School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
School of Computer Science, Chengdu University, Chengdu 610106, China.
Sensors (Basel). 2022 Jul 14;22(14):5273. doi: 10.3390/s22145273.
Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.
基于可穿戴设备的人体运动识别在普适计算中起着至关重要的作用。智能手机内置运动传感器，可以高精度地测量设备的运动。在本文中，我们提出了一种基于智能手机的人体下肢运动捕获和识别方法。我们设计了一个运动记录器，使用两个运动传感器（三轴加速度计、三轴陀螺仪）记录五类肢体活动（站立、坐下、行走、上下楼梯）。我们从感测数据的频域中提取运动特征，并使用快速傅里叶变换（FFT）选择特征子集作为特征向量。我们使用三种有监督学习算法（朴素贝叶斯（NB）、K 最近邻（KNN）和人工神经网络（ANNs））对人体下肢运动进行分类和预测。我们使用 670 个下肢运动样本，通过 10 折交叉验证技术对这些分类器进行训练和验证。最后，我们设计并实现了一个实时检测系统来验证我们的运动检测方法。实验结果表明，我们的低成本方法可以以可接受的精度识别人体下肢活动。平均而言，NB、KNN 和 ANNs 的识别率分别为 97.01%、96.12%和 98.21%。
