User Recognition Research Articles

Crowdsourced Bicycle Monitoring and Finding With Riding User Recognition Based on Internet of Things Technologies

Crowdsourced Bicycle Monitoring and Finding With Riding User Recognition Based on Internet of Things Technologies

Investigating What Factors Influence Users’ Rating of Harmful Algorithmic Bias and Discrimination

There has been growing recognition of the crucial role users, especially those from marginalized groups, play in uncovering harmful algorithmic biases. However, it remains unclear how users’ identities and experiences might impact their rating of harmful biases. We present an online experiment (N=2,197) examining these factors: demographics, discrimination experiences, and social and technical knowledge. Participants were shown examples of image search results, including ones that previous literature has identified as biased against marginalized racial, gender, or sexual orientation groups. We found participants from marginalized gender or sexual orientation groups were more likely to rate the examples as more severely harmful. Belonging to marginalized races did not have a similar pattern. Additional factors affecting users’ ratings included discrimination experiences, and having friends or family belonging to marginalized demographics. A qualitative analysis offers insights into users' bias recognition, and why they see biases the way they do. We provide guidance for designing future methods to support effective user-driven auditing.

RDGait: A mmWave Based Gait User Recognition System for Complex Indoor Environments Using Single-chip Radar

In this paper, we aim to study millimeter-wave-based gait recognition in complex indoor environments, focusing on dealing with multipath ghosts and supporting rapid deployment to new environments. We design a ghost detection algorithm based on velocity change patterns. This algorithm relies solely on velocity estimation, requiring no environmental priors or multipath modeling. Hence, it is suitable for single-chip millimeter-wave radar with low angular resolution and can be conveniently deployed in new indoor settings. In addition, we build a gait recognition network based on an attention-based Recurrent Neural Network (RNN) to extract spatiotemporal-velocity features from RD heatmaps. We have evaluated RDGait in two scenarios: a corridor scenario and a crowded office scenario, with 125 volunteers of different genders and ages ranging from 6 to 63. RDGait achieves a user recognition accuracy exceeding 95% among 125 candidates in both scenarios. We have further deployed RDGait in two additional scenarios using the pretrain-finetune approach. With minimal user registration data, RDGait could achieve satisfactory (> 90%) recognition accuracy in these new environments considering different radar placements, heights, and number of co-existing users.

Speech Recognition and Subjective Hearing Abilities for Electric-Acoustic Stimulation Users With Unilateral Hearing Loss.

Some cochlear implant (CI) recipients with unilateral hearing loss (UHL) have functional acoustic hearing in the implanted ear, warranting the fitting of an ipsilateral electric-acoustic stimulation (EAS) device. The present study assessed speech recognition and subjective hearing abilities over time for EAS users with UHL. Prospective, repeated-measures. Tertiary referral center. Adult CI recipients with normal-to-moderate low-frequency acoustic thresholds in the implanted ear and a contralateral pure-tone average (0.5, 1, and 2 kHz) ≤25 dB HL. Participants were evaluated preoperatively and at 1, 3, and 6 months post-activation. Speech recognition for the affected ear was evaluated with CNC words in quiet. Masked speech recognition in the bilateral condition was evaluated with AzBio sentences in a 10-talker masker (0 dB SNR) for three spatial configurations: target from the front and masker either colocated with the target or presented 90 degrees toward the affected or contralateral ear. Responses to the Speech, Spatial, and Qualities of Hearing Scale subscales were used to assess subjective hearing abilities. Participants experienced significant improvements in CNC scores ( F(3,13) = 14.90, p < 0.001), and masked speech recognition in the colocated ( F(3,11) = 3.79, p = 0.043) and masker toward the contralateral ear ( F(3,11) = 4.75, p = 0.023) configurations. They also reported significantly better abilities on the Speech Hearing ( F(3,13) = 5.19, p = 0.014) and Spatial Hearing ( F(3,13) = 10.22, p = 0.001) subscales. Adults with UHL and functional acoustic hearing in the implanted ear experience significant improvements in speech recognition and subjective hearing abilities within the initial months of EAS use as compared with preoperative performance and perceptions.

