Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors

Abstract

Unlike images or videos data which can be easily labeled by human being, sensor data annotation is a time-consuming process. However, traditional methods of human activity recognition require a large amount of such strictly labeled data for training classifiers. In this paper, we present an attention-based convolutional neural network for human recognition from weakly labeled data. The proposed attention model can focus on labeled activity among a long sequence of sensor data, and while filter out a large amount of background noise signals. In experiment on the weakly labeled dataset, we show that our attention model outperforms classical deep learning methods in accuracy. Besides, we determine the specific locations of the labeled activity in a long sequence of weakly labeled data by converting the compatibility score which is generated from attention model to compatibility density. Our method greatly facilitates the process of sensor data annotation, and makes data collection more easy.

6 pages, 5 figures

References in corpus (5)

• Neural Machine Translation by Jointly Learning to Align and Translate
• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• Recurrent Models of Visual Attention
• Learn To Pay Attention
• Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art

Cited by in corpus (10)

• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey
• Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects
• Deep ConvLSTM with self-attention for human activity decoding using wearables
• Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
• Sequential Weakly Labeled Multi-Activity Localization and Recognition on Wearable Sensors using Recurrent Attention Networks
• Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
• DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors
• A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors
• Octave Mix: Data augmentation using frequency decomposition for activity recognition