A probabilistic interval-based event calculus for activity recognition

2019 ◽  
Vol 89 (1-2) ◽  
pp. 29-52
Author(s):  
Alexander Artikis ◽  
Evangelos Makris ◽  
Georgios Paliouras
Keyword(s):  
Activity Recognition ◽  
Event Calculus

A Logic-Based Approach to Activity Recognition

2013 ◽  
pp. 1-13
Author(s):  
Alexander Artikis ◽  
Marek Sergot ◽  
Georgios Paliouras
Keyword(s):  
Activity Recognition ◽  
Human Activities ◽  
Video Content ◽  
Short Term ◽  
Event Calculus ◽  
Video Frames ◽  
Detailed Evaluation ◽  
Spatio Temporal ◽  
Complex Activities

The authors have been developing a system for recognising human activities given a symbolic representation of video content. The input of the system is a stream of time-stamped short-term activities detected on video frames. The output of the system is a set of recognised long-term activities, which are pre-defined spatio-temporal combinations of short-term activities. The constraints on the short-term activities that, if satisfied, lead to the recognition of a long-term activity, are expressed using a dialect of the Event Calculus. The authors illustrate the expressiveness of the dialect by showing the representation of several typical complex activities. Furthermore, they present a detailed evaluation of the system through experimentation on a benchmark dataset of surveillance videos.

Agent-oriented activity recognition in the event calculus: An application for diabetic patients

2017 ◽  
Vol 33 (4) ◽  
pp. 899-925 ◽  
Author(s):  
Özgür Kafalı ◽  
Alfonso E. Romero ◽  
Kostas Stathis
Keyword(s):  
Activity Recognition ◽  
Diabetic Patients ◽  
Event Calculus ◽  
Oriented Activity

Infant Monitoring System using Activity Recognition

2014 ◽  
Vol 134 (3) ◽  
pp. 332-337 ◽  
Author(s):  
Jun Goto ◽  
Takuya Kidokoro ◽  
Tomohiro Ogura ◽  
Satoshi Suzuki
Keyword(s):  
Monitoring System ◽  
Activity Recognition

Activity Recognition using Ultra Wide Band Range-Time Scan

2021 ◽  
Author(s):  
Arijit Chowdhury ◽  
Taniya Das ◽  
Smriti Rani ◽  
Anwesha Khasnobish ◽  
Tapas Chakravarty
Keyword(s):  
Activity Recognition ◽  
Wide Band ◽  
Ultra Wide Band ◽  
Time Scan

Deep Learning Trends for Video Based Activity Recognition: A Survey

2018 ◽  
Vol 8 (3) ◽  
pp. 165-171 ◽  
Author(s):  
Chandni ◽  
Alok Kumar Singh Kushwaha ◽  
Jagwinder Kaur Dhillon
Keyword(s):  
Deep Learning ◽  
Activity Recognition

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

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