A multi-sensor gait dataset collected under non-standardized dual-task conditions

Non-standardized dual-tasks have recently gained attention in health
monitoring and post-operative rehabilitation. By collecting data with
multiple sensors, we can quantify motion characteristics from different
perspectives and explore the complementarity and interchangeability
between sensors. Currently, there is a lack of publicly available
non-standardized dual-task gait datasets collected with multiple sensors,
thus we proposed a dataset (NONSD-Gait) from 23 healthy adults walking
back and forth over 7 meters under three dual-task conditions, collected
by three types of sensors: optical motion capture (MOCAP) system, depth
camera and inertial measurement unit (IMU). MoCap captured the 3D
trajectories of 22 markers attached to the subject using 8 optical
cameras, while the depth camera recorded the 3D trajectories of 25 joints
through a non-contact depth camera. The IMU was placed on the left ankle
to record 3-axis acceleration and angular velocity data. Each participant
underwent two repeated experiments for each task. Moreover, this dataset
also includes extracted spatio-temporal gait parameters and kinematic
parameters, supporting gait feature recognition in complex scenarios and
multimodal gait data analysis.