Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study

Peluffo-Ordóñez, Diego H.; Umaquinga, Ana; Anaya, Andres; Serrano, Joaquim Rigola; Rosero-Montalvo, Paúl D.; Vasquez, Carlos; Suárez, Luis

doi:10.1109/etcm.2017.8247530

Cited by 13 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prototype selection criteria (PS) allow decreasing the data used as the input, the intrinsic knowledge of the high volume of data obtained being converged. Among all the PS methods, the most prevailing method for eliminating redundant data is the condensed nearest neighbor (CNN) algorithm, since it has been computationally proven to be suitable for reducing a large number of instances at a low computational cost [ 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

Rosero-Montalvo

Fuentes-Hernández

Morocho-Cayamcela

et al. 2021

Sensors

View full text Add to dashboard Cite

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.

show abstract

Section: Discussionmentioning

confidence: 99%

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

Rosero-Montalvo

Fuentes-Hernández

Morocho-Cayamcela

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…[ 169 ] Processing sensor data in real time can consume a significant amount of power, which can reduce battery life. [ 170 ] This can be a significant challenge, especially for wearable devices that are designed to be portable and used for extended periods. [ 171 ] To address this contradiction, researchers have started investigating neuromorphic computing, which exhibits desirable properties, including analogue computation, low power consumption, fast inference, event‐driven processing, online learning, and massive parallelism.…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

Tchantchane,

Zhou,

Zhang

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

Hand gesture, one of the essential ways for a human to convey information and express intuitive intention, has a significant degree of differentiation, substantial flexibility, and high robustness of information transmission to make hand gesture recognition (HGR) one of the research hotspots in the fields of human–human and human–computer or human–machine interactions. Noninvasive, on‐body sensors can monitor, track, and recognize hand gestures for various applications such as sign language recognition, rehabilitation, myoelectric control for prosthetic hands and human–machine interface (HMI), and many other applications. This article systematically reviews recent achievements from noninvasive upper‐limb sensing techniques for HGR, multimodal sensing fusion to gain additional user information, and wearable gesture recognition algorithms to obtain more reliable and robust performance. Research challenges, progress, and emerging opportunities for sensor‐based HGR systems are also analyzed to provide perspectives for future research and progress.

show abstract

“…The third challenge related to processing involves computational issues. The computational cost of recognizing thousands of gestures is quite challenging [46].The processing of data is often computationally expensive [71], and, in the case of multiple data flows, must be optimized to achieve the desirable performance [69]. The fourth challenge related to processing is time issues that arise due to the lack of process data of hand gestures in the time dimension [72].…”

Section: ) Challenges Related To Processingmentioning

confidence: 99%

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

et al. 2022

View full text Add to dashboard Cite

Sign language is the predominant mode of communication for the Deaf community. For the millions of people who suffer from hearing loss around the world, interaction with people who have the ability to hear and do not suffer from hearing impairment or loss is considered as complicated. In line with this issue, technology is perceived as a crucial factor in being an enabler of providing solutions to enhance the quality of life of the Deaf and people with hearing impairment by increasing accessibility. This research aims to review and analyze articles related to sign language recognition based on the sensor-based glove system, in order to identify academic motivations, challenges, and recommendations related to this field. The search for the relevant review materials and articles was performed on four major databases ranging from 2017 to 2022: Science Direct, Web of Science, IEEE Xplore, and Scopus. The articles were chosen based on our inclusion and exclusion criteria. The literature findings of this paper indicate the dataset size to be open issues and challenges for hand gesture recognition. Furthermore, the majority of research on sign language recognition based on data glove was performed on static, single hand, and isolated gestures. Moreover, recognition accuracy typically achieved results higher than 90%. However, most experiments were carried out with a limited number of gestures. Overall, it is hoped that this study will serve as a roadmap for future research and raise awareness among researchers in the field of sign language recognition.

show abstract

Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study

Cited by 13 publications

References 12 publications

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities

A Review of Hand Gesture Recognition Systems Based on Noninvasive Wearable Sensors

A Systematic Review on Systems-Based Sensory Gloves for Sign Language Pattern Recognition: An Update From 2017 to 2022

Contact Info

Product

Resources

About