Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

Nadeem, Amir; Jalal, Ahmad; Kim, Kibum

doi:10.3390/sym12111766

Cited by 50 publications

(13 citation statements)

References 29 publications

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“…ey concluded from their study that implementing psychological concept of habit formation serves as a link for introducing effective artificial neural network-based model into the fitness strategies. Nadeem et al [25] conclude that physical activity recognition is an essential tool for smart health monitoring and for fitness exercises. ey propose a unified framework to explore multidimensional features with the help of a fusion of body part model and discriminant analysis, using the features for human pose estimation.…”

Section: Related Workmentioning

confidence: 99%

Application of Multilayer Neural Network in Sports Psychology

Yang¹

2022

Scientific Programming

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The research on multilayer neural network theory has developed rapidly in recent years. It has parallel processing capabilities and fault tolerance and has aroused the interest of many researchers. The neural network has made great progress in the field of control, especially in model identification and control. It has been quickly applied in the fields of device design, optimized operation, and fault analysis and diagnosis. Neural network control, as an automated control technology in the 21st century, has been fully proved by theories and practices at home and abroad, and it is very useful in complex process control. Sports psychology is a discipline that studies the psychological characteristics and laws of people engaged in sports, and it is also a new development in sports science. The main task of sports psychology is to study people’s psychological processes when participating in sports, such as feeling, perception, appearance, thinking, memory, emotion, and characteristics of will and its role and significance in sports. An important feature of multilayer neural networks is to achieve results that match the expected output through network learning. It has strong self-learning, self-adaptability, and fault tolerance. The multilayer neural network system evaluation method is unique with its extraordinary ability to deal with complex nonlinear problems and does not involve human intervention. This article presents a multilayer neural network algorithm, which classifies the samples of athletes, and studies the physical education training process, the psychological characteristics of related personnel in sports competitions, such as the psychological characteristics of the formation of sports skills, and the psychological training of athletes before the game.

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Section: Related Workmentioning

confidence: 99%

Application of Multilayer Neural Network in Sports Psychology

Yang¹

2022

Scientific Programming

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“…Data acquisition in vision-sensors based action recognition systems comprise of RGB [22,23], depth and skeletal data [24,25]. In this section related work in the field of HOI systems based on all three aforementioned vision sensors techniques is presented.…”

Section: A Multi-vision Sensors Based Hoi Systemsmentioning

confidence: 99%

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

et al. 2021

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Human-Object Interaction (HOI) recognition, due to its significance in many computer visionbased applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher's Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.INDEX TERMS 3D point cloud, fiducial points, human-object interaction, pixel labeling, semantic segmentation, super-pixels, K-ary tree hashing.

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“…The area of each region is computed using Equation ( 6) after object filtering. The area of an object is computed using the number of pixels q(x, y) contained in that object [47]. These areas are used to detect the largest object in a frame.…”

Section: Object Detectionmentioning

confidence: 99%

A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

et al. 2021

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Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.

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Accurate Physical Activity Recognition using Multidimensional Features and Markov Model for Smart Health Fitness

Cited by 50 publications

References 29 publications

Application of Multilayer Neural Network in Sports Psychology

Application of Multilayer Neural Network in Sports Psychology

Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM

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