scholarly journals Sensor Fusion-Based Activity Recognition for Parkinson Patients

10.5772/16646 ◽  
2011 ◽  
Author(s):  
Majid Bahrepour ◽  
Nirvana Meratnia ◽  
Zahra Taghikhaki ◽  
Paul J. M. Having
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition

scholarly journals On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition

Sensors ◽  
2012 ◽  
Vol 12 (6) ◽  
pp. 8039-8054 ◽  
Author(s):  
Oresti Banos ◽  
Miguel Damas ◽  
Hector Pomares ◽  
Ignacio Rojas
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition ◽  
Human Activity ◽  
Additive Noise ◽  
Human Activity Recognition ◽  
The Impact

Ontology-based sensor fusion activity recognition

2018 ◽  
Vol 11 (8) ◽  
pp. 3073-3087 ◽  
Author(s):  
Mohd Halim Mohd Noor ◽  
Zoran Salcic ◽  
Kevin I-Kai Wang
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition ◽  
Fusion Activity

Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition

2014 ◽  
Vol 42 (1) ◽  
pp. 5-26 ◽  
Author(s):  
Oresti Banos ◽  
Miguel Damas ◽  
Alberto Guillen ◽  
Luis-Javier Herrera ◽  
Hector Pomares ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition

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.

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