Journal Papers

Peer-reviewed publications in international journals

A Systematic Evaluation of Feature Encoding Techniques for Gait Analysis Using Multimodal Sensory Data

Fatima, R., Khan, M.H., Nisar, M.A., Doniec, R., Farid, M.S., Grzegorzek, M.

Sensors (MDPI), 2024 , Vol. 24(1) , 75

Systematic evaluation of feature encoding techniques for gait analysis using multimodal time series sensory data, providing comprehensive benchmarks for healthcare applications.

Full Paper

A Hierarchical Multitask Learning Approach for the Recognition of Activities of Daily Living Using Data from Wearable Sensors

Nisar, M.A., Shirahama, K., Irshad, M.T., Huang, X., Grzegorzek, M.

Sensors (MDPI), 2023 , Vol. 23(19) , 8234

This paper presents a hierarchical multitask learning framework for recognizing activities of daily living using wearable sensor data, improving accuracy through task relationship modeling.

Full Paper

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec, R., Konior, J., Sieciński, S., Piet, A., Irshad, M.T., Piaseczna, N., Hasan, M.A., Li, F., Nisar, M.A., Grzegorzek, M.

Sensors (MDPI), 2023 , Vol. 23(12) , 5551

Novel approach using electrooculographic (EOG) signals and convolutional neural networks for classifying primary and secondary driver activities to enhance road safety.

Full Paper

Sleep Stage Classification in Children Using Self-Attention and Gaussian Noise Data Augmentation

Piaseczna, N., Doniec, R., Sieciński, S., Piet, A., Li, F., Grzegorzek, M., Nisar, M.A.

Sensors (MDPI), 2023 , Vol. 23(7) , 3446

Advanced deep learning approach for pediatric sleep stage classification using self-attention mechanisms and data augmentation techniques.

Full Paper

SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors

Irshad, M.T., Nisar, M.A., Huang, X., Hartz, J., Flak, O., Li, F., Gouverneur, P., Piet, A., Oltmanns, K.M., Grzegorzek, M.

Sensors (MDPI), 2022 , Vol. 22(20) , 7711

Novel machine learning approach for automatic hunger detection using wearable sensors, contributing to metabolic assessment and dietary monitoring applications.

Full Paper

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Amjad, F., Khan, M.H., Nisar, M.A., Farid, M.S., Grzegorzek, M.

Sensors (MDPI), 2021 , Vol. 21(7) , 2368

Comprehensive comparison of feature selection methods for human activity recognition using multimodal sensor data, providing insights into optimal feature extraction strategies.

Full Paper

Rank Pooling Approach for Wearable Sensor-Based ADLs Recognition

Nisar, M.A., Shirahama, K., Li, F., Huang, X., Grzegorzek, M.

Sensors (MDPI), 2020 , Vol. 20(12) , 3463

Novel rank pooling approach for recognizing activities of daily living from wearable sensor data, capturing temporal dynamics for improved classification accuracy.

Full Paper

Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification

Li, F., Shirahama, K., Nisar, M.A., Huang, X., Grzegorzek, M.

Sensors (MDPI), 2020 , Vol. 20(15) , 4271

Deep transfer learning methodology for time series data analysis, leveraging sensor modality classification to improve activity recognition performance across different datasets.

Full Paper

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Li, F., Shirahama, K., Nisar, M.A., Köping, L., Grzegorzek, M.

Sensors (MDPI), 2018 , Vol. 18(2) , 679

Comprehensive comparison of deep learning and traditional feature extraction methods for human activity recognition, providing guidelines for method selection based on data characteristics.

Full Paper

Machine Learning Models for Wearable-Based Human Activity Recognition: A Comparative Study

Ciortuz, G., Pour, H.H., Irshad, M.T., Nisar, M.A., Huang, X., Fudíckar, S.

Neurocomputing, 2025 , Vol. 130911

Comprehensive comparative study of machine learning models for human activity recognition using wearable sensor data.

Full Paper
1 2