.png){kind=icon}
返回
Resource-efficient, sensor-based human activity recognition with lightweight deep models boosted with attention
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2024.109274.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With their automatic feature extraction capabilities, deep learning models have become more widespread in sensor-based human activity recognition, particularly on larger datasets. However, their direct use on mobile and wearable devices is challenging due to the extensive resource requirements. Concurrently, attention-based models are emerging to improve recognition performance by dynamically emphasizing relevant parts of features and disregarding the irrelevant ones, particularly in the computer vision domain. This study introduces a novel application of attention mechanisms to smaller deep architectures, investigating whether smaller models can achieve comparable recognition performance to larger models in sensor-based human activity recognition systems while keeping resource usage at lower levels. For this purpose, we integrate the convolutional block attention module into a hybrid model, deep convolutional and long short-term memory network. Experiments are conducted using five public datasets in three model sizes: lightweight, moderate and original. The results show that applying attention to the lightweight model enables achieving similar recognition performances to the moderate-size model, and the lightweight model requires approximately 2-13 times fewer parameters and 3.5 times fewer flops. We also conduct experiments with sensor data at lower sampling rates and from fewer sensors attached to different body parts. The results show that attention improves recognition performance under lower sampling rates, as well as under higher sampling rates when model sizes are smaller, and mitigates the impact of missing data from one or more body parts, making the model more suitable for real-world sensor-based applications.
Keyword:
Attention mechanism
Convolutional neural networks
Human activity recognition
Hybrid deep models
Motion sensors
Resource consumption
期刊
C
IF:
4.9
论文数:
6.7K
被引数:
1.3W
机构
引用论文
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge DevicesAHAR: 自适应CNN，用于低功耗边缘设备中的节能人体活动识别
OAAI
OAAI分享收藏On the Use of a Convolutional Block Attention Module in Deep Learning-Based Human Activity Recognition with Motion Sensors.png)
DIAGNOSTICS
OAAI
DIAGNOSTICSIF3.3
OAAI分享收藏Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges人体传感器网络中的多传感器融合: 最新技术和研究挑战INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challengesINFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
分享收藏A Novel Deep Multifeature Extraction Framework Based on Attention Mechanism Using Wearable Sensor Data for Human Activity RecognitionIEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏
查看更多内容