An Efficient Data Imputation Technique for Human Activity Recognition
This work addresses a domain-specific issue for applications like health monitoring and virtual reality, but it is incremental as it applies an existing method to a known bottleneck.
The paper tackles the problem of incomplete datasets in human activity recognition by proposing a data imputation technique that uses k-Nearest Neighbors to extrapolate missing samples, resulting in improved recognition of daily living activities.
The tremendous applications of human activity recognition are surging its span from health monitoring systems to virtual reality applications. Thus, the automatic recognition of daily life activities has become significant for numerous applications. In recent years, many datasets have been proposed to train the machine learning models for efficient monitoring and recognition of human daily living activities. However, the performance of machine learning models in activity recognition is crucially affected when there are incomplete activities in a dataset, i.e., having missing samples in dataset captures. Therefore, in this work, we propose a methodology for extrapolating the missing samples of a dataset to better recognize the human daily living activities. The proposed method efficiently pre-processes the data captures and utilizes the k-Nearest Neighbors (KNN) imputation technique to extrapolate the missing samples in dataset captures. The proposed methodology elegantly extrapolated a similar pattern of activities as they were in the real dataset.