Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion differentiation through features of eye-tracking and pupil diameter for monitoring well-being

Emotions are an important contributor to human self-expression and well-being. However, many populations express their emotions differently from what is considered "typical". Previous literature has indicated a possible relationship between emotion and eye-movement. The objective of this paper is to further explore this proposed relationship by identifying specific features of eye-movement that relate to six emotion categories: joy, surprise, indifference, disgust, sadness, and fear. Features of eye-movement are extracted from measurements of pupil diameter, saccades, and fixations. These measurements are collected as participants view images from the International Affective Picture System, a validated image deck used to evoke known levels of pleasure, arousal, and dominance. Example features of eye-movement measurements such as pupil diameter include maximum or minimum values, means, and standard deviations. Statistical analyses indicate that the extracted features of eye-tracking in this paper can identify fear and sadness with relative accuracy, while more work is needed to differentiate among joy, indifference, disgust, and surprise. Future work aims to understand differences between typically developing populations such as the individuals included in this analysis, and clinical populations such as individuals with cerebral palsy.Clinical Relevance— This pilot study suggests a link between emotion and features of eye-movement. The information will later be used to develop assistive communication devices that better meet the self-expression needs of individuals with motor and communication challenges.
Prescriptive Method for Optimizing Cost of Data Collection and Annotation in Machine Learning of Clinical Ultrasound

Machine learning in medical ultrasound faces a major challenge: the prohibitive costs of producing and annotating clinical data. Optimizing the data collection and annotation will improve model training efficiency, reducing project cost and times. This paper prescribes a 2-phase method for cost optimization based on iterative accuracy/sample size predictions, and active learning for annotation optimization. Methods: Using public breast, fetal, and lung ultrasound datasets we can: Optimize data collection by statistically predicting accuracy for a desired dataset size; and optimize labeling efficiency using Active Learning, where predictions with lowest certainty were labelled manually using feedback. A practical case study on BUSI data was used to demonstrate the method prescribed in this work. Results: With small data subsets, ~10%, dataset size vs. final accuracy relations can be predicted with diminishing results after 50% usage. Manual annotation was reduced by ~10% using active learning to focus the annotation. Conclusion: This led to cost reductions of 50%-66%, depending on requirements and initial cost model, on BUSI dataset with a negligible accuracy drop of 3.75% from theoretical maximums.Clinical Relevance— This work provides methodology to optimize dataset size and manual data labelling, this allows generation of cost-effective datasets, of interest to all, but particularly for financially limited trials and feasibility studies, Reducing the time burden on annotating clinicians.
Personalized Federated Learning for Institutional Prediction Model using Electronic Health Records: A Covariate Adjustment Approach

Federated learning (FL) has attracted attention as a technology that allows multiple medical institutions to collaborate on AI without disclosing each other's patient data. However, FL has the challenge of being unable to robustly learn when the data of participating clients is non-independently and non-identically distributed (Non-IID). Personalized Federated Learning (PFL), which constructs a personalized model for each client, has been proposed as a solution to this problem. However, conventional PFL methods do not ensure the interpretability of personalization, specifically, the identification of which data samples are contributed to each personalized learning, which is important for AI in medical applications. In this study, we propose a novel PFL framework, Federated Adjustment of Covariate (FedCov), which acquires a propensity score model representing the covariate shift among clients through prior FL, then learns a final model by weighting the contribution of each training sample to PFL based on the estimated propensity score. This approach enables both the learning of personalized models through covariate adjustment and the visualization of the contribution of each client to PFL. FedCov was evaluated in the prediction of in-hospital mortality across 50 hospitals in the eICU Collaborative Research Database, achieving an ROC-AUC of 0.750. This result outperformed the AUCs in the 0.720-0.735 range achieved by conventional FL methods and was closest to the AUC of 0.754 achieved by centralized learning.Clinical Relevance— This study demonstrates the feasibility of providing sophisticated and personalized AI-driven clinical decision support to any medical institution through personalized federated learning.
Evaluating the partial contribution of the P3 event-related potential elicited by auditory oddball stimuli during the Stroop task

