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Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
Kaixuan Chen, Dalin Zhang, Lina Yao +3
The vast proliferation of sensor devices and Internet of Things enables the applications of sensor-based activity recognition. However, there exist substantial challenges that coul…
Multi-agent Attentional Activity Recognition
Kaixuan Chen, Lina Yao, Dalin Zhang +2
Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally va…
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Xiang Zhang, Lina Yao, Xianzhi Wang +3
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.…
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Kaixuan Chen, Lina Yao, Dalin Zhang +3
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-sup…
Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding
Xiang Zhang, Lina Yao, Chaoran Huang +3
Electroencephalography (EEG) signals are known to manifest differential patterns when individuals visually concentrate on different objects. In this work, we present an end-to-end…
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Kaixuan Chen, Lina Yao, Xianzhi Wang +4
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the eff…
Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity
Xiang Zhang, Lina Yao, Shuai Zhang +3
A Brain-Computer Interface (BCI) acquires brain signals, analyzes and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the…
Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks
Xiang Zhang, Lina Yao, Xianzhi Wang +3
Electroencephalography (EEG) signals reflect activities on certain brain areas. Effective classification of time-varying EEG signals is still challenging. First, EEG signal process…
Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition
Kaixuan Chen, Lina Yao, Tao Gu +3
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the eff…
MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network
Xiang Zhang, Lina Yao, Salil S. Kanhere +3
Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person id…
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
Xiang Zhang, Lina Yao, Dalin Zhang +3
An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance co…
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
Xiang Zhang, Lina Yao, Quan Z. Sheng +3
An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interac…
Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface
Dalin Zhang, Lina Yao, Xiang Zhang +3
Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based…