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Journal articles on the topic 'Personalised Signal Processing'

To see the other types of publications on this topic, follow the link: Personalised Signal Processing.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Wolf, KatieAnna E., and Rebecca Fiebrink. "Personalised interactive sonification of musical performance data." Journal on Multimodal User Interfaces 13, no. 3 (March 13, 2019): 245–65. http://dx.doi.org/10.1007/s12193-019-00294-y.

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Shpakova, Tatiana, and Nataliya Sokolovska. "Probabilistic personalised cascade with abstention." Pattern Recognition Letters 147 (July 2021): 8–15. http://dx.doi.org/10.1016/j.patrec.2021.03.029.

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Kasemsuppakorn, Piyawan, and Hassan A. Karimi. "Personalised routing for wheelchair navigation." Journal of Location Based Services 3, no. 1 (March 2009): 24–54. http://dx.doi.org/10.1080/17489720902837936.

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Mills, Jed, Jia Hu, and Geyong Min. "Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing." IEEE Transactions on Parallel and Distributed Systems 33, no. 3 (March 1, 2022): 630–41. http://dx.doi.org/10.1109/tpds.2021.3098467.

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Millonig, A., and G. Gartner. "Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools." Journal of Location Based Services 5, no. 1 (November 29, 2010): 3–21. http://dx.doi.org/10.1080/17489725.2010.535029.

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Kasabov, Nikola. "Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach." Pattern Recognition Letters 28, no. 6 (April 2007): 673–85. http://dx.doi.org/10.1016/j.patrec.2006.08.007.

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Pandithevan, Ponnusamy, and Gurunathan Saravana Kumar. "Personalised bone tissue engineering scaffold with controlled architecture using fractal tool paths in layered manufacturing." Virtual and Physical Prototyping 4, no. 3 (September 2009): 165–80. http://dx.doi.org/10.1080/17452750903055512.

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Cavallo, Francesca Romana, Khalid Baig Mirza, Sara de Mateo, Luca Miglietta, Jesus Rodriguez-Manzano , Konstantin Nikolic, and Christofer Toumazou. "A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR." Biosensors 12, no. 7 (July 19, 2022): 537. http://dx.doi.org/10.3390/bios12070537.

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This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.

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Villarán, Carlos, and Marta Beltrán. "User-Centric Privacy for Identity Federations Based on a Recommendation System." Electronics 11, no. 8 (April 14, 2022): 1238. http://dx.doi.org/10.3390/electronics11081238.

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Specifications such as SAML, OAuth, OpenID Connect and Mobile Connect are essential for solving identification, authentication and authorisation in contexts such as mobile apps, social networks, e-commerce, cloud computing or the Internet of Things. However, end-users relying on identity providers to access resources, applications or services lose control over the Personally Identifiable Information (PII) they share with the different providers composing identity federations. This work proposes a user-centric approach based on a recommendation system to support users in making privacy decisions such as selecting service providers or choosing their privacy settings. The proposed Privacy Advisor gives end-users privacy protection by providing personalised recommendations without compromising the identity federations’ functionalities or requiring any changes in their underlying specifications. A proof of concept of the proposed recommendation system is presented to validate and evaluate its utility and feasibility.

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Adeluyi, Olufemi, Miguel A. Risco-Castillo, María Liz Crespo, Andres Cicuttin, and Jeong-A. Lee. "A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry." Sensors 20, no. 22 (November 12, 2020): 6461. http://dx.doi.org/10.3390/s20226461.

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Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.

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van Erp, Bart, Albert Podusenko, Tanya Ignatenko, and Bert de Vries. "A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms." Applied Sciences 11, no. 20 (October 14, 2021): 9535. http://dx.doi.org/10.3390/app11209535.

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Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today’s commercial hearing aids is how to take into account users’ preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to “situated soundscaping”, which aims at enabling users to make on-the-spot (“situated”) decisions about the enhancement or suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting.

