IEEE J Biomed Health Inform. 2020 May;24(5):1477-1489. doi: 10.1109/JBHI.2019.2938627. Epub 2019 Aug 30.
Continuous recognition of ingested foods without user intervention is very useful for the pre-screening of obesity and diet-related disease. An automatic food recognition method that combines the two modalities of audio and ultrasonic signals (US) is proposed in this study. Under a noise-free environment, classification accuracy of an audio-only recognizer is generally higher than that of US-only recognizers, but the performance of US recognizers is unaffected by acoustic noise levels. In the recognition system presented herein, the likelihood score of the audio-US feature was given by a linear combination of class-conditional observation log-likelihoods for two classifiers, using the appropriate weights. We developed a weighting process adaptive to signal-to-noise ratios (SNRs). The main objective here involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. A feasibility test was conducted to verify the usefulness of the proposed method by conducting recognition experiments on seven types of food. The performance was compared with conventional methods that use in-ear and throat microphones. The proposed method yielded remarkable levels of recognition performance of 90.13% for artificially added noise and 89.67% under actual noisy environments, when the SNR ranged from 0 to 20 dB.
无需用户干预即可连续识别摄入的食物，这对于肥胖症和饮食相关疾病的初步筛查非常有用。本研究提出了一种结合音频和超声信号（US）两种模态的自动食物识别方法。在无噪声环境下，仅使用音频的识别器的分类准确性通常高于仅使用 US 的识别器，但 US 识别器的性能不受声噪声级的影响。在本文提出的识别系统中，音频-US 特征的似然评分由两个分类器的条件观察对数似然的线性组合给出，使用适当的权重。我们开发了一种自适应于信噪比（SNR）的加权过程。这里的主要目标是确定最佳 SNR 分类边界，并为每个 SNR 类构造一组最优流权重。通过对七种食物进行识别实验，进行了可行性测试以验证所提出方法的有用性。与使用入耳式和喉部麦克风的传统方法进行了性能比较。当 SNR 范围从 0 到 20dB 时，所提出的方法在人为添加噪声时的识别性能达到 90.13%，在实际嘈杂环境下的识别性能达到 89.67%。
