An Online Adaptive Sampling Rate Learning Framework for Sensor-Based Human Activity Recognition

2018 ◽  
pp. 278-281
Author(s):  
Zeyi Jin ◽  
Jingjing Cao ◽  
Jingtao Sun ◽  
Wenfeng Li ◽  
Qiang Wang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Adaptive Sampling ◽  
Sampling Rate ◽  
Human Activity Recognition ◽  
Learning Framework

scholarly journals Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1716 ◽  
Author(s):  
Seungeun Chung ◽  
Jiyoun Lim ◽  
Kyoung Ju Noh ◽  
Gague Kim ◽  
Hyuntae Jeong
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Sensor Fusion ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Sampling Rate ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Activity Data

In this paper, we perform a systematic study about the on-body sensor positioning and data acquisition details for Human Activity Recognition (HAR) systems. We build a testbed that consists of eight body-worn Inertial Measurement Units (IMU) sensors and an Android mobile device for activity data collection. We develop a Long Short-Term Memory (LSTM) network framework to support training of a deep learning model on human activity data, which is acquired in both real-world and controlled environments. From the experiment results, we identify that activity data with sampling rate as low as 10 Hz from four sensors at both sides of wrists, right ankle, and waist is sufficient in recognizing Activities of Daily Living (ADLs) including eating and driving activity. We adopt a two-level ensemble model to combine class-probabilities of multiple sensor modalities, and demonstrate that a classifier-level sensor fusion technique can improve the classification performance. By analyzing the accuracy of each sensor on different types of activity, we elaborate custom weights for multimodal sensor fusion that reflect the characteristic of individual activities.

scholarly journals Wi-Fi-Based Location-Independent Human Activity Recognition via Meta Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2654
Author(s):  
Xue Ding ◽  
Ting Jiang ◽  
Yi Zhong ◽  
Yan Huang ◽  
Zhiwei Li
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
Short Term Memory ◽  
Recognition Accuracy ◽  
Metric Learning ◽  
Sampling Rate ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Feature Representation

Wi-Fi-based device-free human activity recognition has recently become a vital underpinning for various emerging applications, ranging from the Internet of Things (IoT) to Human–Computer Interaction (HCI). Although this technology has been successfully demonstrated for location-dependent sensing, it relies on sufficient data samples for large-scale sensing, which is enormously labor-intensive and time-consuming. However, in real-world applications, location-independent sensing is crucial and indispensable. Therefore, how to alleviate adverse effects on recognition accuracy caused by location variations with the limited dataset is still an open question. To address this concern, we present a location-independent human activity recognition system based on Wi-Fi named WiLiMetaSensing. Specifically, we first leverage a Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) feature representation method to focus on location-independent characteristics. Then, in order to well transfer the model across different positions with limited data samples, a metric learning-based activity recognition method is proposed. Consequently, not only the generalization ability but also the transferable capability of the model would be significantly promoted. To fully validate the feasibility of the presented approach, extensive experiments have been conducted in an office with 24 testing locations. The evaluation results demonstrate that our method can achieve more than 90% in location-independent human activity recognition accuracy. More importantly, it can adapt well to the data samples with a small number of subcarriers and a low sampling rate.

scholarly journals A Deep Learning Framework for Human Activity Recognition Using Smartphone Data

IJARCCE ◽  
2021 ◽  
Vol 10 (2) ◽  
Author(s):  
J. Palimote ◽  
O.M.D. Georgewill ◽  
L. Atu
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Learning Framework
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