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Topic Review
Wearable Technology in Sports
Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes.
  • 568
  • 20 Sep 2023
Topic Review
Wearable Sensors and Computer-Vision-Based Methods
Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.
  • 1.4K
  • 22 Feb 2021
Topic Review
Weakly Supervised Object Detection for Remote Sensing Images
To account for the lack of fine-grained annotations, such as object bounding boxes, several object detection methods have been developed that leverage only coarse-grain annotations (especially image-level labels indicating only the presence or absence of an object). This approach is called inexact Weak Supervision and introduces a new branch of Object Detection called Weakly Supervised Object Detection. Given an image, Remote Sensing Fully Supervised Object Detection (RSFSOD) aims to locate and classify objects based on Bounding Boxes annotations. Differently from RSFSOD, Remote Sensing Weakly Supervised Object Detection aims to precisely locate and classify object instances in Remote Sensing Images using only image-level labels or other types of coarse-grained labels (e.g., points or scribbles) as ground truth. 
  • 378
  • 24 Nov 2022
Topic Review
Weakly Supervised Crowd-Counting Models
Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively.  However, challenges like scare variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this research, the researchers propose a weakly-supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of the model.
  • 74
  • 28 Feb 2024
Topic Review
Weakly Supervised and Unsupervised Methods in Plant Segmentation
Plant segmentation is a challenging computer vision task due to plant images complexity. We need to distinguish plant parts rather than the whole plant. The major complication of multi-part segmentation is the absence of well-annotated datasets. It is very time-consuming and expensive to annotate datasets manually on the object parts level.
  • 273
  • 04 Aug 2023
Topic Review
WBAN Authentication Protocols for Intra-BAN Tier
Telecare medical information system (TMIS) is a technology used in a wireless body area network (WBAN), which has a crucial role in healthcare services. TMIS uses wearable devices with sensors to collect patients’ data and transmit the data to the controller node via a public channel.
  • 613
  • 26 Aug 2022
Topic Review
Wavelet Threshold Denoising Algorithm
The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. 
  • 395
  • 06 Jul 2022
Topic Review
Wave Race 64
Wave Race 64 is a racing video game developed by Nintendo EAD and published by Nintendo. It was released for the Nintendo 64 in 1996 and is a follow-up to the 1992 Game Boy title Wave Race. Most of the game involves the player racing on a Jet Ski on a variety of courses while successfully manoeuvring the vehicle around various buoys. A multiplayer mode where two players can compete against each other on a chosen course is also included. The game supports the Controller Pak, which allows players to transfer saved data from one game cartridge to another. Originally referred to as "F-Zero on water", the game was intended to feature high-speed boats with transforming capabilities, but these were ultimately replaced with Jet Skis as producer Shigeru Miyamoto felt that the game would not be differentiated enough from other titles on other systems. Wave Race 64 received acclaim from critics, who praised the game's satisfying controls and dynamic watery environments. The game is credited for helping Nintendo effectively make its paradigmatic leap from the 16-bit 2D graphics of the Super Nintendo Entertainment System to the Nintendo 64's 3D capabilities. It was re-released for the Wii and Wii U's Virtual Console in 2007 and 2016, respectively. A sequel, Wave Race, was released in 2001.
  • 1.2K
  • 13 Oct 2022
Topic Review
Watson (Computer)
Watson is a question-answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson. The computer system was initially developed to answer questions on the quiz show Jeopardy! and, in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first place prize of $1 million. In February 2013, IBM announced that Watson software system's first commercial application would be for utilization management decisions in lung cancer treatment at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with WellPoint (now Anthem). In 2013, Manoj Saxena, IBM Watson's business chief said that 90% of nurses in the field who use Watson now follow its guidance.
  • 1.4K
  • 19 Oct 2022
Topic Review
Watermarking Solution for Medical Imaging Security
Securing medical imaging poses a significant challenge in preserving the confidentiality of healthcare data. Numerous research efforts have focused on fortifying these images, with encryption emerging as a primary solution for maintaining data integrity without compromising confidentiality. However, applying conventional encryption techniques directly to e-health data encounters hurdles, including limitations in data size, redundancy, and capacity, particularly in open-channel patient data transmissions. As a result, the unique characteristics of images, marked by their risk of data loss and the need for confidentiality, make preserving the privacy of data contents a complex task. This underscores the pressing need for innovative approaches to ensure the security and confidentiality of sensitive healthcare information within medical images.
  • 130
  • 02 Jan 2024
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