Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

With the development of industrial automation and intelligent manufacturing, steel surface defect detection has become a critical step in ensuring product quality and production efficiency. However, the diverse types and significant size variations of defects on steel surfaces pose great challenges. Among these defects, small-sized defects are characterized by their subtle appearance on the surface, making them difficult to distinguish from the background. This often results in high false detection and missed detection rates during the inspection process. To address this issue, this paper proposes an improved steel surface defect detection algorithm based on YOLOv9. First, introducing Depthwise Separable Convolution (DSConv) can effectively reduce the computational complexity of the model, thereby enhancing its operational efficiency. Second, the C3 module is incorporated to effectively fuse feature maps from different levels, enhancing the model’s ability to detect multi-scale targets. To improve the recognition accuracy of small targets, a bidirectional feature pyramid network (BiFPN) is integrated, enabling the model to capture small target features more precisely. Additionally, the DySample upsampling operator is employed to address the issue of detail loss in traditional upsampling methods, enhancing the model’s sensitivity and localization accuracy for small targets. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 78.2% and an accuracy of 82.5%, representing increases of 1.8% and 7.4%, respectively, compared to the baseline model. Moreover, the number of model parameters is reduced by 8.9%. The findings of this study hold significant practical value for improving the quality and efficiency of industrial products in the field of steel surface defect detection.

For a population that is moving towards an elderly stage of development, Parkinson's disease (PD) is characterized in the second place for the most common chronic progressive neurodegenerative illness in the world after Alzheimer's disease, which regularly affects older generation. In the next 30 years, this amount is estimated to double due to the increase in the number of ageing people, as age is the leading key risk feature for the start of PD. There are a variety of medications, such as levodopa available to treat PD. With the latest advancement in healthcare technology, current researches permit the monitoring of PD with the application of wearable sensor technology. From previous studies, researchers have realized the application of wearable sensors as a useful tool that had the capability to differentiate various types of PD symptoms using uni-modal sensor or bi-modal sensors (accelerometer and gyroscope). Therefore, early diagnosis of PD through multimodal wearable technology can be considered for this aim. In this paper, the data are collected using on-body triaxial wearable sensors (accelerometer, gyroscope and magnetometer) for classifying people with Parkinson (PWP) from healthy controls. The system performance was characterized based on 10-fold cross validation method, applying the proposed time and frequency domain features and classification algorithms. The strength of the proposed method has been evaluated through several performance measures. In summary, these results show that the proposed machine learning techniques had ability in differentiating PWP from healthy controls with highest average accuracy, sensitivity, specificity and ROC of above 88%.

Freezing of gait (FoG) is a general and disabling indicator during the severe level of Parkinson's disease (PD) that affects millions of PD patients worldwide. Under episodic condition that cannot be predicted, FoG influences gait in term of delay and sudden inability to perform walking. FoG does not respond well to treatment through medication; therefore effective non-medication assistance is necessary. A wearable assistance system for FoG detection has been developed to assist patients in order to proceed walking. However, current objective evaluations for automated FoG detection are not sufficient as only the standard time-based and frequency-based features were extracted and there are still spaces for improvement in term of FoG detection performance. In this paper, we first attempt to adapt and extend current robust feature extraction and inference techniques in order to include additional features compared to the currently existing features. Then we go a step further by applying feature selection with the purpose of obtaining the maximum recognition results using the current available DAPHNet dataset. This dataset was collected using a wearable health assistive system that consists of 3-axes accelerometer to measure patient's movement. Ten PD patients were chosen to perform several walking tasks under laboratory environment. The overall performance was evaluated via subject-dependent and subject independent using the proposed feature extraction, feature selection and classification algorithms. The outcomes showed that the suggested machine learning methods had the ability in detecting FoG with maximum mean accuracy, sensitivity, specificity and area under curve (AUC) of approximately 99%.