Abstract
Gait, a biological trait, is extremely valuable for identifying personnel. Identifying gait is a complex process as it needs a combination of sensor data and efficient learning algorithms. In this paper, we incorporated activities of daily living data for recognizing gait using multiple shallow and ensemble learning models. The proposed model includes various state-of-the-art human activity recognition datasets in order to create a gait recognition database based on daily activities like—walking and running. The dataset is processed using different pre-processing techniques like data normalization, solving class imbalance problems, and removing noise, corrupted data, and outliers, as well. Different shallow and ensemble models are incorporated for training and testing, and the performance is measured using the evaluation metrics like Precision, Recall, F1-Score, and Accuracy. The included model achieved the highest accuracy of 99.60% with CatBoost Classifier. Also, other ensemble models managed to perform optimally for both walking and running activity instances to recognize gait.
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Singh, S., Choudhury, N.A., Soni, B. (2023). Gait Recognition Using Activities of Daily Livings and Ensemble Learning Models. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_20
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DOI: https://doi.org/10.1007/978-981-99-5085-0_20
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