Sensor-Based Human Activity Recognition for Assistive Health Technologies

Nisar, M.A.

Logos Verlag Berlin GmbH, 2023, Berlin, Germany

Comprehensive work on sensor-based human activity recognition systems for assistive health technologies and healthcare applications.

Wearable-Based Human Flow Experience Recognition Enhanced by Transfer Learning Methods Using Emotion Data

Irshad, M.T., Li, F., Nisar, M.A., Huang, X., Buss, M., Kloep, L., Peifer, C.

Computers in Biology and Medicine, 2023, Vol. 166, 107489

Transfer learning approach for recognizing human flow experience using wearable sensors and emotion data for virtual team environments.

Multi-Task Learning for Activity Recognition Using Sensor Data

Nisar, M.A., Shirahama, K., Grzegorzek, M.

International Conference on Pattern Recognition (ICPR), 2022, Montreal, Canada, pp. 445-452

Novel multi-task learning framework leveraging relationships between different activities to improve recognition accuracy in wearable sensor applications.

Deep Learning Approaches for Hunger Detection Using Wearable Sensors

Irshad, M.T., Nisar, M.A., Huang, X., Li, F., Grzegorzek, M.

IEEE EMBS International Conference on Biomedical and Health Informatics, 2021, Virtual Conference, pp. 112-120

Investigation of deep learning architectures for automatic hunger detection from physiological signals captured by wearable devices for metabolic assessment.

Temporal Modeling for Activity Recognition Using Rank Pooling

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

International Conference on Multimedia Modeling, 2020, Daejeon, Korea, pp. 567-574

Rank pooling methodology for capturing temporal dynamics in sensor data for improved activity recognition performance in real-world scenarios.

Transfer Learning for Sensor-Based Human Activity Recognition

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

International Conference on Ubiquitous Computing, 2019, London, UK, pp. 891-898

Transfer learning framework addressing domain adaptation challenges in cross-dataset human activity recognition scenarios using deep neural networks.

Additional Conference Publications

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