Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking
This work addresses the challenge of improving activity recognition for applications like assisted living and security, but it appears incremental as it builds on existing feature representation methods.
The paper tackled the problem of accurately recognizing human activities by proposing a new feature representation based on consecutive observations, which outperformed baselines and achieved better accuracy for both frequent and infrequent actions, leading to state-of-the-art results on a HAR dataset.
The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors). There is a wide range of applications within the field; for instance, assisted living, security surveillance, and intelligent transportation. In HAR, the development of Activity Recognition models is dependent upon the data captured by these devices and the methods used to analyse them, which directly affect performance metrics. In this work, we address the issue of accurately recognising human activities using different Machine Learning (ML) techniques. We propose a new feature representation based on consecutive occurring observations and compare it against previously used feature representations using a wide range of classification methods. Experimental results demonstrate that techniques based on the proposed representation outperform the baselines and a better accuracy was achieved for both highly and less frequent actions. We also investigate how the addition of further features and their pre-processing techniques affect performance results leading to state-of-the-art accuracy on a Human Activity Recognition dataset.