Recognition of dietary activity events using on-body sensors
Recognition of dietary activity events using on-body sensors
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DOI：
10.1016/j.artmed.2007.11.007
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发表时间：
2008-02-01
影响因子：
7.5
通讯作者：
Troester, Gerhard
中科院分区：
文献类型：
--
作者：
Amft, Oliver;Troester, Gerhard
目的：不均衡的饮食增加了许多慢性疾病的健康风险，包括肥胖。饮食监测可以为生活方式指导和饮食管理提供重要信息，然而，目前的监测解决方案对于长期实施是不可行的。走向自动饮食监测，这项工作的目标是不断识别的饮食活动，使用身体上的sensors.Methods：一个身体上的传感方法被选择，基于三个核心活动在摄入：手臂运动，咀嚼和吞咽。在三个独立的评价研究中，连续识别的活动事件进行了调查和精确召回性能进行了分析。部署了一个事件识别过程，解决了连续活动识别的多个挑战，包括对可变长度活动的动态适应性和通过支持一个到多个独立类的灵活部署。该方法使用敏感的活动事件搜索，然后使用不同的信息融合方案的检测的选择性细化。该方法是简单的，模块化的设计和implementation.Results：识别过程是成功地适应调查的饮食活动。四个摄入手势类别的手臂运动和两个食物组的咀嚼周期的声音被检测和识别的召回率为80-90%，精度为50- 64%。单个吞咽的检测导致68%的召回率和20%的准确率。样本的准确识别率为79%的运动，86%的咀嚼和70%forswalling.Conclusions：身体动作和咀嚼的声音可以准确地识别使用身体上的传感器，证明身体上的饮食监测的可行性。需要进一步的研究，以提高吞咽斑点的性能。(C)2007 Elsevier B. V.保留所有权利。
Objective: An imbalanced diet elevates health risks for many chronic diseases including obesity. Dietary monitoring could contribute vital information to lifestyle coaching and diet management, however, current monitoring solutions are not feasible for a tong-term implementation. Towards automatic dietary monitoring, this work targets the continuous recognition of dietary activities using on-body sensors.Methods: An on-body sensing approach was chosen, based on three core activities during intake: arm movements, chewing and swallowing. In three independent evaluation studies the continuous recognition of activity events was investigated and the precision-recall performance analysed. An event recognition procedure was deployed, that addresses multiple challenges of continuous activity recognition, including the dynamic adaptability for variable-length activities and flexible deployment by supporting one to many independent classes. The approach uses a sensitive activity event search followed by a selective refinement of the detection using different information fusion schemes. The method is simple and modular in design and implementation.Results: The recognition procedure was successfully adapted to the investigated dietary activities. Four intake gesture categories from arm movements and two food groups from chewing cycle sounds were detected and identified with a recall of 80-90% and a precision of 50-64%. The detection of individual swallows resulted in 68% recall and 20% precision. Sample-accurate recognition rates were 79% for movements, 86% for chewing and 70% for swallowing.Conclusions: Body movements and chewing sounds can be accurately identified using on-body sensors, demonstrating the feasibility of on-body dietary monitoring. Further investigations are needed to improve the swallowing spotting performance. (C) 2007 Elsevier B.V. All rights reserved.