Hybrid deep learning approaches for smartphone sensor-based human activity recognition
Hybrid deep learning approaches for smartphone sensor-based human activity recognition
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DOI：
10.1007/s11042-020-10478-4
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发表时间：
2021-02-06
影响因子：
3.6
通讯作者：
Hemalatha, Sweetlin C.
中科院分区：
文献类型：
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作者：
Ghate, Vasundhara;Hemalatha, Sweetlin C.
人类活动识别(HAR)已经成为实现实时监测人类活动以便在跌倒检测、老年护理等各种应用中及时决策的最重要的研究领域之一。现在，大多数人使用智能手机来监测加速度和角速度等各种内嵌的惯性传感器，如加速计和陀螺仪。事实证明，这些基于智能手机的传感器在识别属于日常生活活动(ADL)的活动方面是一种经济高效的解决方案。目前已经提出并实现了各种机器学习、深度学习和混合模型。本文还提出了多种混合深度学习方法，将深度神经网络与LSTM(长短期记忆)模型和GRU(门控递归单元)模型相结合，以有效地对CNN(卷积神经网络)模型中的工程特征进行分类。为了增加模型的随机性，提出了一种将CNN和随机森林分类器相结合的新体系结构(DeepCNN-RF)。所提出的模型已经在UCI HAR和WISDM活动识别数据集等公开可用的HAR数据集上进行了测试。实验结果表明，混合模型在UCI HAR和WISDM中的总体最高准确率分别为97.77%和98.2%，优于最新的数据挖掘、机器学习技术。
Human Activity Recognition (HAR) has become one of the most important research fields to achieve real-time monitoring of human activities for timely decision making in various applications like fall detection, elderly care etc. Now-a-days, most people use smartphones which come with various embedded inertial sensors like accelerometer and gyroscope to monitor acceleration and angular velocity. These smartphone-based sensors have proven to be cost-effective solution in identification of activities belonging to ADL (Activities of Daily Living). Various Machine Learning, Deep learning and hybrid models have been proposed and implemented for HAR. This paper also proposes various hybrid deep learning approaches which combine Deep Neural Networks with other models like LSTM (Long Short Term Memory) Model and GRU (Gated Recurrent Unit) for effective classification of engineered features from CNN (Convolutional Neural Network) Model. A novel architecture that integrates CNN with Random Forest Classifier (DeepCNN-RF) is proposed to add randomness to the model. The proposed models have been tested on publicly available HAR Datasets like UCI HAR and WISDM Activity Recognition Datasets. Experimental results show that the hybrid models outperform the state-of-the-art data mining, machine learning techniques in UCI HAR and WISDM with an overall maximum accuracy of 97.77% and 98.2% respectively.