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van Erp, Bart, Albert Podusenko, Tanya Ignatenko, and Bert de Vries. "A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms." Applied Sciences 11, no. 20 (October 14, 2021): 9535. http://dx.doi.org/10.3390/app11209535.

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Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today’s commercial hearing aids is how to take into account users’ preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to “situated soundscaping”, which aims at enabling users to make on-the-spot (“situated”) decisions about the enhancement or suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting.

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Müller, H., A. Foncubierta-Rodriguez, and A. Depeursinge. "Sensors, Medical Images and Signal Processing: Ubiquitous Personalized Health Monitoring." Yearbook of Medical Informatics 21, no. 01 (August 2012): 100–103. http://dx.doi.org/10.1055/s-0038-1639438.

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SummaryTo summarize excellent research in the field of medical sensor, signal and imaging informatics published in the year 2011.Synopsis of the articles selected for the IMIA (International Medical Informatics Association) Yearbook 2012 through a manual initial selection and a peer review process to find the best paper in this domain published in 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics. An increased number of systems with embedded signal processing where sensors include signal processing were observed in 2011. In all domains, pragmatic solutions with the goal of clinical impact have grown, including in developing countries where simple, robust techniques are combined to address primary and simple medical problems with potentially high impact. Finally, recent advances in image and signal processing are moving towards patient-based modeling.The best paper selection of articles on sensors, signal, and imaging informatics shows examples of excellent research on methods concerning theoretically sound original development in this field in the year 2012.

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Evtushenko, Gennadiy, Inna A. Lezhnina, Artem I. Morenetz, Boris N. Pavlenko, Arman A. Boyakhchyan, Stanislav N. Torgaev, and Irina Nam. "Development of medical capacitive coupling electrodes using the skin-electrode contact control." Sensor Review 40, no. 3 (April 11, 2020): 347–54. http://dx.doi.org/10.1108/sr-11-2019-0289.

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Purpose The purpose of this paper is the development and study of capacitive coupling electrodes with the ability to monitor the quality of the skin–electrode contact in the process of electrocardiogram (ECG) diagnostics. The study’s scope embraces experimental identification of distortions contributed into the recorded ECG signal at various degrees of disturbance of the skin–electrode contact. Design/methodology/approach A capacitive coupling electrode is designed and manufactured. A large number of experiments was carried out to record ECG signals with different quality of the skin–electrode contact. Using spectral analysis, the characteristic distortions of the ECG signals in the event of contact disturbance are revealed. Findings It was found that the violation of the skin–electrode contact leads to significant deterioration in the recorded signal. In this case, the most severe distortions appear with various violations of the skin–electrode contact of two sensors in one lead. It has been experimentally shown that the developed sensor allows monitoring the quality of the contact, and therefore, improvement of the quality of signal registration, enabled by the use of bespoke processing algorithms. Practical implications These sensors will be used in personalized medicine devices and tele-ECG devices. Originality/value In this work, authors studied the effect of the skin–electrode contact of a capacitive electrode with the body on the quality of the recorded ECG signal. Based on the studies, the necessity of monitoring contact was shown to improve the quality of diagnostics provided by personalized medicine devices; the capacitive sensor with contact feedback was developed.

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Keshishzadeh, Sarineh, Markus Garrett, and Sarah Verhulst. "Towards Personalized Auditory Models: Predicting Individual Sensorineural Hearing-Loss Profiles From Recorded Human Auditory Physiology." Trends in Hearing 25 (January 2021): 233121652098840. http://dx.doi.org/10.1177/2331216520988406.

