Computer Science Department, Alex Ekwueme Federal University, Ndufu-Alike, Ikwo, P.M.B 1010, Abakaliki, Ebonyi State 480263, Nigeria.
Computer Science Department, Ebonyi State University, P.M.B 053, Abakaliki, Ebonyi State 480211, Nigeria.
Sensors (Basel). 2020 Nov 5;20(21):6300. doi: 10.3390/s20216300.
Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefore, it is challenging to accurately and automatically identify activity details as a result of the complexity and orientation inconsistencies of the smartphone. Furthermore, the current activity identification methods utilize conventional machine learning algorithms that are application dependent. Moreover, it is difficult to model the hierarchical and temporal dynamic nature of the current, complex, activity identification process. This paper aims to propose a deep stacked autoencoder algorithm, and orientation invariant features, for complex human activity identification. The proposed approach is made up of various stages. First, we computed the magnitude norm vector and rotation feature (pitch and roll angles) to augment the three-axis dimensions (3-D) of the accelerometer sensor. Second, we propose a deep stacked autoencoder based deep learning algorithm to automatically extract compact feature representation from the motion sensor data. The results show that the proposed integration of the deep learning algorithm, and orientation invariant features, can accurately recognize complex activity details using only smartphone accelerometer data. The proposed deep stacked autoencoder method achieved 97.13% identification accuracy compared to the conventional machine learning methods and the deep belief network algorithm. The results suggest the impact of the proposed method to improve a smartphone-based complex human activity identification framework.
使用智能手机嵌入式加速度计传感器进行人体运动分析，为识别静态、动态和复杂活动序列提供了重要依据。基于智能手机的运动分析研究可用于健康状态监测、跌倒检测和预防、能量消耗估计和情绪检测等任务。然而，目前在这方面的方法假设设备紧密地固定在一个预定的位置和方向上，这可能会由于方向的变化而导致加速度计数据的性能下降。因此，由于智能手机的复杂性和方向不一致性，很难准确和自动地识别活动细节。此外，当前的活动识别方法使用传统的机器学习算法，这些算法依赖于应用程序。此外，很难对当前复杂的活动识别过程的层次和时间动态特性进行建模。本文旨在提出一种深度堆叠自动编码器算法和方向不变特征，用于复杂的人体活动识别。所提出的方法由多个阶段组成。首先，我们计算了幅度范数向量和旋转特征（俯仰角和滚动角），以增强加速度计传感器的三轴（3-D）维度。其次，我们提出了一种基于深度堆叠自动编码器的深度学习算法，用于从运动传感器数据中自动提取紧凑的特征表示。结果表明，该方法结合了深度学习算法和方向不变特征，可以仅使用智能手机加速度计数据准确识别复杂的活动细节。与传统的机器学习方法和深度置信网络算法相比，所提出的深度堆叠自动编码器方法的识别准确率达到 97.13%。结果表明，该方法对改进基于智能手机的复杂人体活动识别框架具有重要意义。
