GaitMotion: A Multitask Dataset for Pathological Gait Forecasting
<|reference_start|>GaitMotion: A Multitask Dataset for Pathological Gait Forecasting: Gait benchmark empowers uncounted encouraging research fields such as gait recognition, humanoid locomotion, etc. Despite the growing focus on gait analysis, the research community is hindered by the limitations of the currently available databases, which mostly consist of videos or images with limited labeling. In this paper, we introduce GaitMotion, a multitask dataset leveraging wearable sensors to capture the patients' real-time movement with pathological gait. This dataset offers extensive ground-truth labeling for multiple tasks, including step/stride segmentation and step/stride length prediction, empowers researchers with a more holistic understanding of gait disturbances linked to neurological impairments. The wearable gait analysis suit captures the gait cycle, pattern, and parameters for both normal and pathological subjects. This data may prove beneficial for healthcare products focused on patient progress monitoring and post-disease recovery, as well as for forensics technologies aimed at person reidentification, and biomechanics research to aid in the development of humanoid robotics. Moreover, the analysis has considered the drift in data distribution across individual subjects. This drift can be attributed to each participant's unique behavioral habits or potential displacement of the sensor. Stride length variance for normal, Parkinson's, and stroke patients are compared to recognize the pathological walking pattern. As the baseline and benchmark, we provide an error of 14.1, 13.3, and 12.2 centimeters of stride length prediction for normal, Parkinson's, and Stroke gaits separately. We also analyzed the gait characteristics for normal and pathological gaits in terms of the gait cycle and gait parameters.<|reference_end|>
@article{zhang2024gaitmotion:,
    title={GaitMotion: A Multitask Dataset for Pathological Gait Forecasting},
    author={Wenwen Zhang, Hao Zhang, Zenan Jiang, Jing Wang, Amir Servati, Peyman
  Servati},
    journal={arXiv preprint arXiv:2405.09569},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09569},
    primaryClass={eess.SP cs.LG}
}