The cognitive load of a precisely timed task, such as the Stroop task, may be measured through the use of event-related potentials (ERPs). To determine the time at which cognitive load is at its peak, oddball tones may be applied at various times surrounding a cognitive task. However, we need to determine whether the simultaneous presentation of auditory and visual stimuli would mask a potential change in P3 in an ERP-producing task. If the contribution of the Stroop stimulus is too large, then Stroop ERP with oddball stimuli occurring at different timepoints may not be directly comparable across the various timepoints due to the contribution of the Stroop ERP. The aim of this study was to measure the magnitude of the difference wave between that of simultaneously presented stimuli and that of linearly added stimuli of separate responses. Participants were fitted with a dry-sensor EEG cap and were presented with a series of Stroop and auditory stimuli. For some Stroop stimuli, auditory stimuli occurred simultaneously or in a close time proximity to the Stroop stimuli. We sought to estimate the linear contribution of the ERP from Stroop and oddball stimuli. We found that the magnitude of the difference waves were 3.07 ± 1.65 μV and 2.82 ± 1.34 μV for congruent and incongruent stimuli, respectively. As the average amplitude in the P3 region for both the congruent and incongruent difference waves was lower than the magnitude of the auditory oddball presented simultaneously with Stroop stimuli (12.13 ± 1.00 μV for congruent and 11.78 ± 1.05 μV for incongruent Stroop), we expect that the contribution of P3 auditory oddball would not mask a potential Stroop effect even if the timing of the auditory oddball stimuli were experimentally manipulated, a direction that we hope to explore in future work. In conclusion, we determine this paradigm is suitable for measuring cognitive load in precisely timed tasks.Clinical Relevance— This study establishes the efficacy of presenting a Stroop task as a proxy for a cognitive challenge that could cause cognitive overload.
UML-Based Design Flow for Systems with Neural Networks

Artificial intelligence has demonstrated its ability to solve lots of critical tasks, but at the cost of high computational requirements. Different hardware has been proposed to provide this computational power, each one with its benefits and drawbacks. However, the exploration of the different alternatives in an easy an integrated way is still a complex task. To solve so, this paper proposes a UML-based design flow where neural networks are initially specified and then automatically generated and trained using TensorFlow. The approach also enables automatic mapping of models to CPU, GPU and FPGAs, using Xilinx’s Deep Learning Processor Units (DPUs). The framework also generates the communication codes required to connect the other system components with the implementation selected. This approach addresses design-space exploration challenges, system architecture definition, and improves implementation and training processes by saving time and effort.
The Classification System uses a Support Vector Machine and a Decision Tree Method Based on X-Ray Images for Spinal Abnormalities

A prevalent form of disorder that effects the vertebrae is a spinal disorder. X-ray technology is frequently used by medical professionals to detect abnormalities in the human body that are not visible to the unaided eye. Spinal ailment. Using the Hu moment invariant and machine learning, this study develops a system capable of feature extraction and spinal anomaly classification. The Hu Moment Invariant technique is used to derive seven moments (features) that describe an object. A support vector machine (SVM) identifies the optimal hyperplane in the input space that separates two classes. A decision tree (DT) is a technique for predicting the future by constructing a classification or regression model in the shape of a tree. Using the DT -Fine classification model derived from the Hu-Moment extraction results, the system can classify the newly developed research data in 1 minute with an accuracy of 88.2 % (highest) and 0.885782 seconds of feature extraction.
Assistive Completion of Agrammatic Aphasic Sentences: Amalgamation of NLP and Neurolinguistics-based Synthetic Dataset

Damage to the inferior frontal gyrus (Broca’s area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.Clinical relevance— We demonstrate the utilization of Natural Language Processing (NLP) for developing assistive technology for Aphasia patients. While disorders like Broca’s aphasia offer a small sample size of patients and data, synthetic linguistic models like ours offer extensive scope for developing assistive technology and rehabilitation monitoring.
A Low-Power High Input Range PPG Readout Amplifier with a Current Buffer Input*

