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Fulltext Radio frequency-based human activity dataset collected using ESP32 microcontroller in line-of-sight and non-line-of-sight indoor experiment setups

Lim ZY, Ong LY, Leow MC

Data Brief, 2024 Dec;57:111101.
PMID: 39633969 DOI: 10.1016/j.dib.2024.111101

This study presents the "ESP32 Dataset," a dataset of radio frequency (RF) data intended for human activity detection. This dataset comprises 10 activities carried out by 8 volunteers in three different indoor floor plan experiment setups. Line-of-sight (LOS) scenarios are represented by the first two experiment setups, and non-line-of-sight (NLOS) scenarios are simulated in the third experiment setup. For every activity, the volunteers performed 20 trials, hence there were 1,600 recorded trials overall per experiment setup in the sample (8 people × 10 activities × 20 trials) . In order to obtain the Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) values from the recorded transmissions, the D-Link AX3000 router and ESP32 microcontroller were used as the transmitter (Tx) and receiver (Rx) in the data collection process. This collection is an invaluable resource for academics and practitioners in the field of human activity detection since it offers rich and diversified RF data across a wide range of experiment setups and activities. In contrast to other datasets with different hardware configurations, this dataset records one RSSI value and fifty-two CSI subcarriers using the ESP-CSI Tool RF data capture tool. The number of RSSI and CSI signals, specific to the ESP32 hardware, allows for the exploration of resource-efficient activity detection algorithms, which is crucial for Internet of Things (IoT) applications where low-power and cost-effective solutions are required. This dataset is particularly valuable because it reflects the constraints and capabilities of the widely used ESP32 microcontrollers, making it highly relevant for developing and testing new algorithms tailored to IoT environments. The availability of this dataset enables the development and evaluation of activity detection algorithms and methodologies, enhancing the potential for improved experimental setups in IoT applications.
Working memory and literacy as predictors of performance on algebraic word problems

Lee K, Ng SF, Ng EL, Lim ZY

J Exp Child Psychol, 2004 Oct;89(2):140-58.
PMID: 15388303 DOI: 10.1016/j.jecp.2004.07.001

Previous studies on individual differences in mathematical abilities have shown that working memory contributes to early arithmetic performance. In this study, we extended the investigation to algebraic word problem solving. A total of 151 10-year-olds were administered algebraic word problems and measures of working memory, intelligence quotient (IQ), and reading ability. Regression results were consistent with findings from the arithmetic literature showing that a literacy composite measure provided greater contribution than did executive function capacity. However, a series of path analyses showed that the overall contribution of executive function was comparable to that of literacy; the effect of executive function was mediated by that of literacy. Both the phonological loop and the visual spatial sketchpad failed to contribute directly; they contributed only indirectly by way of literacy and performance IQ, respectively.
Bayesian optimization driven strategy for detecting credit card fraud with Extremely Randomized Trees

Lim ZY, Pang YH, Kamarudin KZB, Ooi SY, Hiew FS

MethodsX, 2024 Dec;13:103055.
PMID: 39640389 DOI: 10.1016/j.mex.2024.103055

Credit card usage has surged, heightening concerns about fraud. To address this, advanced credit card fraud detection (CCFD) technology employs machine learning algorithms to analyze transaction behavior. Credit card data's complexity and imbalance can cause overfitting in conventional models. We propose a Bayesian-optimized Extremely Randomized Trees via Tree-structured Parzen Estimator (TP-ERT) to detect fraudulent transactions. TP-ERT uses higher randomness in split points and feature selection to capture diverse transaction patterns, improving model generalization. The performance of the model is assessed using real-world credit card transaction data. Experimental results demonstrate the superiority of TP-ERT over the other CCFD systems. Furthermore, our validation exhibits the effectiveness of TPE compared to other optimization techniques with higher F1 score.•The optimized Extremely Randomized Trees model is a viable artificial intelligence tool for detecting credit card fraud.•Model hyperparameter tuning is conducted using Tree-structured Parzen Estimator, a Bayesian optimization strategy, to efficiently explore the hyperparameter space and identify the best combination of hyperparameters. This facilitates the model to capture intricate patterns in the transactions, resulting in enhanced model performance.•The empirical findings exhibit that the proposed approach is superior to the other machine learning models on a real-world credit card transaction dataset.
Patient-reported outcome measures among breast cancer survivors: A cross-sectional comparison between Malaysia and high-income countries

Rajaram N, Lim ZY, Song CV, Kaur R, Mohd Taib NA, Muhamad M, et al.

Psychooncology, 2019 01;28(1):147-153.
PMID: 30346074 DOI: 10.1002/pon.4924

OBJECTIVES: Patient-reported outcomes (PROs) in high-income countries (HICs) suggest that physical, emotional, and psychological needs are important in cancer care. To date, there have been few inconsistent descriptions of PROs in low-income and middle-income Asian countries. Using a standard questionnaire developed by the International Consortium for Health Outcomes Measurement (ICHOM), we compared the perceived importance of PROs between patients in Malaysia and those in HICs and between clusters of Malaysian women.
METHODS: Breast cancer patients were recruited from three Malaysian hospitals between June and November 2017. We compared the proportion of patients who rated PROs as very important (scored 7-9 on a 9-point Likert scale) between Malaysian patients and data collected from patients in HICs via the ICHOM questionnaire development process, using logistic regression. A two-step cluster analysis explored differences in PROs among Malaysian patients.
RESULTS: The most important PROs for both cohorts were survival, overall well-being, and physical functioning. Compared with HIC patients (n = 1177), Malaysian patients (n = 969) were less likely to rate emotional (78% vs 90%), cognitive (76% vs 84%), social (72% vs 81%), and sexual (30% vs 56%) functioning as very important outcomes (P