Abstract
New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.
Highlights
Today, mobiles devices such as smartphones and tablet computers have powerful processors, high memory capacities and other sophisticated features, which allow for the development of intelligent context-aware services for smart environments such as smart homes, smart cities, and smart mobility [49]
Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification; 3) a new dataset of the accelerometer and gyroscope signals acquired from a smartphone of users performing three types of daily user activities; 4) classification of the fused accelerometer and gyroscope data using K-NN and Support Vector Machine (SVM) classifiers
Classification was performed for a combination of two activities and lastly for all three activities using the k-Nearest Neighbor (k-NN) and SVM classifiers
Summary
Mobiles devices such as smartphones and tablet computers have powerful processors, high memory capacities and other sophisticated features, which allow for the development of intelligent context-aware services for smart environments such as smart homes, smart cities, and smart mobility [49]. Correct determination of user activity enables high-level reasoning over the domain of activities and services in order to create contextual rules such as “unmute my phone when the meeting ends and I start walking”, “forward all the incoming calls if I am driving”, “ring my phone loudly if I am walking”, etc. Such rules can be utilized by contextual reasoning engines to provide support for upper-level applications that provide smart context-aware services to its users. Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification; 3) a new dataset of the accelerometer and gyroscope signals acquired from a smartphone of users performing three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle); 4) classification of the fused accelerometer and gyroscope data using K-NN and SVM classifiers
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