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rTsfNet: A DNN Model with Multi-head 3D Rotation and Time Series Feature Extraction for IMU-based Human Activity Recognition

https://doi.org/10.1145/3699733
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-13105-4_14openalex-snapshot
mHealthDroid: A Novel Framework for Agile Development of Mobile Health Applications
inferredcites →https://doi.org/10.1007/978-3-319-26561-2_6openalex-snapshot
Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors
inferredcites →https://doi.org/10.1007/s00779-023-01776-3openalex-snapshot
Transformer-based models to deal with heterogeneous environments in Human Activity Recognition
inferredcites →https://doi.org/10.1007/s10618-013-0312-3openalex-snapshot
CID: an efficient complexity-invariant distance for time series
inferredcites →https://doi.org/10.1007/s11042-021-11885-xopenalex-snapshot
Inception inspired CNN-GRU hybrid network for human activity recognition
inferredcites →https://doi.org/10.1016/j.asoc.2017.09.027openalex-snapshot
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
inferredcites →https://doi.org/10.1016/j.asoc.2021.107671openalex-snapshot
Attention induced multi-head convolutional neural network for human activity recognition
inferredcites →https://doi.org/10.1016/j.eswa.2016.04.032openalex-snapshot
Human activity recognition with smartphone sensors using deep learning neural networks
inferredcites →https://doi.org/10.1016/j.measurement.2022.111445openalex-snapshot
Human activity recognition in IoHT applications using Arithmetic Optimization Algorithm and deep learning
inferredcites →https://doi.org/10.1016/j.softx.2020.100548openalex-snapshot
Mcfly: Automated deep learning on time series
inferredcites →https://doi.org/10.1109/4235.996017openalex-snapshot
A fast and elitist multiobjective genetic algorithm: NSGA-II
inferredcites →https://doi.org/10.1109/access.2018.2890675openalex-snapshot
InnoHAR: A Deep Neural Network for Complex Human Activity Recognition
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.2022.3185112openalex-snapshot
A Multichannel CNN-GRU Model for Human Activity Recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/icaiic48513.2020.9065078openalex-snapshot
A CNN-LSTM Approach to Human Activity Recognition
inferredcites →https://doi.org/10.1109/icaiic48513.2020.9065250openalex-snapshot
Utilization of Postural Transitions in Sensor-based Human Activity Recognition
inferredcites →https://doi.org/10.1109/icnc.2014.6975918openalex-snapshot
Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models
inferredcites →https://doi.org/10.1109/inss.2010.5573462openalex-snapshot
Collecting complex activity datasets in highly rich networked sensor environments
inferredcites →https://doi.org/10.1109/iswc.2012.13openalex-snapshot
Introducing a New Benchmarked Dataset for Activity Monitoring
inferredcites →https://doi.org/10.1109/jiot.2019.2920283openalex-snapshot
IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment
inferredcites →https://doi.org/10.1109/jsen.2018.2885796openalex-snapshot
Weakly Supervised Human Activity Recognition From Wearable Sensors by Recurrent Attention Learning
inferredcites →https://doi.org/10.1109/jsen.2019.2917225openalex-snapshot
Attention-Based Convolutional Neural Network for Weakly Labeled Human Activities’ Recognition With Wearable Sensors
inferredcites →https://doi.org/10.1109/jsen.2019.2946095openalex-snapshot
Bi-LSTM Network for Multimodal Continuous Human Activity Recognition and Fall Detection
inferredcites →https://doi.org/10.1109/jsen.2019.2956901openalex-snapshot
Human Action Recognition Using Deep Learning Methods on Limited Sensory Data
inferredcites →https://doi.org/10.1109/percom.2016.7456521openalex-snapshot
On-body localization of wearable devices: An investigation of position-aware activity recognition
inferredcites →https://doi.org/10.1109/smc.2015.528openalex-snapshot
Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer
inferredcites →https://doi.org/10.1109/ssci.2015.199openalex-snapshot
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition
inferredcites →https://doi.org/10.1109/titb.2009.2036165openalex-snapshot
Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom
inferredcites →https://doi.org/10.1145/1964897.1964918openalex-snapshot
Activity recognition using cell phone accelerometers
inferredcites →https://doi.org/10.1145/2733373.2806333openalex-snapshot
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1145/2809695.2809718openalex-snapshot
Smart Devices are Different
inferredcites →https://doi.org/10.1145/3195258.3195260openalex-snapshot
Protecting Sensory Data against Sensitive Inferences
inferredcites →https://doi.org/10.1145/3550285openalex-snapshot
Augmented Adversarial Learning for Human Activity Recognition with Partial Sensor Sets
inferredcites →https://doi.org/10.1145/3550299openalex-snapshot
Assessing the State of Self-Supervised Human Activity Recognition Using Wearables
inferredcites →https://doi.org/10.1145/3550319openalex-snapshot
Predicting Performance Improvement of Human Activity Recognition Model by Additional Data Collection
inferredcites →https://doi.org/10.1145/3594739.3610736openalex-snapshot
Where Are the Best Positions of IMU Sensors for HAR? - Approach by a Garment Device with Fine-Grained Grid IMUs -
inferredcites →https://doi.org/10.1155/2018/7316954openalex-snapshot
Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors
inferredcites →https://doi.org/10.1177/1550147716683687openalex-snapshot
Recognizing human activities from smartphone sensors using hierarchical continuous hidden Markov models
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.3390/s16010115openalex-snapshot
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-31236-5_49openalex-snapshot
A Frequency–Time Dual-Domain Fusion Algorithm for Multi-IMU Human Motion Recognition
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2026.111561openalex-snapshot
Achieving more with less: An ultra-lightweight dual-branch multi-level depthwise separable convolution for sensor-based activity recognition
inferred← citeshttps://doi.org/10.1016/j.displa.2026.103415openalex-snapshot
Towards high-dimensional IMU-based human activity recognition: data construction via 3D body modeling and classification with a multi-channel attention fusion network
inferred← citeshttps://doi.org/10.1016/j.ins.2026.123862openalex-snapshot
TVCA-IF: A temporal-variable cross-attention interactive fusion network for multidimensional sensor-based human activity recognition
inferred← citeshttps://doi.org/10.1109/chase69719.2026.00011openalex-snapshot
From Detection to Forecasting: An Empirical Study of College Student Mental Health Using Smartphone Sensing
inferred← citeshttps://doi.org/10.1109/comcomap68359.2025.11353183openalex-snapshot
Detection of Body Focused Repetitive Behaviors using Deep Learning
inferred← citeshttps://doi.org/10.1109/icassp55912.2026.11464885openalex-snapshot
Activity Recognition Using Inaudible Acoustic FMCW
inferred← citeshttps://doi.org/10.1109/icdsis65355.2025.11071220openalex-snapshot
Multi-Scale Multi-Headed Attention Framework for Wearable IMU-based Exercise Recognition
inferred← citeshttps://doi.org/10.1145/3675094.3678449openalex-snapshot
DNN Model Comparison for Sensor Location Robustness
inferred← citeshttps://doi.org/10.1145/3714394.3756334openalex-snapshot
Where Are the Best Positions of IMUs for HAR? - Investigation with Four DNN Models of Different Characteristics
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