赋予糖尿病患者权力:基于智能手机的食品分类,体积测量和营养估计方面的进展
Afnan Ahmed Crystal1, Maria Valero2, Valentina Nino3
1Department of Computer Science, Kennesaw State University, Kennesaw, GA 30060, USA.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
自动化食品图像识别系统 (FIRS) 通过简化饮食跟踪来改善糖尿病管理,提供了一种技术解决方案. 本综述探讨了FIRS在食品分类,体积和营养估计方面的进展.
科学领域:
- 医疗信息学 医疗信息学
- 计算机视觉 计算机视觉
- 营养科学 营养科学
背景情况:
- 糖尿病影响全球5.37亿成年人,需要通过饮食,运动和药物进行勤奋的血糖管理.
- 传统的食物日记是繁的,容易出现不准确的情况,这阻碍了糖尿病患者有效的饮食监测.
- 自动化技术,特别是食品图像识别系统 (FIRS),为有效和准确的饮食跟踪提供了一个有希望的替代方案.
研究的目的:
- 系统地审查基于移动计算机视觉的方法,用于食品分类,体积估计和营养估计.
- 探索FIRS在糖尿病管理背景下的当前状态,挑战和未来方向.
主要方法:
- 进行了对过去二十年出版的相关科学文献的系统审查.
- 评估了专注于用于食品分析的移动计算机视觉应用的研究.
- 该审查综合了关于FIRS在分类食品,估计份量大小和计算营养含量方面的发现.
主要成果:
- 使用移动摄像头和计算机视觉的FIRS显示了减少饮食记录手工工作的潜力.
- 这些系统在食品分类,体积和营养估计方面提供不同程度的准确性.
- 在FIRS的进步可以提供关于食组成的见解,并建议营养改善.
结论:
- 基于移动计算机视觉的FIRS代表了改善糖尿病护理中饮食监测的重大技术进步.
- 需要进一步的研究和开发来解决目前的局限性,并提高FIRS的准确性和实用性.
- 通过改善饮食洞察力,FIRS有可能使糖尿病患者能够更好地管理他们的病情.
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