The analysis of human motion is a challenging research domain that attracts the attention of researchers from several disciplines, including socio-psychology, neuro-biology, and computer science. A successful recognition of the person’s walk could be used for personal identification, and also, would be important for understanding the human’s emotions, personality, and neurological disorders. However, recognising the human gaits is a challenging task because of the complexity of the eventual analytical model that defines the numerical relationship between the relevant features of the gait. In our work we propose an approach of applying genetic programming to automatically design such a model in a way much similar to the evolution in nature. The experimental results verify that genetic programming could be successfully applied for the recognition of human gaits. For more on this research please refer to the associated research papers below:
Further Readings
- D. G. Sharma, R. Yusuf, I. Tanev, and K. Shimohara, “Evaluation of Genetic Programs in Multiple Cases Evolved for Gait Classification and Recognition”, Institute of Electrical Engineers of Japan (IEEJ) Transactions on Electronics, Information and Systems, Vol. 136, No. 9, pp. 1400-1410, 2016
- D. G. Sharma, R. Yusuf, I. Tanev, and K. Shimohara, “Human Gait Analysis based on Biological Motion and Evolutionary Computing”, Journal of Artificial Life and Robotics, Vol. 21, Issue 2, pp 188–194, June 2016
- D. G. Sharma, R. Yusuf, I. Tanev, and K. Shimohara, “Human Recognition based on Gait Features and Genetic Programming”, Journal of Robotics, Networking and Artificial Life, Vol. 1, No.3, pp. 194-197, Dec. 2014