生理数据给机器学习带来了许多挑战，包括处理大量数据、序列数据、传感器融合问题，以及包含噪声、隐藏变量和受环境显著影响的丰富领域。

Physiological data offers many challenges to the machine learning community including dealing with large amounts of data, sequential data, issues of sensor fusion, and a rich domain complete with noise, hidden variables, and significant effects of context.

1. 每一列对应哪些传感器？Which sensors correspond to each column?

特征1：年龄

characteristic1 age

特征2：用右手或左手的习惯

characteristic2 handedness

传感器1：gsr_low_average

sensor1 gsr_low_average

传感器2：heat_flux_high_average

sensor2 heat_flux_high_average

传感器3：near_body_temp_average

sensor3 near_body_temp_average

传感器4：计步器

sensor4 pedometer

传感器5：skin_temp_average

sensor5 skin_temp_average

传感器6：纵向加速度计SAD

sensor6 longitudinal_accelerometer_SAD

传感器7：纵向加速度计平均值

sensor7 longitudinal_accelerometer_average

传感器8：横向加速度计SAD

sensor8 transverse_accelerometer_SAD

传感器9：横向加速度计平均值

sensor9 transverse_accelerometer_average

2. 每个注解背后的活动是什么？What are the activities behind each annotation?

比赛的注解如下：

The annotations for the contest were:

5102 = sleep

3104 = watching TV

项目思路：

*行为分类；根据传感器测量数据对人进行分类。

* behavior classification; to classify the person based on the sensor measurements.

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