Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer

2015 ◽  
Author(s):  
Yong-Joong Kim ◽  
Bong-Nam Kang ◽  
Daijin Kim
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Hidden Markov ◽  
Model Ensemble

scholarly journals Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmad Jalal ◽  
Shaharyar Kamal ◽  
Daijin Kim
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Hidden Markov ◽  
Human Motion ◽  
Experimental Results ◽  
Depth Image ◽  
Depth Imaging ◽  
Spatiotemporal Features ◽  
Depth Sensors

Nowadays, advancements in depth imaging technologies have made human activity recognition (HAR) reliable without attaching optical markers or any other motion sensors to human body parts. This study presents a depth imaging-based HAR system to monitor and recognize human activities. In this work, we proposed spatiotemporal features approach to detect, track, and recognize human silhouettes using a sequence of RGB-D images. Under our proposed HAR framework, the required procedure includes detection of human depth silhouettes from the raw depth image sequence, removing background noise, and tracking of human silhouettes using frame differentiation constraints of human motion information. These depth silhouettes extract the spatiotemporal features based on depth sequential history, motion identification, optical flow, and joints information. Then, these features are processed by principal component analysis for dimension reduction and better feature representation. Finally, these optimal features are trained and they recognized activity using hidden Markov model. During experimental results, we demonstrate our proposed approach on three challenging depth videos datasets including IM-DailyDepthActivity, MSRAction3D, and MSRDailyActivity3D. All experimental results show the superiority of the proposed approach over the state-of-the-art methods.

scholarly journals Log-Viterbi algorithm applied on second-order hidden Markov model for human activity recognition

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771877254 ◽  
Author(s):  
Yang Sung-Hyun ◽  
Keshav Thapa ◽  
M Humayun Kabir ◽  
Lee Hee-Chan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Human Activities ◽  
Time Complexity ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Elderly Care ◽  
Second Order ◽  
Assistive Living

Recognition of human activities is getting into the limelight among researchers in the field of pervasive computing, ambient intelligence, robotic, and monitoring such as assistive living, elderly care, and health care. Many platforms, models, and algorithms have been developed and implemented to recognize the human activities. However, existing approaches suffer from low-activity accuracy and high time complexity. Therefore, we proposed probabilistic log-Viterbi algorithm on second-order hidden Markov model that facilitates our algorithm by reducing the time complexity with increased accuracy. Second-order hidden Markov model is efficient relevance between previous two activities, current activity, and current observation that incorporate more information into recognition procedure. The log-Viterbi algorithm converts the products of a large number of probabilities into additions and finds the most likely activity from observation sequence under given model. Therefore, this approach maximizes the probability of activity recognition with improved accuracy and reduced time complexity. We compared our proposed algorithm among other famous probabilistic models such as Naïve Bayes, condition random field, hidden Markov model, and hidden semi-Markov model using three datasets in the smart home environment. The recognition possibility of our proposed method is significantly better in accuracy and time complexity than early proposed method. Moreover, this improved algorithm for activity recognition is much effective for almost all the dynamic environments such as assistive living, elderly care, healthcare applications, and home automation.

scholarly journals Hidden Markov model-based activity recognition for toddlers

2020 ◽  
Vol 41 (2) ◽  
pp. 025003
Author(s):  
Mark V Albert ◽  
Albert Sugianto ◽  
Katherine Nickele ◽  
Patricia Zavos ◽  
Pinky Sindu ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Activity Recognition ◽  
Hidden Markov ◽  
Model Based
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