Abstract
With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.
Highlights
Since the 21st century, the development of Internet and the popularity of wearable devices brings data explosion. ese massive data provide the foundation of structuring artificial intelligence (AI) algorithms
Artificial neural network (ANN) is a widely used model that models the relationship between input and output units. e most popular training algorithm in ANN is the backpropagation which iterative learns a set of weights for the prediction of the class labels
Standard deviation (SD) shows the difference among ten times prediction results. e processing time is shown in the last column (Time) of each model, which is recorded in seconds
Summary
Since the 21st century, the development of Internet and the popularity of wearable devices brings data explosion. ese massive data provide the foundation of structuring artificial intelligence (AI) algorithms. With development of microprocessor and enabling sensors with high computational ability, the small size, and low cost, portable devices, such as smartphone, band, smart watch, or professional sensors, are used widely and record the huge data from users. Machine learning algorithms are applied in variant intelligent devices, such as intelligent band with activity detection element that records the different activity situation for keeping health, or iWatch with fall detection function, which assists in monitoring older people activities and alarming dangerous activities. E most popular training algorithm in ANN is the backpropagation which iterative learns a set of weights for the prediction of the class labels. Paper [9] applies optimal ANN classifier to recognize human activity based on mobile sensors data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