This paper presents ultra-low power photoplethysmography (PPG) readout circuits. The proposed system architecture uses a current buffer between the photodiode (PD) and the transimpedance amplifier (TIA) to isolate the large parasitic capacitance of the PD leading to improves the power consumption of the TIA. A class AB topology is exploited at the output of the amplifier, which allows for increased drive capability without the use of auxiliary circuits. The maximum input current range of the TIA is 160 µA, so the large DC current of the input signal does not saturate the circuit. In the LED driver circuit, by varying the duty cycle of a pulse wave modulation (PWM) signal, the ON and OFF times of the circuits. The amplifier and LED driver are manufactured in the 130 nm TSMC CMOS process. The power consumption of the circuits with a duty cycle of 1% is 3.28 µW (at VDD = 1.2V). Clinical Relevance— Vital signs are becoming a very important research topic due to the recent prevalence of COVID-19 and other respiratory diseases. This research aims to develop and interface circuits to monitor vital signs including blood pressure, heart rate, and respiratory rate to study respiratory disease, drug safety, and efficacy.
A novel force-constrained non-negative matrix factorization algorithm reveals the effectiveness of muscle synergies in the task space*

According to the synergy hypothesis, the motor system recruits a small number of synergies in a task-dependent manner. Existing synergy extraction algorithms typically only consider the muscle pattern and it remains unclear to which extent muscle synergies encode task-relevant variations of muscle activity. We propose a novel force-constrained non-negative matrix algorithm (FCNMF) based on a gradient descent update rule that considers also the task space by adding a term penalizing force reconstruction error in the cost function. We validated the FCNMF algorithm using simulated muscle data and corrupted them by noise. We compared task performances with reconstructed trajectories using synergies (RS) extracted from the FCNMF algorithm and from the standard multiplicative non-negative matrix factorization NMF algorithm. We found that FCNMF outperforms NMF for different types of noise. Finally, we demonstrated the effectiveness of FCNMF on EMG data collected during an isometric reaching task. The new algorithm accurately reconstructs the trajectories in all participants, even in those for which the NMF algorithm fails. These findings show the effectiveness of muscle synergies extracted considering the task space, possibly thanks to the robustness of FCNMF against non-isotropic noise present in muscle data, suggesting that they provide an effective strategy for motor coordination.
Automatic Detection of Chronic Insomnia from Polysomnographic and Clinical Variables Using Machine Learning

Insomnia is defined subjectively by the presence and frequency of specific clinical symptoms and an association with distress. Although sleep study data has shown some weak associations, no objective test can currently be used to predict insomnia. The purpose of this study was to use previously reported and relatively crafted insomnia-related polysomnographic variables in machine learning models to classify groups with and without insomnia. Demographics, diagnosed depression, Epworth Sleepiness Scale (ESS), and features derived from electroencephalography (EEG), arousals, and sleep stages from 3,407 sleep clinic patients (2,617 without insomnia and 790 insomnia patients based on responses to a set of questions) were included in this analysis. The number of features were reduced using pair-wise correlation and recursive feature elimination. Predictive value of three machine learning models (logistic regression, neural network, and support vector machine) was investigated, and the best performance was achieved with logistic regression, yielding a balanced accuracy of 71%. The most important features in predicting insomnia were depression, age, sex, duration of longest arousal, ESS score, and EEG power in theta and sigma bands across all sleep stages. Results indicate potential of machine learning-based screening for insomnia using clinical variables and EEG.
Fault Diagnosis of Motor Roller Bearing Based on Relative Wavelet Energy and Decision Tree

Wavelet transform is often used in fault diagnosis of rolling bearings. However, in practical applications, the number of layers decomposed by wavelet transform needs to be manually specified. If there are too many layers, the corresponding features will also increase, which increases the difficulty of feature selection. To solve this problem, this paper proposes a fault diagnosis method based on relative wavelet energy and decision tree algorithm. This method has strong interpretability, which is mainly reflected in the following two aspects: (1) Establishing classification rules that is easy to be understood with clear tree structures. (2) The feature importance index can be used to quantify the importance of each feature and screen important features. The results show that: when the number of layers decomposed by wavelet increases to more than 6 layers, the accuracy rate will increase to more than 95%. In addition, due to the pre-pruning strategy in the decision tree, the decision tree actually uses fewer features, and the features with higher feature importance are selected when building the decision tree, which avoids the complicated tree structure and increases its interpretability.
Singaporean Conversational English-Malay Code-Switching Speech: An Analysis Based on Code-switching Points and Part -of-Speech

