*.csv

Human activity data based on wearable sensors, such as the Inertial Measurement Unit (IMU), have been widely used in human activity recognition. However, most publicly available datasets only collected data from few body parts and the type of data collected is relatively homogeneous. Activity data from local body parts is challenging for recognizing specific activities or complex activities. Hence, we create a new  HAR dataset which is colledted from the project named MPJA HAD: A Multi-Position Joint Angles Dataset for Human Activity Recognition Using Wearable Sensors.

For the data acquisition we used JINS MEME smart glasses -- a device furnished with three-point EOG and six-axis inertial measurement unit (IMU) with an accelerometer and a gyroscope. The sampling frequency of the acquired signals is 100 Hz. The data are transmitted to a computer via Bluetooth or USB and can be exported to CSV file.

Data were acquired under real road conditions from 30 healthy subjects, including twenty experienced drivers and ten students attending a driving school. 16 males and 14 females with average age = 38 +-17 participated in the study.

The dataset shows the decisions of the participants in a dictator game split into two rounds. The participants faced an android as their opponent. Round 1 represents their decision in the game after they met the android and listened to its introductory speech. Round 2 is their decision in the game after having an interactive conversation with the android.

Industrial drying processes are an energy extensive and often one of the most time consuming processes in a manufacturing line. Optimizing the drying process can therefore lead to a reducing in energy consumption and an increase in manufacturing throughput.