  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.
