Applying Multiple Knowledge to Sussex-Huawei Locomotion Challenge
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Summary
The JSI-Deep team utilized an AR approach based on combining multiple machine-learning methods following the principle of multiple knowledge, which achieved 96% accuracy, which is a significant leap over the baseline 60%.
- Type
- article
- Published
- 2018-10-08
- Cited by
- 23
- References
- 17
- OpenAlex
- https://openalex.org/W2899274674
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53223102
Keywords
Benchmark (surveying), Computer science, Artificial intelligence, Machine learning, Ensemble learning
References
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- Using mobile phones to determine transportation modes
- Networked
- Accelerometer Based Transportation Mode Recognition on Mobile Phones
- A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices
- e-Gibalec: Mobile application to monitor and encourage physical activity in schoolchildren
- Chiron: translating nanopore raw signal directly into nucleotide sequence using deep learning
- Real-time physical activity and mental stress management with a wristband and a smartphone
- The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics With Mobile Devices
- Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge
- A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge
- Et al
- Human activity recognition with smartphone sensors using deep learning neural networks
- Expert Systems With Applications
- Efficient Activity Recognition and Fall Detection Using Accelerometers
- Comparing Deep and Classical Machine Learning Methods for Human Activity Recognition using Wrist Accelerometer
- I and J
- Collecting complex activity datasets in highly rich networked sensor environments
Cited by
- Summary of the Sussex-Huawei Locomotion-Transportation Recognition Challenge
- Application of IndRNN for human activity recognition: the Sussex-Huawei locomotion-transportation challenge
- Transportation Mode Recognition Fusing Wearable Motion, Sound, and Vision Sensors
- Classical and deep learning methods for recognizing human activities and modes of transportation with smartphone sensors
- Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients
- IndRNN based long-term temporal recognition in the spatial and frequency domain
- Transition-Aware Detection of Modes of Locomotion and Transportation Through Hierarchical Segmentation
- A General Framework for Making Context-Recognition Systems More Energy Efficient
- Sequence Metric Learning as Synchronization of Recurrent Neural Networks
- NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition
- Recognition of human locomotion on various transportations fusing smartphone sensors
- Three-Year Review of the 2018–2020 SHL Challenge on Transportation and Locomotion Mode Recognition From Mobile Sensors
- EmbraceNet for activity: a deep multimodal fusion architecture for activity recognition
- Transportation Mode Detection Combining CNN and Vision Transformer with Sensors Recalibration Using Smartphone Built-In Sensors
- Distributional and spatial-temporal robust representation learning for transportation activity recognition
- Enhancing Locomotion Recognition with Specialized Features and Map Information via XGBoost
- Transportation Mode Recognition Based on Low-Rate Acceleration and Location Signals With an Attention-Based Multiple-Instance Learning Network
- SensorNet: An Adaptive Attention Convolutional Neural Network for Sensor Feature Learning
- Communication Scene Recognition Method Based on Multi Phone Sensors and Deep Learning
- A communication scene recognition framework based on deep learning with multi-sensor fusion
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