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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >Recognizing Eating Gestures Using Context Dependent Hidden Markov Models
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Recognizing Eating Gestures Using Context Dependent Hidden Markov Models

机译：使用上下文相关的隐马尔可夫模型识别饮食手势

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摘要

This paper considers the problems of recognizing eating gestures by tracking wrist motion. Hidden Markov models (HMMs) were developed to capture variations in motion patterns of subgroups of participants. Specifically, we examined if foreknowledge of the gender, age, and utensil used for eating could improve recognition accuracy. Improvement in accuracy was measured by comparing to a baseline HMM that was trained on all participants. Data was collected for 276 participants eating a single meal within a cafeteria setting. A total of 44,873 gestures were manually labeled using video synchronized with the wrist motion tracking device. Results show that gender HMMs performed slightly better than the baseline, indicating that there is not much difference in wrist motion patterns during eating between females and males. Age HMMs provided a 4.3% increase in accuracy and utensil HMMs provided a 6.2% increase inaccuracy. The results suggest that contextual variables can be used for improving gesture recognition.
机译：本文考虑了通过跟踪手腕运动来识别饮食手势的问题。隐藏的马尔可夫模型（HMM）的开发是为了捕获参与者子组运动模式的变化。具体来说，我们研究了对性别，年龄和饮食习惯的理解是否可以提高识别准确性。通过与对所有参与者进行了训练的基线HMM进行比较，可以测量准确性的提高。收集了在自助餐厅环境中单餐进餐的276名参与者的数据。使用与腕部运动跟踪设备同步的视频手动标记了总计44,873个手势。结果表明，性别HMMs的表现略好于基线，这表明男性和女性进食期间腕部运动方式没有太大差异。年龄HMM的准确性提高了4.3％，器皿HMM的准确性提高了6.2％。结果表明上下文变量可以用于改善手势识别。

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