This paper investigates various code-switching prop-erties of conversational speech from bilingual English-Malay Singaporean speakers with data obtained from the National Speech Corpus (NSC) and provides baseline language models for various combinations between English-Malay monolingual and codeswitching transcripts. Specifically, the study analyzed the correlation between code-switching patterns and (i) trigger words and code-switched word pairs at code-switching points, and (ii) wordwise POS and pairwise POS tags. Our analysis shows there is a certain set of words that frequently “triggered” code-switching behavior, and speakers tend to code-switch more frequently around nouns. Additionally, we provide perplexities for language models built on the selected datasets. These perplexities could serve as baselines for future language models for Singaporean speech, especially, English-Malay code-switch speech.
Collision-Free Reconfiguration Planning for Variable Topology Trusses Using a Linking Invariant

We introduce a multi-modal reconfiguration planner for the Variable Topology Truss (VTT) modular robot system. The VTT system is a truss-architecture modular self-reconfigurable robot. When a VTT is restricted to a single topology, the collision constraints between the truss members divide the configuration space into many connected components, which makes collision-free planning difficult. This new planner leverages a mathematical invariant based on link theory to find topological reconfiguration actions that can connect these different regions and make progress towards a goal. We show that this planner is effective at finding paths between configurations with different truss topologies.
Somatisation Disorder Detection via Speech: Introducing a Self-Supervised Learning Model

With the depressive psychiatric disorders becoming more common, people are gradually starting to take it seriously. Somatisation disorders, as a general mental disorder, are rarely accurately identified in clinical diagnosis for its specific nature. In the previous work, speech recognition technology has been successfully applied to the task of identifying somatisation disorders on the Shenzhen Somatisation Speech Corpus. Nevertheless, there is still a scarcity of labels for somatisation disorder speech database. The current mainstream approaches in the speech recognition heavily rely on the well labelled data. Compared to supervised learning, self-supervised learning is able to achieve the same or even better recognition results while reducing the reliance on labelled samples. Moreover, self-supervised learning can generate general representations without the need for human hand-crafted features depending on the different recognition tasks. To this end, we apply self-supervised learning pre-trained models to solve few-labelled somatisation disorder speech recognition. In this study, we compare and analyse the results of three self-supervised learning models (contrastive predictive coding, wav2vec and wav2vec 2.0). The best result of wav2vec 2.0 model achieves 77.0 % unweighted average recall and is significantly better than CPC (p < .005), performing better than the benchmark of the supervised learning model.Clinical relevance— This work proposed a self-supervised learning model to resolve the few-labelled SD speech data, which can be well used for helping psychiatrists with clinical assistant to diagnosis. With this model, psychiatrists no longer need to spend a lot of time labelling SD speech data.
Reliability-Based Design of Time-Varying Electricity Tariff

Implementing price-based energy management programs to change the consumption pattern of residential consumers has become an indispensable solution to the operational challenges of smart distribution power systems. However, practical methods to design efficient time-varying prices to improve the reliability and operation of distribution systems have remained scarce. This paper proposes a bi-objective optimization problem to design time-differentiated electricity tariff for residential energy management, where a new reliability evaluation method is employed to evaluate the reliability enhancement caused by load shifting. A set of numerical studies and sensitivity analysis are conducted to investigate the effectiveness of the proposed pricing method.
Improving the Reliability of Results in Pulse Wave Processing

The need for further research on transmission pulse oximetry for various applications (clinical medicine or personal use) has been justified. We construct a database from groups of people with different health statuses and identify several factors that negatively affect the parameters of recorded pulse waves (signal-to-noise ratio, wavefront structure). Mathematical functions have been developed to obtain additional information during pulse wave processing to improve the reliability of the results. A new technique is proposed based on the pulse rate, waveform, and oxygen saturation information. It helps to get more reliable information about the presence of deviations in the state of human health during the process of express diagnostics.
Toward a Holistic Computational Representation for Sleep Quality and its Support for Explainability

Sleep quality is recognized as one of the main factors that affect human health. Thus, several studies have been encouraged to analyze features, such as stress level and female menopause, which are directly related to sleep quality. While these works rely mostly on reductionism as the philosophical framework, we approach this problem from a holist perspective, using a model with 10 features that could provide more reliable explanations for inductive conclusions. We demonstrate the principles of this hypothesis by analyzing the data regarding the day before a sleep episode of 1736 volunteers. This analysis shows, for example, the performance of each feature when they are jointly used along prediction tasks. Moreover, we evaluate the readability and accuracy of explanations, given as description logic sentences and based on a knowledge representation that considers the 10 features as elements that compose a sleep quality ontological definition.