S3 Dataset

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones. All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors: Sensors: This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer. Statistics: These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces. Voice: This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding. A summary of the details of the collected database. - Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

S3数据集包含21名志愿者使用智能手机超过60天的行为数据，涵盖传感器数据、应用统计数据与语音数据。本数据集的用户群体多样，覆盖了年龄介于18至70岁的男性与女性，年龄跨度大是该数据集的核心特性之一——由于年龄会对智能手机使用行为产生显著影响。

为构建该数据集，志愿者在其安卓（Android）手机上安装了一款智能手机应用原型。各类数据的所有属性均以向量形式存储。本数据集包含以下三类向量：
1. 传感器向量：此类向量存储智能手机传感器（加速度计与陀螺仪）在指定时间窗口内采集的数据。每20秒生成一条向量，监测特征包括：加速度计与陀螺仪数值的平均值、加速度计与陀螺仪数值的最大值与最小值、加速度计与陀螺仪数值的方差、X/Y/Z坐标的峰峰值（最大值减最小值）、陀螺仪与加速度计的幅值。
2. 应用统计向量：此类向量包含用户近期使用各类应用的相关数据。每60秒计算生成一条统计向量，具体内容包括：过去1分钟与过去1天的前台应用计数（不同应用数量与总应用使用次数）、过去1分钟与过去1天内最常使用的应用ID及其使用次数、当前前台应用ID、当前应用之前的最后一个活跃应用ID、当前应用之前使用频次最高的应用ID、通过网络接口传输与接收的字节数。
3. 语音向量：此类向量在麦克风用于通话或语音笔记时生成。该说话人向量是从音频中提取的嵌入向量，包含用户身份相关信息。在说话人识别领域，此类向量通常被称为“x向量（x-vector）”，其计算遵循Kaldi库"egs/sitw/v2"中详述的步骤，使用公开可用的嵌入提取模型完成。

以下为本数据集采集详情的汇总：
- 志愿者用户总数：21名
- 传感器向量总数：417128条
- 应用使用统计向量总数：151034条
- 说话人向量总数：2720条
- 通话录音总数：629条
- 语音消息总数：2091条