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Over the past decades, different types of auditory models have been developed to study the functioning of normal and impaired auditory processing. Several models can simulate frequency-dependent sensorineural hearing loss (SNHL) and can in this way be used to develop personalized audio-signal processing for hearing aids. However, to determine individualized SNHL profiles, we rely on indirect and noninvasive markers of cochlear and auditory-nerve (AN) damage. Our progressive knowledge of the functional aspects of different SNHL subtypes stresses the importance of incorporating them into the simulated SNHL profile, but has at the same time complicated the task of accomplishing this on the basis of noninvasive markers. In particular, different auditory-evoked potential (AEP) types can show a different sensitivity to outer-hair-cell (OHC), inner-hair-cell (IHC), or AN damage, but it is not clear which AEP-derived metric is best suited to develop personalized auditory models. This study investigates how simulated and recorded AEPs can be used to derive individual AN- or OHC-damage patterns and personalize auditory processing models. First, we individualized the cochlear model parameters using common methods of frequency-specific OHC-damage quantification, after which we simulated AEPs for different degrees of AN damage. Using a classification technique, we determined the recorded AEP metric that best predicted the simulated individualized cochlear synaptopathy profiles. We cross-validated our method using the data set at hand, but also applied the trained classifier to recorded AEPs from a new cohort to illustrate the generalizability of the method.

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Marano, Stefano, Marco Marano, and Leandro Pecchia. "Frontiers in hemodialysis part II: Toward personalized and optimized therapy." Biomedical Signal Processing and Control 61 (August 2020): 102029. http://dx.doi.org/10.1016/j.bspc.2020.102029.

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Zhang, Long, Lijuan Zhao, Binghuan Cai, Jinwen Yang, Wenbing Tu, Hao Zhang, and Yi Lu. "Novel FEM-Based Wavelet Bases and Their Contextualized Applications to Bearing Fault Diagnosis." Machines 10, no. 6 (June 1, 2022): 440. http://dx.doi.org/10.3390/machines10060440.

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Feature extraction herein refers to using an appropriate wavelet basis to filter vibration signals with the aim to reveal fault transient characteristics, which underlies bearing fault diagnosis. Wavelet transform has developed into a well-established signal processing approach with wide applications in bearing fault diagnosis. Nevertheless, a suitable wavelet basis is essential for wavelet transform to perform its best. So far, numerous wavelet bases are available for bearing diagnosis, most of which, however, have a waveform analogous to that of impulse responses of a single-degree-of-freedom system. In fact, bearings are of multi-degree-of-freedom and not totally rigid. Furthermore, a specific wavelet basis is definitely unable to accommodate all bearing vibrations, given that fault characteristics vary with bearings’ operating conditions and fault types. As such, a simulated wavelet-driven personalized scheme is proposed to improve bearing fault diagnosis for contextualized engineering practical applications. For a specific bearing of interest, personalized finite element models (FEM) with various faults are constructed and corresponding fault-induced responses are then obtained. Afterward, FEM-based wavelet bases are formulated and specified by its discrete values from such responses. Taking NU306 bearing with inner or outer defect for example, FEM-based wavelet basis is applied to the corresponding experimental signals by means of wavelet filtering. The comparisons with adaptive Morlet and impulse wavelet demonstrate that the personalized FEM-based wavelet basis match very well with the fault-induced transients present in experimental bearing vibrations and thus have a promising superiority and expandability.

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Zhong, Mingxia, and Rongtao Ding. "Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering." International Journal of Circuits, Systems and Signal Processing 16 (January 7, 2022): 122–31. http://dx.doi.org/10.46300/9106.2022.16.16.

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At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

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Yin, Wenfeng, Xiuzhu Yang, Lei Li, Lin Zhang, Nattapong Kitsuwan, Ryoichi Shinkuma, and Eiji Oki. "Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar." Biomedical Signal Processing and Control 47 (January 2019): 75–87. http://dx.doi.org/10.1016/j.bspc.2018.08.002.

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Zambrana-Vinaroz, David, Jose Maria Vicente-Samper, and Jose Maria Sabater-Navarro. "Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings." Sensors 22, no. 8 (April 9, 2022): 2900. http://dx.doi.org/10.3390/s22082900.