Examining User Recognition of a Sport-Centered OTT Service Using Topic Modeling and Sentiment Analysis Focusing on the SPOTV Now App

PURPOSE Recently, the need to improve service quality to provide an ideal viewing experience for sport OTT service users has increased. This study aimed to analyze user reviews of the SPOTV Now app, an OTT service specializing in sports in South Korea, to identify major perceptions of viewers and suggest measures for improvement.METHODS Review data (8,512) recorded on the SPOTV Now app were collected from November 21, 2017 to November 30, 2023. LDA-based topic modeling analysis was conducted along with a series of data preprocessing processes. Sentiment analysis was carried out through machine learning logistic regression analysis and odds ratio scores were calculated.RESULTS A total of five topics were derived, including ‘member management service', 'service stability', 'sport streaming service', 'sport video service', and 'subscription service'. Sentiment analysis indicated that 'exclusive', 'server', 'payment', 'log-in', 'authentication', 'pay', 'subscription', 'advertisement', 'play', and 'loading' were the top ten keywords.CONCLUSIONS Sport OTT operators should be more responsive to user requests and feedback, and video playback and server error problems should be improved for service stability. It is necessary to secure diversity as well as exclusivity of content, in addition to minimizing advertising or preparing various rate types, so that app users feel that they receive high value for their money.

A Survey: Fake News Detection using Machine Learning and AI Techniques

Artificial intelligence is an analysis of how algorithms can do things that humans are doing differently right now. It is machine intelligence it industry that aims to create it. Researchers around the world have developed a lot of interest in the fake trend of news detection. Several social science research on the effect of false knowledge and how people respond to it have been conducted. Fake news would be any material that is not accurate and created to encourage its viewers to believe in something Fake. Data mining classification is a methodology focused on algorithms for machine learning that uses mathematics, probability, distributions of probabilities, or artificial intelligence. The new and hot topic of deep learning in machine learning can be described as a cascade of layers conducting non-linear processing to achieve many levels of data representation. Data preprocessing faces many particular problems that have resulted in a set of algorithms and heuristic strategies for preprocessing activities such as blending and washing, the recognition of users and sessions, etc. In this review paper, we researched, Artificial intelligence, Data preprocessing, Fake news detection, deep learning as well as we described the various classification techniques

Analysis and Improvement Strategies of Gansu Youth League Official Short Video Content from the Perspective of Recognition

How to enhance the recognition of new media Official Short Videos among users? This paper selects the Douyin account of the Youth League of Gansu Province for analysis. Based on the literature on evaluating the effectiveness of communication of short videos, this article has developed a model for measuring video identity, and provided content analysis for the 9% of Gansu Youth League members with the highest degree of identity using this model. In the research, this article found that video materials tend to be official and original videos, and tends to focus on comprehensive organization, local content, and positive emotions. High quality video content in the form of video, plus the popular sound effect of only music, and videos with tags to participate in topics or Douyin challenges that last between 1 and 15 seconds, have the greatest chance of gaining user recognition. At the same time, official video sources, original videos, and videos from local sources have the highest correlation with recognition. Therefore, this article believes that in order to gain recognition for future Official Short Videos, more efforts should be made in these three aspects.

A Study on market competition in digital music streaming services : Focusing on user recognition of service characteristics

A Study on market competition in digital music streaming services : Focusing on user recognition of service characteristics

Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control.

