Gender Recognition Using Motion Data from Multiple Smart Devices Abstract   Using multiple smart devices, such as smartphone and smartwatch simultaneously, is becoming a popular life style with the popularity of wearables. This multiplesensor setting provides new opportunities for enhanced user trait analysis via multiple data fusion. In this study, we explore the task of gender recognition by using motion data collected from multiple smart devices. Specifically, motion data are collected from smartphone and smart band simultaneously. Motion features are extracted from the collected motion data according to three aspects: time, frequency, and wavelet domains. We present a feature selection method considering the redundancies between motion features. Gender recognition is performed using four supervised learning methods. Experimental results demonstrate that using motion data collected from multiple smart devices can significantly improve the accuracy of gender recognition. Evaluation of our method on a dataset of 56 subjects shows that it can reach an accuracy of 98.7% compared with the accuracies of 93.7% and 88.2% when using smartphone and smart band individually.

Smart Phone Based Human Activity Recognition Human Activity Recognition (HAR) is a field that uses collected data to classify different human actions. One simple and general approach to HAR is to use the sensor data from a mobile device to recognize different patterns behind complex motions. Early studies show promising results on simple activities using manually selected features from accelerometer readings. As newer publicly available datasets include more complex data and activities, manual feature selection have become cumbersome, impractical and face limitations in finding the optimal feature sets for HAR. In this paper, we present an empirical approach to defining models of 3D tensor data structures from 2D time series data obtained from multiple sensors on a smart phone, and a new Convolutional Neural Network (CNN) model, which uses the tensor data and performs automatic feature extraction and classification for HAR. We use the public benchmark dataset, MobiAct v2.0, to train and validate our model, which achieved an overall better performance in classifying 11 Activities of Daily Living (ADL) than the stateoftheart approaches. Compared to the approach presented by Chatzaki et al. which has a very high rate of misclassifications for carstep out (CSO), carstep in (CSI), sit to stand (CHU), and stand to sit (SCH) classes, our proposed approach has 15% higher sensitivity for each of these activities with the optimal number of training epochs being only 25.