Thailand has committed to reducing population sodium intake by 30% by 2025. However, reliable nationally representative data are unavailable for monitoring progress toward the goal. We estimated dietary sodium consumption using 24‐hour urinary analyses in a nationally representative, cross‐sectional population‐based survey. We selected 2388 adults (aged ≥ 18 years) from the North, South, North‐east, Central Regions, and Bangkok, using multi‐stage cluster sampling. Mean sodium excretion was inflated by 10% to adjust for non‐urinary sources. Multivariate logistic regression was performed to assess factors associated with sodium consumption ≥ 2000 mg. Among 1599 (67%) who completed urine collection, mean age was 43 years, 53% were female, and 30% had hypertension. Mean dietary sodium intake (mg/day) was 3636 (±1722), highest in South (4108 ± 1677), and lowest in North‐east (3316 ± 1608). Higher sodium consumption was independently associated with younger age (Adjusted Odds Ratio (AOR) 2.81; 95% Confidence interval (CI): 1.53‐5.17; p = .001); higher education (AOR 1.79; 95% CI: 1.19‐2.67; p = .005), BMI ≥ 25 (AOR 1.55; 95% CI: 1.09‐2.21; p=.016), and hypertension (AOR 1.58; 95% CI: 1.02‐2.44; p = .038). Urine potassium excretion was 1221 mg/day with little variation across Regions. Estimated dietary sodium consumption in Thai adults is nearly twice as high as recommended levels. These data provide a benchmark for future monitoring.

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Identification of unique individuals is being extensively used in security and surveillance. Gait recognition has caught the attention of computer vision researchers. This interest has been stimulated by the development of systems to automatically identify individuals. This paper presents a gait gesture recognition algorithm, using Incremental Dynamic Time Warping (IDTW) with a body measurement technique that identifies personal gait patterns recorded on video via Microsoft’s Kinect® 3D depth-sensing camera. We used the height of a person to further clarify the recognition and accurate identification of the individual. The initial results demonstrated 81.25% accuracy with our gait and height recognition algorithm. This recognition technique is ideal for high-level security requirements.

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