Lower Limb Prostheses Environmental Context Dataset

Citation Author(s):
Rafael L.
da Silva
Submitted by:
Boxuan Zhong
Last updated:
Mon, 01/04/2021 - 22:35
Data Format:
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This is the data for paper "Environmental Context Prediction for Lower Limb Prostheses with Uncertainty Quantification" published on IEEE Transactions on Automation Science and Engineering, 2020. DOI: 10.1109/TASE.2020.2993399. For more details, please refer to https://research.ece.ncsu.edu/aros/paper-tase2020-lowerlimb. 


Seven able-bodied subjects and one transtibial amputee participated in this study. Subject_001 to Subject_007 are able-bodied participants and Subject_008 is a transtibial amputee.


Each folder in the subject_xxx.zip file has one continuous session of data with the following items: 

1. folder named "rpi_frames": the frames collected from the lower limb camera. Frame rate: 10 frames per second. 

2. folder named "tobii_frames": the frames collected from the on-glasses camera. Frame rate: 10 frames per second. 

3. labels_fps10.mat: synchronized terrain labels, gaze from the eye-tracking glasses, GPS coordinates, and IMU signals. 

3.1 cam_time: the timestamps for the videos, GPS, gazes, and labeled terrains (unit: second). 10Hz

3.2 imu_time: the timestamps for the IMU sensors (unit: second). 40Hz.

3.3 GPS: the GPS coordinates (latitude, longitude)

3.4 rpi_FrameIds, tobii_FrameIds: the frame ID for the lower-limb and on-glasses cameras respectively. The ids indicate the filenames in "rpi_frames" and "tobii_frames" respectively. 

3.5 rpi_IMUs, tobii_IMUs: the imu signals from the two devices. Columns: (accel_x,accel_y,accel_z,gyro_x,gyro_y,gyro_z)

3.6 terrains: the type of terrains the subjects are current on. Six terrains: tile, brick, grass, cement, upstairs, downstairs. "undefined" and "unlabelled" can be regarded as the same kind of data that needs to be deprecated.


The following sessions were collected during busy hours (many pedestrians were around):







The following sessions were collected during non-busy hours (few pedestrians were around):









The other sessions were collected without specific collecting hours (e.g. busy or non-busy). 

For the following sessions, the data collection devices were not optimized (e.g. non-optimal brightness balance). Thus, we recommend to use these sessions as training or validation dataset but not as testing data.