Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor

2016 ◽  
Author(s):  
Alan K. Bourke ◽  
Espen A F Ihlen ◽  
Pepijn Van de Ven ◽  
John Nelson ◽  
Jorunn L. Helbostad
Keyword(s):  
Physical Activity ◽  
Real Time ◽  
Video Analysis ◽  
Detection Algorithm ◽  
Activity Detection ◽  
Accelerometer Sensor

scholarly journals Comparison of Physical Activity Sensors and Heart Rate Monitoring for Real-Time Activity Detection in Type 1 Diabetes and Control Subjects

2013 ◽  
Vol 15 (9) ◽  
pp. 751-757 ◽  
Author(s):  
Chinmay Manohar ◽  
Derek T. O'Keeffe ◽  
Ling Hinshaw ◽  
Ravi Lingineni ◽  
Shelly K. McCrady-Spitzer ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Type 1 Diabetes ◽  
Real Time ◽  
Activity Detection ◽  
Heart Rate Monitoring ◽  
Time Activity ◽  
Activity Sensors ◽  
And Control

Real-time Worker Safety Management System Using Deep Learning-based Video Analysis Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 25-30
Author(s):  
So Yeon Jeon ◽  
Jong Hwa Park ◽  
Sang Byung Youn ◽  
Young Soo Kim ◽  
Yong Sung Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Analysis ◽  
Management System ◽  
Safety Management ◽  
Worker Safety ◽  
Safety Management System ◽  
Analysis Algorithm

Real-time Judgment of Workload using Heart Rate and Physical Activity

2020 ◽  
Author(s):  
Nobuki Hashiguchi ◽  
Lim Yeongjoo ◽  
Cyo Sya ◽  
Shinichi Kuroishi ◽  
Yasuhiro Miyazaki ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Real Time ◽  
Time Judgment

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

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