A Weight Factor Algorithm for Activity Recognition Utilizing a Lattice-Based Reasoning Structure

2011 ◽  
Author(s):  
Jing Liao ◽  
Yaxin Bi ◽  
Chris Nugent
Keyword(s):  
Activity Recognition ◽  
Weight Factor

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

Smart Estimation Ver-H.1.0

2018 ◽  
Author(s):  
Sigit Haryadi
Keyword(s):  
Sample Size ◽  
Confidence Level ◽  
Time Span ◽  
Regression Equation ◽  
Weight Factor ◽  
The Past ◽  
Level Of Confidence ◽  
The Future ◽  
Future Value ◽  
The Difference

We cannot be sure exactly what will happen, we can only estimate by using a particular method, where each method must have the formula to create a regression equation and a formula to calculate the confidence level of the estimated value. This paper conveys a method of estimating the future values, in which the formula for creating a regression equation is based on the assumption that the future value will depend on the difference of the past values divided by a weight factor which corresponding to the time span to the present, and the formula for calculating the level of confidence is to use "the Haryadi Index". The advantage of this method is to remain accurate regardless of the sample size and may ignore the past value that is considered irrelevant.

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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