Many of the publicly available electrocardiogram (ECG) databases either have a low number of people in the database, each with longer recordings, or have more people, each with shorter recordings. As a result, attempting to split a single database into training, testing, and, optionally, validation datasets is challenging. Some models seem to do well with larger training sets, but that leaves only a small set of data for testing. Moreover, if the ECG is segmented by heartbeat, the data are further limited by the number of heartbeats in the recording.

This dataset is used for sign language emotion recognition and contains five emotions from 12 participants (6 males and 6 females) with high-positive, low-positive, high-negative, low-negative, and neutral emotions. The surface electromyography (sEMG) and inertial measurement unit (IMU) sensors were used to capture 30 sign language sentence signals. Participants' emotions were activated by film clips.

The dataset contains short video clips of four shoulder exercises.

1. Arm flexion and extension
2. Arm abduction and adduction
3. Arm lateral and medial rotation
4. Arm circumduction

The videos are labeled as either correct or incorrect.

This dataset is a private foot pressure image dataset containing 317 images of high arches (H), 217 images of flat feet (L) and 362 images of normal feet (N).