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Effective Ways to Overcome Classification Limitations for Activities of Daily Livings (ADLs)

机译：克服日常生活活动（ADL）分类限制的有效方法

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Development of reliable and precise methods of Human Activity Recognition (HAR) are highly important, since wrong or inaccurate recognition can cause harmful consequences for human health. Scientists working in the field try to find the ways to enhance achievements for recognition accuracy. Taking this into account, it is vital to choose classifiers which make classification of activities with reliable rates. However, limitations of the currently existing algorithms and inherent lack of precision level can put their applicability to the field under the question. In particular, Hidden Markov Model has certain restrictions, which is caused by the principle of random selection of parameters and it is problematic to discriminate between the classes with high accuracy. Other well-known methods also have some drawbacks due to their nature. With the aim to solve these problems and to ensure the required results, we propose a hybrid complex of classifiers that provide adequate models of the study area. This work will deal with the classification of daily living human activities using wearable inertial sensors. Walking, Lying, Standing up, Stair Ascent, Stair Descent, Sitting on the Ground, etc. are examples of these activities. In this study, a dataset including twelve activities is created using three inertial sensors. Two hybrids of classifiers that combines Bayesian Networks with distance based classifier, namely with k Nearest Neighbor and Neural Network with Hidden Markov Model will be presented in the study. The achieved results will be compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features will be used separately as inputs of the classifier. The inertial sensor units worn by different healthy subjects are placed at key points of upper/lower body limbs (chest, right thigh and left ankle). In this study, only acceleration data is used, as a modality for estimating the activities.
机译：可靠和精确的人类活动识别（HAR）方法的开发非常重要，因为错误或不正确的识别可能会对人体健康造成有害影响。从事该领域工作的科学家试图找到提高识别准确性的方法。考虑到这一点，选择分类器以可靠的比率对活动进行分类至关重要。然而，当前现有算法的局限性和固有的精度水平不足可能使它们在该领域中的适用性受到质疑。特别地，隐马尔可夫模型具有一定的限制，这是由于参数的随机选择原理所引起的，并且以高的准确度来区分类别是有问题的。由于其性质，其他公知的方法也具有一些缺点。为了解决这些问题并确保获得所需的结果，我们提出了一种混合分类器，可以为研究区域提供适当的模型。这项工作将使用可穿戴惯性传感器处理人类日常活动的分类。这些活动包括步行，躺着，站起来，爬楼梯，爬下楼梯，坐在地上等。在这项研究中，使用三个惯性传感器创建了包含十二个活动的数据集。这项研究将提出两种结合了贝叶斯网络和基于距离的分类器的混合器，即k最近邻和具有隐马尔可夫模型的神经网络。将根据正确的分类率，F量度，召回率，精确度和特异性对获得的结果进行比较。原始数据和提取的特征将分别用作分类器的输入。不同健康受试者佩戴的惯性传感器单元放置在上/下肢的关键点（胸部，右大腿和左脚踝）。在本研究中，仅使用加速度数据作为估算活动的方式。

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