Incremental Learning Techniques for Online Human Activity Recognition

Vakili, Meysam; Rezaei, M. Katayoon

doi:10.48550/arxiv.2109.09435

Cited by 3 publications

(4 citation statements)

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“…They also concluded that the classification times were also dependent on the device models and capabilities. Similarly, Meysam, Vakili et al [25] proposed a real-time HAR model for online prediction of human physical movements based on the smartphone inertial sensors. A total of 20 different activities were selected, and six incremental learning algorithms were used to check the performance of the system, then all of them were also compared with the state-of-the-art HAR algorithms such as Decision Trees (DTs), AdaBoost, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The bagging method was used in this study to improve the classification performance of a single tree. Different numbers of decision trees of RF were used in the training process {5, 10,15,20,25,30,35,40,45, 50} to see how many decision trees were required to achieve the best results. After comparing all the results from all the activities, an RF with a 30-decision-tree classifier gave the best accuracy among all.…”

Section: Random Forestmentioning

confidence: 99%

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Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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There are more than 962 million people aged 60 and up globally. Physical activity declines as people get older, as does their capacity to undertake everyday tasks, effecting both physical and mental health. Many researchers use machine learning and deep learning methods to recognize human activities, but very few studies have been focused on human activity recognition of elderly people. This paper focuses on providing assistance to elderly people by monitoring their activities in different indoor and outdoor environments using gyroscope and accelerometer data collected from a smart phone. Smart phones have been routinely used to monitor the activities of persons with impairments; routine activities such as sitting, walking, going upstairs, going downstairs, standing, and lying are included in the dataset. Conventional Machine Learning and Deep Learning algorithms such as k-Nearest Neighbors, Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory Network are used for human activity recognition. Long Short-Term Memory is a recurrent neural network variation that is best suited to handling temporal sequences. Two-fold and ten-fold cross-validation methods were performed to show the effect of changing the data in the training and testing dataset. Among all the classification techniques, the proposed Long Short-Term Memory Network gave the best accuracy of 95.04%. However, Support Vector Machine gave 89.07% accuracy with a very low computational time of 0.42 min using 10-fold cross-validation.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

et al. 2022

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“…Additionally, it was determined that the duration of categorization was contingent upon the specific device types and their respective capabilities. In the same direction, authors [22], worked on a realtime human activity recognition (HAR) model that enables the live prediction of physical motions using inertial sensors embedded in smartphones. A selection of 20 distinct activities was made, its efficiency was evaluated using six incremental learning algorithms.…”

Section: Literaturementioning

confidence: 99%

Comparative performance of machine learning models for the classification of human gait

Thakur,

Lalwani

2024

Biomed. Phys. Eng. Express

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The efficacy of human activity recognition (HAR) models mostly relies on the characteristicsderived from domain expertise. The input of the classification algorithmconsists of many characteristics that are utilized to accurately and effectively classifyhuman physical activities. In contemporary research, machine learning techniqueshave been increasingly employed to automatically extract characteristics from unprocessedsensory input to develop models for Human Activity Recognition (HAR)and classify various activities. The primary objective of this research is to compareand contrast several machine learning models and determine a reliable and preciseclassification model for classifying activities. This study does a comparison analysisin order to assess the efficacy of 10 distinct machine learning models using frequentlyused datasets in the field of HAR. In this work, three benchmark public human walkingdatasets are being used. The research is conducted based on eight evaluatingparameters. Based on the study conducted, it was seen that the machine learningclassification models Random Forest, Extra Tree, and Light Gradient Boosting Machinehad superior performance in all the eight evaluating parameters compared tospecific datasets. Consequently, it can be inferred that machine learning significantlyenhances performance within the area of Human Activity Recognition (HAR). Thisstudy can be utilized to provide suitable model selection for HAR-based datasets.Furthermore, this research can be utilized to facilitate the identification of variouswalking patterns for bipedal robotic systems.

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“…Shahraki et al [50] investigated and compared the online techniques in the networking domain, highlighting the advantages of online learning and the challenges associated with online-based network traffic stream analysis. Vakili and Rezaei [51] analyzed the performance of online incremental algorithms applied to the real-time prediction of human physical movements using a human activity recognition (HAR) approach. They show that as the style of activities made by a person typically changes, the patterns of performing activities vary from person to person, with the incremental methods attaining consistently better performance than the offline batch ones.…”

Section: E Related Workmentioning

confidence: 99%

A Comparative Study of Situation Awareness-Based Decision-Making Model Reinforcement Learning Adaptive Automation in Evolving Conditions

2023

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Situation-awareness-based decision-making (SABDM) models constructed using cognitive maps and goal-direct task analysis techniques have been successfully used in decision support systems in safety-critical and mission-critical environments such as air traffic control and electrical energy distribution. Reinforcement learning (RL) and other machine learning techniques are used to automate situational awareness mental model parameter adjustments, reducing the expert work on the initial configuration and long-term maintenance without affecting the mental model's structure and maintaining the situationawareness-based decision-making model's cognitive and explainability characteristics. Real-world models should evolve to cope with changes in the environmental conditions. This study evaluates the application of reinforcement learning as an online adaptive technique to adjust the situational-awareness mental model's parameters under evolving conditions. We conducted evaluation experiments using real-world public datasets to compare the performance of the SABDM model with that of the reinforcement learning adaptation technique (SABDM/RL) and other adaptive machine learning methods under distinct concept drift evolving conditions. We measured the techniques' overall and dynamic performance to understand how well they adapt to evolving environmental conditions. The experiments show that SABDM/RL performs similarly to modern online adaptive machine learning classification methods with the support of concept drift detection techniques while maintaining the mental model strength of the situation awarenessbased systems.

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Incremental Learning Techniques for Online Human Activity Recognition

Cited by 3 publications

References 0 publications

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Human Activity Recognition for Elderly People Using Machine and Deep Learning Approaches

Comparative performance of machine learning models for the classification of human gait

A Comparative Study of Situation Awareness-Based Decision-Making Model Reinforcement Learning Adaptive Automation in Evolving Conditions

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