Using a deep learning method and data from two-dimensional (2D) marker-less video-based images for walking speed classification

Human body measurement data related to walking can characterize functional move ment and thereby become an important tool for health assessment. Single-camera-captured two dimensional (2D) image sequences of marker-less walking individuals might be a simple approach for estimating human body measurement data which could be used in walking speed-related health assessment. Conventional body measurement data of 2D images are dependent on body-worn garments (used as segmental markers) and are susceptible to changes in the distance between the participant and camera in indoor and outdoor settings. In this study, we propose five ratio-based body measurement data that can be extracted from 2D images and can be used to classify three walking speeds (i.e., slow, normal, and fast) using a deep learning-based bidirectional long short-term memory classification model. The results showed that average classification accuracies of 88.08% and 79.18% could be achieved in indoor and outdoor environments, respectively. Additionally, the proposed ratio-based body measurement data are independent of body-worn garments and not susceptible to changes in the distance between the walking individual and camera. As a simple but efficient technique, the proposed walking speed classification has great potential to be employed in clinics and aged care homes

Xerostomia, salivary flow, and oral health status among saudi diabetic patients: A comparative cross-sectional study

Purpose: Diabetes mellitus (DM) is associated with numerous oral complications, including frequent oral infections, periodontal diseases, hyposalivation, and xerostomia. The present study aimed to investigate salivary flow rate, xerostomia, and oral health status among a group of Saudi diabetic patients as compared to healthy controls. Patients and Methods: This comparative cross-sectional study involved 50 diabetic patients (aged between 15 and 70 years) and 53 age-and gender-matched healthy controls. Data collection was carried out using a structured questionnaire and clinical examination of oral health status, which included salivary flow rates, saliva pH, tooth loss, plaque accumula-tion, and gingival health. Independent t-tests, one-way analysis of variance (ANOVA), and chi-square tests were performed to compare between groups. Results: The results revealed a statistically significant lower salivary flow (0.33 ± 0.16 vs 0.59 ± 0.54; p = 0.002) and lower saliva pH (6.36 ± 0.49 vs 6.58 ± 0.39; p = 0.014) in diabetic patients than in the control group. A higher proportion of diabetic subjects (60%) self-reported having xerostomia compared to controls (52%), but the findings were statistically non-significant. Additionally, the results revealed slightly poorer oral health and greater tooth loss among DM patients, although the results did not attain a significant difference (P > 0.05). Conclusion: The findings of the present study demonstrate poor oral health and a high prevalence of xerostomia among Saudi diabetic patients. Oral health education should therefore be promoted in this group of patients