Fall Detection.Sensors (Basel, Switzerland) · 2023Article
- Personalised Gait Recognition for People with Neurological Conditions.Sensors (Basel, Switzerland) · 2022Article
- A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars.Sensors (Basel, Switzerland) · 2022Article
- Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks.Sensors (Basel, Switzerland) · 2021Article
- Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series.Sensors (Basel, Switzerland) · 2021Article
- The Symphony of Team Flow in Virtual Teams. Using Artificial Intelligence for Its Recognition and Promotion.Frontiers in psychology · 2021Article
- 3D Motion Capture May Detect Spatiotemporal Changes in Pre-Reaching Upper Extremity Movements with and without a Real-Time Constraint Condition in Infants with Perinatal Stroke and Cerebral Palsy: A Longitudinal Case Series.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems-Human Activity Recognition (HAR) and Emotion Recognition (ER)-and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.
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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.