Advancements in wearable sensors technologies provide prominent effects in the daily life activities of humans. These wearable sensors are gaining more awareness in healthcare for the elderly to ensure their independent living and to improve their comfort. In this paper, we present a human activity recognition model that acquires signal data from motion node sensors including inertial sensors, i.e., gyroscopes and accelerometers. First, the inertial data is processed via multiple filters such as Savitzky–Golay, median and hampel filters to examine lower/upper cutoff frequency behaviors. Second, it extracts a multifused model for statistical, wavelet and binary features to maximize the occurrence of optimal feature values. Then, adaptive moment estimation (Adam) and AdaDelta are introduced in a feature optimization phase to adopt learning rate patterns. These optimized patterns are further processed by the maximum entropy Markov model (MEMM) for empirical expectation and highest entropy, which measure signal variances for outperformed accuracy results. Our model was experimentally evaluated on University of Southern California Human Activity Dataset (USC-HAD) as a benchmark dataset and on an Intelligent Mediasporting behavior (IMSB), which is a new self-annotated sports dataset. For evaluation, we used the “leave-one-out” cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving an improved recognition accuracy of 91.25%, 93.66% and 90.91% when compared with USC-HAD, IMSB, and Mhealth datasets, respectively. The proposed system should be applicable to man–machine interface domains, such as health exercises, robot learning, interactive games and pattern-based surveillance.

中文翻译：

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基于 Adam 优化和最大熵马尔可夫模型的用于日常活动分析的可穿戴惯性传感器

可穿戴传感器技术的进步对人类的日常生活活动产生了显着影响。这些可穿戴传感器在老年人的医疗保健中越来越受到关注，以确保他们的独立生活并提高他们的舒适度。在本文中，我们提出了一种人类活动识别模型，该模型从运动节点传感器（包括惯性传感器，即陀螺仪和加速度计）获取信号数据。首先，惯性数据通过多个滤波器（如 Savitzky-Golay、中值和汉佩尔滤波器）进行处理，以检查下/上截止频率行为。其次，它为统计、小波和二值特征提取多融合模型，以最大化最佳特征值的出现。然后，在特征优化阶段引入了自适应矩估计 (Adam) 和 AdaDelta，以采用学习率模式。这些优化的模式由最大熵马尔可夫模型 (MEMM) 进一步处理以获得经验期望和最高熵，其测量信号方差以获得优于准确度的结果。我们的模型在作为基准数据集的南加州大学人类活动数据集 (USC-HAD) 和智能媒体运动行为 (IMSB) 上进行了实验评估，IMSB 是一个新的自我注释运动数据集。对于评估，我们使用了“留一法”交叉验证方案，结果优于现有的知名统计最先进方法，相比之下，识别准确率提高了 91.25%、93.66% 和 90.91%分别使用 USC-HAD、IMSB 和 Mhealth 数据集。