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Epilepsy is a chronic disease with a significant social impact, given that the patients and their families often live conditioned by the possibility of an epileptic seizure and its possible consequences, such as accidents, injuries, or even sudden unexplained death. In this context, ambulatory monitoring allows the collection of biomedical data about the patients’ health, thus gaining more knowledge about the physiological state and daily activities of each patient in a more personalized manner. For this reason, this article proposes a novel monitoring system composed of different sensors capable of synchronously recording electrocardiogram (ECG), photoplethysmogram (PPG), and ear electroencephalogram (EEG) signals and storing them for further processing and analysis in a microSD card. This system can be used in a static and/or ambulatory way, providing information about the health state through features extracted from the ear EEG signal and the calculation of the heart rate variability (HRV) and pulse travel time (PTT). The different applied processing techniques to improve the quality of these signals are described in this work. A novel algorithm used to compute HRV and PTT robustly and accurately in ambulatory settings is also described. The developed device has also been validated and compared with other commercial systems obtaining similar results. In this way, based on the quality of the obtained signals and the low variability of the computed parameters, even in ambulatory conditions, the developed device can potentially serve as a support tool for clinical decision-taking stages.

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Wang, Ya. "User Behavior Identification and Personalized Recommendation Based on Web Data Mining." International Journal of Circuits, Systems and Signal Processing 15 (July 20, 2021): 643–50. http://dx.doi.org/10.46300/9106.2021.15.72.

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A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

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Sun, Chenxi, Shenda Hong, Moxian Song, Junyuan Shang, and Hongyan Li. "Personalized vital signs control based on continuous action-space reinforcement learning with supervised experience." Biomedical Signal Processing and Control 69 (August 2021): 102847. http://dx.doi.org/10.1016/j.bspc.2021.102847.

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Sun, Xue, Jie Xiong, Chao Feng, Wenwen Deng, Xudong Wei, Dingyi Fang, and Xiaojiang Chen. "Earmonitor." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (December 21, 2022): 1–22. http://dx.doi.org/10.1145/3569472.

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Earphones are emerging as the most popular wearable devices and there has been a growing trend in bringing intelligence to earphones. Previous efforts include adding extra sensors (e.g., accelerometer and gyroscope) or peripheral hardware to make earphones smart. These methods are usually complex in design and also incur additional cost. In this paper, we present Earmonitor, a low-cost system that uses the in-ear earphones to achieve sensing purposes. The basic idea behind Earmonitor is that each person's ear canal varies in size and shape. We therefore can extract the unique features from the ear canal-reflected signals to depict the personalized differences in ear canal geometry. Furthermore, we discover that the signal variations are also affected by the fine-grained physiological activities. We can therefore further detect the subtle heartbeat from the ear canal reflections. Experiments show that Earmonitor can achieve up to 96.4% Balanced Accuracy (BAC) and low False Acceptance Rate (FAR) for user identification on a large-scale data of 120 subjects. For heartbeat monitoring, without any training, we propose signal processing schemes to achieve high sensing accuracy even in the most challenging scenarios when the target is walking or running.

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Geng, Duyan, Zhaoxu Qin, Jiaxing Wang, Zeyu Gao, and Ning Zhao. "Personalized recognition of wake/sleep state based on the combined shapelets and K-means algorithm." Biomedical Signal Processing and Control 71 (January 2022): 103132. http://dx.doi.org/10.1016/j.bspc.2021.103132.

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Roy, Biplab, and Rajarshi Gupta. "MoDTRAP: Improved heart rate tracking and preprocessing of motion-corrupted photoplethysmographic data for personalized healthcare." Biomedical Signal Processing and Control 56 (February 2020): 101676. http://dx.doi.org/10.1016/j.bspc.2019.101676.

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Xu, Jie, Linqi Song, James Y. Xu, Gregory J. Pottie, and Mihaela van der Schaar. "Personalized Active Learning for Activity Classification Using Wireless Wearable Sensors." IEEE Journal of Selected Topics in Signal Processing 10, no. 5 (August 2016): 865–76. http://dx.doi.org/10.1109/jstsp.2016.2553648.

