赵一帆

EEG Signal Processing Techniques and Applications

Neural network-based parametric system identification: a review

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data

Three‐stage feature selection approach for deep learning‐based RUL prediction methods

Compound Uncertainty Quantification and Aggregation for Reliability Assessment in Industrial Maintenance

Classification of barely visible impact damage in composite laminates using deep learning and pulsed thermographic inspection

A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems

The role of wrist-worn technology in the management of Parkinson’s disease in daily life: A narrative review

Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.

The Evaluations of the Impact of the Pilot’s Visual Behaviours on the Landing Performance by Using Eye Tracking Technology

Human-machine interaction for automated vehicles

Human-Machine Interaction for Automated Vehicles: Driver Status Monitoring and the Takeover Process explains how to design an intelligent human-machine interface by characterizing driver behavior before and during the takeover process. Multiple solutions are presented to accommodate different sensing technologies, driving environments and driving styles. Depending on the availability and location of the camera, the recognition of driving and non-driving tasks can be based on eye gaze, head movement, hand gesture or a combination. Technical solutions to recognize drivers various behaviors in adaptive automated driving are described with associated implications to the driving quality. Finally, cutting-edge insights to improve the human-machine-interface design for safety and driving efficiency are also provided, based on the use of this sensing capability to measure drivers’ cognition capability.