Robotic lower-limb prostheses, with their actively powered joints, may significantly improve amputee users' mobility and enable them to obtain healthy-like gait in various modes of locomotion in daily life. However, timely recognition of the amputee users' locomotive mode and mode transition still remains a major challenge in robotic lower-limb prosthesis control. In the paper, the authors present a new multi-dimensional dynamic time warping (mDTW)-based intent recognizer to provide high-accuracy recognition of the locomotion mode/mode transition sufficiently early in the swing phase, such that the prosthesis' joint-level motion controller can operate in the correct locomotive mode and assist the user to complete the desired (and often power-demanding) motion in the stance phase. To support the intent recognizer development, the authors conducted a multi-modal gait data collection study to obtain the related sensor signal data in various modes of locomotion. The collected data were then segmented into individual cycles, generating the templates used in the mDTW classifier. Considering the large number of sensor signals available, we conducted feature selection to identify the most useful sensor signals as the input to the mDTW classifier. We also augmented the standard mDTW algorithm with a voting mechanism to make full use of the data generated from the multiple subjects. To validate the proposed intent recognizer, we characterized its performance using the data cumulated at different percentages of progression into the gait cycle (starting from the beginning of the swing phase). It was shown that the mDTW classifier was able to recognize three locomotive mode/mode transitions (walking, walking to stair climbing, and walking to stair descending) with 99.08% accuracy at 30% progression into the gait cycle, well before the stance phase starts. With its high performance, low computational load, and easy personalization (through individual template generation), the proposed mDTW intent recognizer may become a highly useful building block of a prosthesis control system to facilitate the robotic prostheses' real-world use among lower-limb amputees.

Multi-Session Electrocardiogram–Electromyogram Database for User Recognition

Current advancements in biosignal-based user recognition technology are paving the way for a next-generation solution that addresses the limitations of face- and fingerprint-based user recognition methods. However, existing biosignal benchmark databases (DBs) for user recognition often suffer from limitations, such as data collection from a small number of subjects in a single session, hindering comprehensive analysis of biosignal variability. This study introduces CSU_MBDB1 and CSU_MBDB2, databases containing electrocardiogram (ECG) and electromyogram (EMG) signals from diverse experimental subjects recorded across multiple sessions. These in-house DBs comprise ECG and EMG data recorded in multiple sessions from 36 and 58 subjects, respectively, with a time interval of more than one day between sessions. During the experiments, subjects performed a total of six gestures while comfortably seated at a desk. CSU_MBDB1 and CSU_MBDB2 consist of three identical gestures, providing expandable data for various applications. When the two DBs are expanded, ECGs and EMGs from 94 subjects can be used, which is the largest number among the multi-biosignal benchmark DBs built by multi-sessions. To assess the usability of the constructed DBs, a user recognition experiment was conducted, resulting in an accuracy of 66.39% for ten subjects. It is important to emphasize that we focused on demonstrating the applicability of the constructed DBs using a basic neural network without signal denoising capabilities. While this approach results in a sacrifice in accuracy, it concurrently provides substantial opportunities for performance enhancement through the implementation of optimized algorithms. Adapting signal denoising processes to the constructed DBs and designing a more sophisticated neural network would undoubtedly contribute to improving the recognition accuracy. Consequently, these constructed DBs hold promise in user recognition, offering valuable research for future investigations. Additionally, DBs can be used in research to analyze the nonlinearity characteristics of ECG and EMG.

Interface design features and evaluation of batik 4.0 mobile application

The use of information and communication technology could increase the quantity and quality of small medium enterprises (SME) production, including batik industry. This study focuses on the development of batik 4.0, a custom batik mobile-based interface that makes it easier for customer which can be used to quickly produce high-quality digital batik designs. The findings of this study simplify the ordering process for potential clients who want to acquire custom batik designs. Ease of transactions, namely down payments (DP) where users can make advance payments, so that users are relieved in terms of payment transactions. In designing mobile devices, applications, and user interfaces (UI), it is important to consider the user experience (UX). This paper focuses on UX design rooted in the user-centered design (UCD) approach, placing emphasis on understanding user requirements and prioritizing empathy for users. This ensures the recognition of user needs and the creation of a high-fidelity prototype. Then it was validated by the UI experts to identify problems and user difficulties in interacting with the UI. The experts responded positively towards the application and suggest for prototype improvement. Lastly, UX testing; based on the user experience questionnaire (UEQ)-S benchmark results, the batik 4.0 mobile is included in the “Excellent” category.

