Abstract
Traditional vision-based systems used for automatic gait pathology detection, associate high-cost. However, with the advent of Microsoft Kinect sensor, researchers tried to model some low-cost gait assessment systems; but they suffer from the device-specific generic constraints. This study attempted to mitigate those pitfalls by introducing a noble multi-Kinect setup for automated gait diagnosis. Ten healthy participants were recruited to simulate pathological gait. Extracted salient features were classified using supervised learning, leading to an overall accuracy of 93%, which outperformed state-of-the-art.
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Acknowledgements
We would like to be extremely thankful to Science and Engineering Research Board (SERB), DST, Govt. of India to partially support this research work. The Kinect V2 sensors used in our research experiment were purchased from the project, funded by SERB with FILE NO: ECR/2017/000408.
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Chakraborty, S., Mishra, R., Dwivedi, A., Das, T., Nandy, A. (2020). A Low-Cost Pathological Gait Detection System in Multi-Kinect Environment. In: Bhattacharya, I., Otani, Y., Lutz, P., Cherukulappurath, S. (eds) Progress in Optomechatronics. Springer Proceedings in Physics, vol 249. Springer, Singapore. https://doi.org/10.1007/978-981-15-6467-3_13
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