## 1 Introduction  
As a result of concurrent developments in healthcare and technology, artificial intelligence technologies have made exceptional contributions to improving healthcare quality. For instance, in medical imaging, deep learning models have been developed for predicting bladder cancer [1] or other urinary tract tumors [2]. Relevant studies have demonstrated that these models can lessen excessive diagnoses and treatment sessions, alleviate pain in patients, and reduce unnecessary treatment costs. Regarding clinical studies on voiding dysfunction, according to the statistics compiled by [PERSON] et al. [3], the prevalence of voiding dysfunction increases with age. The incidence of storage and voiding male lower -uninary tract symptoms report [4] about 51% and 26%, respectively. Interestingly, it is 18% of men who reported the combination of storage and voiding symptoms. Typical symptoms of voiding dysfunction include urinary frequency, urgency, urge incontinence, obstructive sensation, and even urinary retention. These conditions could result in recurring urinary tract infections and lead to kidney failure in severe cases if not managed appropriately. In clinical practice, there are many people with voiding dysfunction, and current approaches to monitoring the voiding status of patients include voiding diary records at home or urodynamic examinations at hospitals [5]. The former is less objective and is prone to missing data, while the latter is an invasive procedure that requires costly equipment, thus reducing the frequency of measurements.  
In the context of monitoring voiding dysfunction, medical professionals expect to achieve their task goals through fully contact-free approaches [6]. Studies on the assistive monitoring of voiding dysfunction, such as by [PERSON] et al. [7], utilized ultrasound to estimate the bladder weight of male patients and analyzed the association between bladder weight and voiding dysfunction. [PERSON] et al. [8] used ultrasound to measure the detrusor wall thickness of patients. They found a correlation between the detrusor wall thickness of male patients and overactive bladder syndrome, while no such correlation was found in females. These findings were consistent with those of [PERSON] et al. [9], who investigated 128 female patients whose urodynamic examination findings were either ambiguous or whose symptoms could not be explained by the examination findings alone. After measuring the bladder wall thickness of patients through ultrasound, the authors noted a marginal correlation between bladder wall thickness and voiding dysfunction among female patients.  
In the study by [PERSON] et al. [10], only 7.2% of the 1415 female patients sought urrologic treatment for lower urinary tract symptoms. Among them, voiding symptoms were more common than storage symptoms according to urodynamic examinations. The low visiting rate of female patients might be due to the lack of a convenient and non-invasive diagnostic detection of voiding dysfunction. Similarly, because of data acquisition difficulties, this study targeted men needing assistive monitoring of voiding dysfunction to serve as participants. [PERSON] et al. [11] attached a small-size vibration sensor that transforms vibration signals into uroflow during urination to the dorsal site of the penis. The device assists in in-home monitoring of patients' voiding status. It is a new technology for repeated outpatient uroflow monitoring.  
The remaining sections of this text are as follows: Section 2 concludes the related works of machine learning technologies applied for voiding symptoms identification and dysfunction diagnoses of clinical research, Section 3 describes the materials and methodology of the proposed system, Section 4 introduces the data analysis methods and model development results and discussion, and Section 5 presents the conclusions and directions for future research.