Micro Doppler Radar and Depth Sensor Fusion for Human Activity Monitoring in AAL

2019 ◽  
pp. 519-528
Author(s):  
Susanna Spinsante ◽  
Matteo Pepa ◽  
Stefano Pirani ◽  
Ennio Gambi ◽  
Francesco Fioranelli
Keyword(s):  
Sensor Fusion ◽  
Human Activity ◽  
Doppler Radar ◽  
Activity Monitoring ◽  
Depth Sensor

scholarly journals Improving posture classification accuracy for depth sensor-based human activity monitoring in smart environments

2016 ◽  
Vol 148 ◽  
pp. 97-110 ◽  
Author(s):  
Edmond S.L. Ho ◽  
Jacky C.P. Chan ◽  
Donald C.K. Chan ◽  
Hubert P.H. Shum ◽  
Yiu-ming Cheung ◽  
...  
Keyword(s):  
Human Activity ◽  
Classification Accuracy ◽  
Activity Monitoring ◽  
Smart Environments ◽  
Depth Sensor ◽  
Posture Classification

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

CNN-based sensor fusion techniques for multimodal human activity recognition

2017 ◽  
Author(s):  
Sebastian Münzner ◽  
Philip Schmidt ◽  
Attila Reiss ◽  
Michael Hanselmann ◽  
Rainer Stiefelhagen ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

Wearable Technology for Human Activity Monitoring and Recognition

2021 ◽  
pp. 191-218
Author(s):  
Shiban Kishen Koul ◽  
Richa Bharadwaj
Keyword(s):  
Human Activity ◽  
Wearable Technology ◽  
Activity Monitoring

scholarly journals Sport-Related Human Activity Detection and Recognition Using a Smartwatch

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5001 ◽  
Author(s):  
Zhendong Zhuang ◽  
Yang Xue
Keyword(s):  
Human Activity ◽  
Recognition Task ◽  
Sliding Window ◽  
Research Field ◽  
Activity Monitoring ◽  
Activity Detection ◽  
Related Activity ◽  
Window Method ◽  
Human Activity Detection ◽  
Detection And Recognition

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.

Sign in / Sign up

Export Citation Format

Share Document