A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

Miranda, Leandro; Viterbo, José; Bernardini, Flávia

doi:10.1007/s10462-021-10094-0

Cited by 18 publications

(8 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There have been several reviews published in the area of human activity recognition in vision [25], [26], [27], [28], [29], [30], [31], sensor [32], [33], [34], [35], [36], [37], machine learning [38], [39], [40], [41], [42], [43], and deep learning-based methodologies [44], [45], [46], [47], [48], [49], [50], [51]. Nevertheless, there needs to be a survey that focuses specifically on yogic posture recognition.…”

Section: The Role Of Computer Vision In Yogamentioning

confidence: 99%

A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

View full text Add to dashboard Cite

Yoga has been a great form of physical activity and one of the promising applications in personal health care. Several studies prove that yoga is used as one of the physical treatments for cancer, musculoskeletal disorder, depression, Parkinson's disease, and respiratory heart diseases. In yoga, the body should be mechanically aligned with some effort on the muscles, ligaments, and joints for optimal posture. Postural-based yoga increases flexibility, energy, overall brain activity and reduces stress, blood pressure, and back pain. Body Postural Alignment is a very important aspect while performing yogic asanas. Many yogic asanas including uttanasana, kurmasana, ustrasana, and dhanurasana, require bending forward or backward, and if the asanas are performed incorrectly, strain in the joints, ligaments, and backbone can result, which can cause problems with the hip joints. Hence it is vital to monitor the correct yoga poses while performing different asanas. Yoga posture prediction and automatic movement analysis are now possible because of advancements in computer vision algorithms and sensors. This research investigates a thorough analysis of yoga posture identification systems using computer vision, machine learning, and deep learning techniques.INDEX TERMS Yogic posture recognition, optimal posture, machine learning methods, deep learning methods.

show abstract

Section: The Role Of Computer Vision In Yogamentioning

confidence: 99%

A Survey on Yogic Posture Recognition

Rajendran

Sethuraman

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Vahdat et al [ 42 ] expose a survey study about specific application domains, namely Mobile Crowd Sensing (MCS) in smart environments. In [ 43 ], authors have aimed to understand the state-of-the-art in the development of context-aware middlewares (CAMs) for aiding the construction of HAR applications when using ML. However, they do not consider explicitly the sensor networks and they have focused only on HAR applications.…”

Section: Background and Related Workmentioning

confidence: 99%

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Al-Saedi

Boeva

Casalicchio

et al. 2022

Sensors

View full text Add to dashboard Cite

Recent advances in sensor technology are expected to lead to a greater use of wireless sensor networks (WSNs) in industry, logistics, healthcare, etc. On the other hand, advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are becoming dominant solutions for processing large amounts of data from edge-synthesized heterogeneous sensors and drawing accurate conclusions with better understanding of the situation. Integration of the two areas WSN and AI has resulted in more accurate measurements, context-aware analysis and prediction useful for smart sensing applications. In this paper, a comprehensive overview of the latest developments in context-aware intelligent systems using sensor technology is provided. In addition, it also discusses the areas in which they are used, related challenges, motivations for adopting AI solutions, focusing on edge computing, i.e., sensor and AI techniques, along with analysis of existing research gaps. Another contribution of this study is the use of a semantic-aware approach to extract survey-relevant subjects. The latter specifically identifies eleven main research topics supported by the articles included in the work. These are analyzed from various angles to answer five main research questions. Finally, potential future research directions are also discussed.

show abstract

“…Machine learning is the use of computer computing to simulate human learning behavior. Machine learning did not emerge to replace traditional methods of statistical learning; rather, it is an extension of statistics [14,15]. Regression analysis is mainly used for prediction, inferring the output value corresponding to it from the training set.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

2022

NEP

View full text Add to dashboard Cite

A good environment can provide material security for urban development, facilitate the efficient use of energy resources, and attract large amounts of investment, bring technological innovation, and thus promote the pace of urbanization. On the contrary, a bad ecological environment restricts the development of urbanization, and the image, development speed, construction scale, investment environment and resource supply of cities are affected. The purpose of this paper is to study the coordinated development of urban development and nature conservation environment based on machine learning. The ecological city theory and the kinds of machine learning algorithms are analyzed. Among the mutual constraints of urbanization and ecological environment studied by using gray correlation matrix as well as coordination degree, the experimental results show that the unreasonable industrial structure is an important factor causing serious industrial pollution in M city.

show abstract

A survey on the use of machine learning methods in context-aware middlewares for human activity recognition

Cited by 18 publications

References 77 publications

A Survey on Yogic Posture Recognition

A Survey on Yogic Posture Recognition

Context-Aware Edge-Based AI Models for Wireless Sensor Networks—An Overview

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

Contact Info

Product

Resources

About