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Bernard, Francis, Clare Gallagher, Donald Griesdale, Andreas Kramer, Mypinder Sekhon, and Frederick A. Zeiler. "The CAnadian High-Resolution Traumatic Brain Injury (CAHR-TBI) Research Collaborative." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 47, no. 4 (March 16, 2020): 551–56. http://dx.doi.org/10.1017/cjn.2020.54.

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ABSTRACT:In traumatic brain injury (TBI), future integration of multimodal monitoring of cerebral physiology and high-frequency signal processing techniques, with advanced neuroimaging, proteomic and genomic analysis, provides an opportunity to explore the molecular pathways involved in various aspects of cerebral physiologic dysfunction in vivo. The main issue with early and rapid discovery in this field of personalized medicine is the expertise and complexity of data involved. This brief communication highlights the CAnadian High-Resolution Traumatic Brain Injury (CAHR-TBI) Research Collaborative, which has been formed from centers with specific expertise in the area of high-frequency physiologic monitoring/processing, and outlines its objectives.

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Zhang, Qiang, William H. Clark, Jason R. Franz, and Nitin Sharma. "Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion." Biomedical Signal Processing and Control 71 (January 2022): 103100. http://dx.doi.org/10.1016/j.bspc.2021.103100.

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Suga, N., H. Niwa, I. Taniguchi, and D. Margoliash. "The personalized auditory cortex of the mustached bat: adaptation for echolocation." Journal of Neurophysiology 58, no. 4 (October 1, 1987): 643–54. http://dx.doi.org/10.1152/jn.1987.58.4.643.

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1. In the mustached bat, Pteronotus parnellii, the "resting" frequency of the constant-frequency component of the second harmonic (CF2) of the orientation sound (biosonar signal) is different among individuals within a range from 59.69 to 63.33 kHz. The standard deviation of CF2 resting frequency is 0.091 kHz on the average for individual bats. The male's CF2 resting frequency (61.250 +/- 0.534 kHz, n = 58) is 1.040 kHz lower than the female's (62.290 +/- 0.539 kHz, n = 58) on the average. Females' resting frequencies measured in December are not different from those measured in April when almost all of them are pregnant. Therefore, the orientation sound is sexually dimorphic. 2. In the DSCF (Doppler-shifted CF processing) area of the auditory cortex, tonotopic representation differs among individual bats. The higher the CF2 resting frequency of the bat's own sound, the higher the frequencies represented in the DSCF area of that bat. There is a unique match between the tonotopic representation and the CF2 resting frequency. This match indicates that the auditory cortex is "personalized" for echolocation and that the CF2 resting frequency is like a signature of the orientation sound. 3. If a bat's resting frequency is normalized to 61.00 kHz, the DSCF area overrepresents 60.6-62.3 kHz. The central region of this overrepresented band is 61.1-61.2 kHz. This focal band matches the "reference" frequency to which the CF2 frequency of a Doppler-shifted echo is stabilized by Doppler-shift compensation. 4. Since DSCF neurons are extraordinarily sharply tuned in frequency, the personalization of the auditory cortex or system is not only suited for the detection of wing beats of insects, but also for the reduction of the masking effect on echolocation of consepecific's biosonar signals. 5. Because the orientation sound is sexually dimorphic and the auditory cortex is personalized, the tonotopic representation of the auditory cortex is also sexually dimorphic.

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Koh, Dong-Woo, Jin-Kook Kwon, and Sang-Goog Lee. "Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals." Sensors 21, no. 13 (July 5, 2021): 4607. http://dx.doi.org/10.3390/s21134607.

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Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.

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Li, Yinsheng, and Wei Zheng. "Emotion Recognition and Regulation Based on Stacked Sparse Auto-Encoder Network and Personalized Reconfigurable Music." Mathematics 9, no. 6 (March 10, 2021): 593. http://dx.doi.org/10.3390/math9060593.

