CRAWDAD columbia/kinetic

To help us better understand the properties of various energy sources and their impact on energy harvesting adaptive algorithms, we collected acceleration traces from different participants.release date: 2014-05-13collection environment: Measurements were collected from five participants of different ages, physiques and means of transportation to the same laboratory work location. The participants included two undergraduate students who commuted by foot as well as an undergraduate student, graduate student, and software developer who commuted by train. As mentioned earlier, the five participants were asked to carry the sensing units in a comfortable manner over a period of 25 days. The dominant motion frequency of all collected traces ran in the range of 1.92-2.8Hz, which corresponded to human walking. Using collected data, we were able to calculate the average power a harvester could generate over a trace's length as well as over a 24-hour period. data collection methodology: For the logging of measurements, we use ADXL345 tri-axis accelerometers, Atmega328P microcontrollers and microSD cards located on SparkFun ADXL345 evaluation boards. The accelerometer sensors record acceleration along the x, y, and z axes with a 100 Hz sampling frequency. Experiments were conducted with the sensing units placed in multiple locations. Tracesetscolumbia/kinetic/kinetic-energy200 hours of acceleration information in 25 days from 5 participantsmethodology: Measurements were collected from five participants of different ages, physiques and means of transportation to the same laboratory work location. The participants included two undergraduate students who commuted by foot as well as an undergraduate student, graduate student, and software developer who commuted by train. As mentioned earlier, the five participants were asked to carry the sensing units in a comfortable manner over a period of 25 days. The dominant motion frequency of all collected traces ran in the range of 1.92-2.8 Hz, which corresponded to human walking. Using collected data, we were able to calculate the average power a harvester could generate over a trace's length as well as over a 24-hour period.columbia/kinetic/kinetic-energy Tracescolumbia/kinetic/kinetic-energy/M1:

为帮助我们更深入地理解各类能源的特性及其对能量收集自适应算法的影响，我们采集了不同受试者的加速度轨迹。
发布日期：2014-05-13
采集环境：本次测量招募了5名年龄、体型各异且通勤至同一实验室工作地点的交通方式各不相同的受试者，其中包括2名步行通勤的本科生，以及1名本科生、1名研究生与1名软件开发者（均乘火车通勤）。如前所述，这5名受试者被要求以舒适的方式佩戴传感单元，时长共计25天。所有采集到的轨迹的主导运动频率处于1.92~2.8Hz区间，对应人类步行运动。利用所采集的数据，我们可计算出能量收集器在单条轨迹时长内以及24小时周期内可产生的平均功率。
数据采集方法：本次测量采用搭载于SparkFun ADXL345评估板上的ADXL345三轴加速度计（ADXL345 tri-axis accelerometers）、Atmega328P微控制器（Atmega328P microcontrollers）与microSD存储卡完成数据记录。加速度传感器以100Hz的采样频率，沿x、y、z三轴记录加速度数据。实验中将传感单元放置于多个不同位置开展采集。
轨迹数据集：columbia/kinetic/kinetic-energy
包含5名受试者在25天内采集得到的总计200小时的加速度数据。
数据采集方法：本次测量招募了5名年龄、体型各异且通勤至同一实验室工作地点的交通方式各不相同的受试者，其中包括2名步行通勤的本科生，以及1名本科生、1名研究生与1名软件开发者（均乘火车通勤）。如前所述，这5名受试者被要求以舒适的方式佩戴传感单元，时长共计25天。所有采集到的轨迹的主导运动频率处于1.92~2.8Hz区间，对应人类步行运动。利用所采集的数据，我们可计算出能量收集器在单条轨迹时长内以及24小时周期内可产生的平均功率。
columbia/kinetic/kinetic-energy 轨迹集
columbia/kinetic/kinetic-energy/M1：