Net valence analysis of iris recognition technology-based FinTech

Iris recognition technology (IRT)-based authentication is a biometric financial technology (FinTech) application used to automate user recognition and verification. In addition to being a controversial technology with various facilitators and inhibitors, the adoption of IRT-based FinTech is driven by contextual factors, such as customer perceptions, deployed biometric technology, and financial transaction settings. Due to its controversial and contextual properties, analyzing IRT-based FinTech acceptance is challenging. This study uses a net valence framework to investigate the salient positive and negative factors influencing the intention to use IRT-based FinTech in automated teller machines (ATMs) in Jordan. This study is pertinent because there is a dearth of research on IRT-based FinTech in the relevant literature; most previous research has taken purely engineering and technical approaches. Furthermore, despite considerable investments by banks and other financial institutions in this FinTech, target user adoption is minimal, and only 6% of Jordan’s ATM transactions are currently IRT-enabled. This study employs mixed methods. In the first qualitative study, 17 Jordanian customers were interviewed regarding the benefits and risks of IRT-based FinTech in ATMs. Content analyses determined the most important concepts or themes. The advantages include financial security, convenience, and FinTech-enabled hygiene, whereas the concerns include performance, financial, privacy, and physical risks. The research model is constructed based on the qualitative study and theoretical underpinnings, wherein 631 Jordanian bank customers with active ATM accounts were surveyed to validate the research model. The findings indicate that IRT-based FinTech usage in ATMs is proportional to its perceived value. In descending order of effect, financial security, FinTech-enabled hygiene, and convenience benefits positively impact perceived value. Privacy, financial, and physical risks have negative impacts on perceived value, whereas performance risk has no effect. This study contributes to the relatively untapped domain of biometric technology in information systems, with important theoretical and practical implications.

Conductive chenille yarn-based triboelectric carpet fabrics with enhanced flexibility and comfort for smart home monitoring

Smart home monitoring systems are essential for home security. Creating monitoring devices that are intrusion-resistant, scalable, and environment-compatible remains a challenge. In this study, we have developed a soft, warm, and mass-producible triboelectric carpet fabric for motion monitoring and user recognition. A specifically designed conductive chenille yarn is utilized as the inlay yarn, which is inserted into the highly elastic 1 × 1 rib courses using knitting weft inlay technology to produce the chenille triboelectric carpet fabric. The carpet fabric generates a maximum power density of about 2942 μW/m2 in the contact-separation mode, which could power small electronic devices by harvesting energy using simple circuit management. Additionally, it can be used for behavior recognition and user identification with the support of machine learning. Four behaviors including slow walking, walking, jogging and jumping are classified successfully, and four different subjects are recognized. The carpet fabric is flexible, warm, inexpensive, easy to manufacture, and compatible with the living environment, showing great potential in smart monitoring systems for home security.

Iris Liveness Detection using Fusion of Thepade SBTC and Triangle Thresholding Features with Machine Learning Algorithms

Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.

Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks

Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and personal information due to the convenience and accessibility. However, losing control of this data poses risks if the phone gets lost or stolen. While passwords, PINs, and pattern locks are common security methods, they can still be compromised through exploits like smudging residue from touching the screen. This research explored leveraging smartphone sensors to authenticate users based on behavioral patterns when operating the device. The proposed technique uses a deep learning model called DeepResNeXt, a type of deep residual network, to accurately identify smartphone owners through sensor data efficiently. Publicly available smartphone datasets were used to train the suggested model and other state-of-the-art networks to conduct user recognition. Multiple experiments validated the effectiveness of this framework, surpassing previous benchmark models in this area with a top F1-score of 98.96%.

Triboelectric Contact Localization Electronics: A Systematic Review.