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Music can regulate and improve the emotions of the brain. Traditional emotional regulation approaches often adopt complete music. As is well-known, complete music may vary in pitch, volume, and other ups and downs. An individual’s emotions may also adopt multiple states, and music preference varies from person to person. Therefore, traditional music regulation methods have problems, such as long duration, variable emotional states, and poor adaptability. In view of these problems, we use different music processing methods and stacked sparse auto-encoder neural networks to identify and regulate the emotional state of the brain in this paper. We construct a multi-channel EEG sensor network, divide brainwave signals and the corresponding music separately, and build a personalized reconfigurable music-EEG library. The 17 features in the EEG signal are extracted as joint features, and the stacked sparse auto-encoder neural network is used to classify the emotions, in order to establish a music emotion evaluation index. According to the goal of emotional regulation, music fragments are selected from the personalized reconfigurable music-EEG library, then reconstructed and combined for emotional adjustment. The results show that, compared with complete music, the reconfigurable combined music was less time-consuming for emotional regulation (76.29% less), and the number of irrelevant emotional states was reduced by 69.92%. In terms of adaptability to different participants, the reconfigurable music improved the recognition rate of emotional states by 31.32%.

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Anagnostopoulos, I., C. N. Anagnostopoulos, E. Vlachogiannis, D. Gavalas, and G. Tsekouras. "Adaptive and personalized multimedia content delivery for disabled users in Internet TV." Signal, Image and Video Processing 4, no. 3 (January 27, 2009): 273–87. http://dx.doi.org/10.1007/s11760-009-0103-x.

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Jung, Alexander, and Pedro H. J. Nardelli. "An Information-Theoretic Approach to Personalized Explainable Machine Learning." IEEE Signal Processing Letters 27 (2020): 825–29. http://dx.doi.org/10.1109/lsp.2020.2993176.

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Chiang, Hsiu-Sen, and Tien-Chi Huang. "User-adapted travel planning system for personalized schedule recommendation." Information Fusion 21 (January 2015): 3–17. http://dx.doi.org/10.1016/j.inffus.2013.05.011.

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Wallace, M., K. Karpouzis, G. Stamou, G. Moschovitis, S. Kollias, and C. Schizas. "The electronic road: personalized content browsing." IEEE Multimedia 10, no. 4 (October 2003): 49–59. http://dx.doi.org/10.1109/mmul.2003.1237550.

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Patrikakis, Charalampos, Nikolaos Papaoulakis, Panagiotis Papageorgiou, Aristodemos Pnevmatikakis, Paul Chippendale, Mario Nunes, Rui Santos Cruz, Stefan Poslad, and Wang Zhenchen. "Personalized Coverage of Large Athletic Events." IEEE Multimedia 18, no. 4 (April 2011): 18–29. http://dx.doi.org/10.1109/mmul.2010.69.

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Horiguchi, Shota, Sosuke Amano, Makoto Ogawa, and Kiyoharu Aizawa. "Personalized Classifier for Food Image Recognition." IEEE Transactions on Multimedia 20, no. 10 (October 2018): 2836–48. http://dx.doi.org/10.1109/tmm.2018.2814339.

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Kim, Ho-Sook, and Hwan-Seung Yong. "Personalized cache management for mobile computing environments." Information Processing Letters 87, no. 4 (August 2003): 221–28. http://dx.doi.org/10.1016/s0020-0190(03)00352-1.

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Shyam Kumar, Prashanth, Mouli Ramasamy, and Vijay K. Varadan. "Evaluation of Signal Quality from a Wearable Phonocardiogram (PCG) Device and Personalized Calibration." Electronics 11, no. 17 (August 25, 2022): 2655. http://dx.doi.org/10.3390/electronics11172655.

