An Efficient Data Imputation Technique for Human Activity Recognition

TL;DR

This paper introduces a KNN-based data imputation method to fill missing samples in human activity datasets, improving the accuracy of activity recognition systems.

Contribution

It presents a novel application of KNN imputation for dataset completion, enhancing human activity recognition performance.

Findings

Effective extrapolation of missing activity data

Improved recognition accuracy with imputed data

Preservation of activity patterns in imputation

Abstract

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.