The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

Machine learning-based novel continuous authentication system using soft keyboard typing behavior and motion sensor data

Smartphones utilize various authentication methods, including passwords, fingerprints, and face recognition. While this information is quite practical and easy to remember, it introduces several security issues. The primary concerns involve theft, password forgetfulness, or unauthorized password copying. Implementing behavioral biometrics for user authentication adds an extra layer of security. The main contribution of this study is the utilization of soft keyboard typing behavior, a behavioral biometric, for continuous user recognition. To achieve this, the phone's grip style and typing characteristics of users are scrutinized using data collected from motion sensors and the touchscreen panel. Another challenge in mobile device authentication pertains to recognition accuracy and processing time. To expedite and optimize data classification, a hybrid classification structure is suggested. This structure incorporates correlation-based feature selection and a straightforward logistic regression method, offering rapid and highly accurate classification outcomes—a further contribution of this study. Experimental results demonstrate that user identification can be accomplished in as little as 0.03 ms, with a classification accuracy of up to 93%. Continuous authentication systems offer greater security compared to one-time authentication systems. Nevertheless, these systems might not always yield the most precise results. Overcoming this challenge necessitates the development of an efficient software architecture. In line with this, an additional contribution of this study is an explanation of how to construct a continuous authentication system using the developed architecture.

SemiHAR: Improving Semisupervised Human Activity Recognition via Multitask Learning.

Semisupervised human activity recognition (SemiHAR) has attracted attention in recent years from various domains, such as digital health and ambient intelligence. Currently, it still faces two challenges. For one thing, discriminative features may exist among multiple sequences rather than a single sequence since activities are combinations of motions involving several body parts. For another thing, labeled data and unlabeled data suffer from distribution discrepancies due to the different behavior patterns or biological conditions of users. For that, we propose a novel SemiHAR method based on multitask learning. First, a dimension-based Markov transition field (DMTF) technique is designed to generate 2-D activity data for capturing the interactions among different dimensions. Second, we jointly consider the user recognition (UR) task and the activity recognition (AR) task to reduce the underlying discrepancy. In addition, a task relation learner (TRL) is introduced to dynamically learn task relations, which enables the primary AR task to exploit preferred knowledge from other secondary tasks. We theoretically analyze the proposed SemiHAR and provide a novel generalization result. Extensive experiments conducted on four real-world datasets demonstrate that SemiHAR outperforms other state-of-the-art methods.

Tecnologia auditiva assistiva em usuários de implante coclear em ambientes reverberantes com múltiplas fontes de ruído

ABSTRACT Purpose This study aimed to evaluate the contribution of assistive listening technology with wireless connectivity in cochlear implant (CI) users in reverberating and noise situations. Methods Prospective cross-sectional study approved by the Institutional Ethics Committee (CAAE 8 3031418.4.0000.0068). Adolescents and adults CI users with pre- or post-lingual deafness were selected. For bilateral users, each ear was assessed separately. Speech recognition was assessed using recorded lists of disyllabic words presented at 65 dBA at 0° azimuth with and without the Wireless Mini Microphone 2 (Cochlear™) connected to the Nucleus®6 speech processor. Room reverberation was measured as 550 ms. To assess the contribution of the assistive listening device (ALD) in a reverberating environment, speech recognition was assessed in quiet. To assess the contribution of the ALD in reverberation and noise, speech recognition was presented at 0° azimuth along with the noise coming from 8 loudspeakers symmetrically arranged 2 meters away from the center with multi-talker babble noise using signal to noise ratio of +10dB. To avoid learning bias or fatigue, the order of the tests was randomized. Comparison of means was analyzed by t test for paired samples, adopting significance level of p &lt;0.005. Results Seventeen patients with a mean age of 40 years were invited and agreed to participate, with 2 bilateral participants, totaling 19 ears assessed. There was a significant positive contribution from the Mini Mic2 in reverberation, and noise+reverberation (p &lt;0.001). Conclusion ALD was able to improve speech recognition of CI users in both reverberation and noisy situations.

Biosignal Contrastive Representation Learning for Emotion Recognition of Game Users

Biosignal Contrastive Representation Learning for Emotion Recognition of Game Users