Human activity recognition framework improves wearable sensor accuracy
An Effective, Reliable, and Robust Framework for Human Activity Recognition Using Wearable Sensors
Machine Learning
Summary
Recognizing human activities using wearable sensors can help improve health monitoring and daily life assistance. The authors found that existing methods struggle when data is limited or when activities are very similar, especially for patients like older adults. They created a new method that better uses information from multiple sensors and improves data variety without distorting it. Their approach works more reliably and accurately across several different activity datasets than previous methods.
What this means in practice
- •For healthcare monitoring teams: Improve activity classification for older or hospitalized patients using wearable sensors with more reliable and robust models that handle limited or noisy data.
- •For wearable device developers: Enhance activity recognition software by integrating a new data augmentation technique that maintains realistic sensor data relationships to boost model accuracy.$Commercial implications: Enables more accurate and robust activity tracking features in consumer and medical wearables, improving product value for device manufacturers.
Authors
Nafees Ahmad, Ho-fung Leung, Muhammad Adil Abid, Sadia Shakil
Abstract
Human Activity Recognition (HAR) through wearable sensors greatly improves the quality of human life through its multiple applications. For HAR, multi-sensor channel information is vital for optimal performance. Current work states that applying an attention neural network to prioritize discriminatory sensor channels helps the model classify activity more precisely. However, obtaining discriminatory information from multisensory channels is not always trivial, such as when collecting data from older hospitalized patients. In this context, existing HAR methods struggle to classify activities, particularly activities with similar natures. Moreover, HAR models predominantly suffer from overfitting due to the small size of available datasets, which leads to poor performance. Data augmentation (DA) is a viable solution to this problem. However, available DA methods have various drawbacks, including the possibility of being domain-dependent, resulting in distorted models for test sequences. To address these HAR problems, we propose a novel framework, ALAE-TAE-CutMix+, which focuses on two aspects. First, it enhances the latent information across each sensor channel and learns to exploit the relation among multiple latent features and the ongoing activity. Consequently, the discriminatory feature representations of each activity is enriched. Second, a new augmentation strategy is introduced to address the shortcomings of existing multi-sensor channel data augmentation. We then extend the framework to create a further enhanced version, namely ALAE-CIE-TAE-CutMix+, which learns to capture the interactions between the features of each pair of sensor channels. We find that although the first framework performs slightly better than the latter, the latter is nonetheless more reliable and robust. Both frameworks significantly outperform SOTA approaches on the four HAR datasets from diverse domains.