Activity and Subject Detection for UCI HAR Dataset with & without missing Sensor Data
Debashish Saha, Piyush Malik, Adrika Saha

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…
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Taxonomy
TopicsContext-Aware Activity Recognition Systems · Human Pose and Action Recognition · Emotion and Mood Recognition
MethodsFocus · Principal Components Analysis