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Abstract:

Currently, the only clinically utilized Phonocardiogram (PCG) is an electronic stethoscope used in a hospital or clinical environment. The availability of continuously recorded PCGs can provide a new avenue of research into chronic disease management at home. Researchers have proposed such wearable PCG devices. However, limitations exist in evaluating such devices as PCG recording devices in home-like environments. Here, we evaluate a wearable PCG system in a belt-type form factor with an embedded force sensor, accelerometer, and a single lead ECG to study the feasibility of acquiring diagnostic-grade PCGs while the wearer performs daily activities. We describe qualitative and quantitative exploratory analysis methods for cross-subject comparison of PCG signal quality, wearer comfort, and the impact of activities using Signal-to-Noise (SNR) comparisons and cross-spectral coherence between activity and PCG. The analysis of the data suggests that a common user-chosen method of donning a wearable PCG is not applicable across subjects for obtaining optimal PCG recording quality. We propose a method to calibrate wearable PCG devices using an embedded force sensor and by following a protocol involving feedback from the embedded force sensor to determine the optimal method of wearing the device. Following a similar path to precision medicine using genomic data and the extrapolation of risk, wearable devices with healthcare applications should be developed with the ability to be adapted and calibrated to each individual. In the immediate future this may involve calibration procedures such as those followed in this work, using controlled measurements performed with each patient to tune a device for them.

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Bassani, Elias, and Gabriella Pasi. "A multi-representation re-ranking model for Personalized Product Search." Information Fusion 81 (May 2022): 240–49. http://dx.doi.org/10.1016/j.inffus.2021.11.010.

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Chow, Lawrence, Nicholas Bambos, Alex Gilman, and Ajay Chander. "Personalized Monitors for Real-Time Detection of Physiological States." International Journal of E-Health and Medical Communications 5, no. 4 (October 2014): 1–19. http://dx.doi.org/10.4018/ijehmc.2014100101.

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The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.

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Kosmopoulos, D. I., A. Doulamis, A. Makris, N. Doulamis, S. Chatzis, and S. E. Middleton. "Vision-based production of personalized video." Signal Processing: Image Communication 24, no. 3 (March 2009): 158–76. http://dx.doi.org/10.1016/j.image.2008.12.010.

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Wallace, M., K. Karpouzis, G. Stamou, G. Moschovitis, S. Kollias, and C. Schizas. "Correction "The electronic road: personalized content browsing"." IEEE Multimedia 11, no. 1 (January 2004): 53. http://dx.doi.org/10.1109/mmul.2004.1261107.

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Li, Qing, and Yuanzhu Peter Chen. "Personalized text snippet extraction using statistical language models." Pattern Recognition 43, no. 1 (January 2010): 378–86. http://dx.doi.org/10.1016/j.patcog.2009.06.003.

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Maghsudi, Setareh, Andrew Lan, Jie Xu, and Mihaela van der Schaar. "Personalized Education in the Artificial Intelligence Era: What to Expect Next." IEEE Signal Processing Magazine 38, no. 3 (May 2021): 37–50. http://dx.doi.org/10.1109/msp.2021.3055032.

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YANG, Tan, Yi-dong CUI, and Yue-hui JIN. "BPR-UserRec: a personalized user recommendation method in social tagging systems." Journal of China Universities of Posts and Telecommunications 20, no. 1 (February 2013): 122–28. http://dx.doi.org/10.1016/s1005-8885(13)60018-7.

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Lim, Hyungjun, Younggwan Kim, Jahyun Goo, and Hoirin Kim. "Interlayer Selective Attention Network for Robust Personalized Wake-Up Word Detection." IEEE Signal Processing Letters 27 (2020): 126–30. http://dx.doi.org/10.1109/lsp.2019.2959902.

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Jiang, Dan-yang, and Hong-hui Chen. "Cohort-based personalized query auto-completion." Frontiers of Information Technology & Electronic Engineering 20, no. 9 (September 2019): 1246–58. http://dx.doi.org/10.1631/fitee.1800010.

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Wang, Hee-Lin, Jian-Gang Wang, and Wei-Yun Yau. "Automated age regression for personalized IPTV services." Signal Processing: Image Communication 26, no. 7 (August 2011): 390–99. http://dx.doi.org/10.1016/j.image.2011.03.004.

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Liu, Hongzhi, Yingpeng Du, and Zhonghai Wu. "AEM: Attentional Ensemble Model for personalized classifier weight learning." Pattern Recognition 96 (December 2019): 106976. http://dx.doi.org/10.1016/j.patcog.2019.106976.

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