Telemonitoring of Daily Activity Using Accelerometer and Gyroscope in Smart Home Environments

Batool, Mouazma; Jalal, Ahmad; Kim, Ki-Bum

doi:10.1007/s42835-020-00554-y

Cited by 67 publications

(20 citation statements)

References 14 publications

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“…For example, in the field of medical rehabilitation, we can establish a remote monitoring network for patients to strengthen the behavior monitoring of patients [28], so as to timely feed back medical data; in the field of ergonomics, we can also provide enough accurate human posture data for research [29]; in the field of sports, motion analysis technology can be used to simulate training, record athletes' action data, and compare with the standard template [30]. It can be used in the entertainment industry to generate vivid images for reference [31]. In the human motion recognition system based on sensors, the denoising and smoothing of data are usually used [32].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Sun

2021

Complexity

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With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Sun

2021

Complexity

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“…During approximate entropy, we measure the randomness of a series of data without any previous knowledge [57] about the dataset. Equations (15) and (16) show the inner concept of the calculation of approximate entropy, where m is the embedding dimensions and r is the noise filter.…”

Section: Approximate Entropymentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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“…In the genetic model, operations are performed on a basic unit known as chromosomes. Feature vectors are converted into chromosomes by mapping every single feature to respective genes [82]. Chromosomes consist of genes; each gene represents a single feature in the feature vector.…”

Section: Classifier: Reweighted Genetic Algorithmmentioning

confidence: 99%

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

et al. 2021

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Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.

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Telemonitoring of Daily Activity Using Accelerometer and Gyroscope in Smart Home Environments

Cited by 67 publications

References 14 publications

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

[Retracted] Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities

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