Activity and Subject Detection for UCI HAR Dataset with & without missing Sensor Data

TL;DR

This paper introduces a lightweight LSTM model for human activity and subject recognition using UCI HAR data, addressing missing sensor data with imputation techniques, and achieves high accuracy close to benchmarks.

Contribution

The study presents a novel LSTM-based approach for simultaneous activity and subject recognition, including methods to handle missing sensor data with imputation and PCA.

Findings

Achieved 93.89% activity recognition accuracy, close to the benchmark of 96.67%.

Achieved 80.19% subject recognition accuracy, establishing a new baseline.

KNN imputation outperforms PCA in restoring missing sensor data.

Abstract

Current studies in Human Activity Recognition (HAR) primarily focus on the classification of activities through sensor data, while there is not much emphasis placed on recognizing the individuals performing these activities. This type of classification is very important for developing personalized and context-sensitive applications. Additionally, the issue of missing sensor data, which often occurs in practical situations due to hardware malfunctions, has not been explored yet. This paper seeks to fill these voids by introducing a lightweight LSTM-based model that can be used to classify both activities and subjects. The proposed model was used to classify the HAR dataset by UCI [1], achieving an accuracy of 93.89% in activity recognition (across six activities), nearing the 96.67% benchmark, and an accuracy of 80.19% in subject recognition (involving 30 subjects), thereby establishing a new baseline for this area of research. We then simulate the absence of sensor data to mirror real-world scenarios and incorporate imputation techniques, both with and without Principal Component Analysis (PCA), to restore incomplete datasets. We found that K-Nearest Neighbors (KNN) imputation performs the best for filling the missing sensor data without PCA because the use of PCA resulted in slightly lower accuracy. These results demonstrate how well the framework handles missing sensor data, which is a major step forward in using the Human Activity Recognition dataset for reliable classification tasks.