Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks

Abstract

Human activity recognition (HAR) research has increased in recent years due to its applications in mobile health monitoring, activity recognition, and patient rehabilitation. The typical approach is training a HAR classifier offline with known users and then using the same classifier for new users. However, the accuracy for new users can be low with this approach if their activity patterns are different than those in the training data. At the same time, training from scratch for new users is not feasible for mobile applications due to the high computational cost and training time. To address this issue, we propose a HAR transfer learning framework with two components. First, a representational analysis reveals common features that can transfer across users and user-specific features that need to be customized. Using this insight, we transfer the reusable portion of the offline classifier to new users and fine-tune only the rest. Our experiments with five datasets show up to 43% accuracy improvement and 66% training time reduction when compared to the baseline without using transfer learning. Furthermore, measurements on the Nvidia Jetson Xavier-NX hardware platform reveal that the power and energy consumption decrease by 43% and 68%, respectively, while achieving the same or higher accuracy as training from scratch.

Personalised Gait Recognition for People with Neurological Conditions

“…Methods such as those proposed by Cook et al [ 23 ] and Ding et al [ 24 ] should be considered. Furthermore, personalisation for clustered groups, a method proposed by An et al [ 25 ], can easily be extended to our method. In their research, they cluster participants using k -means clustering, and they personalise for each cluster.…”

“…These data are first labelled using an improved pseudo-labelling algorithm, after which the models were enhanced for each participant with that personal data, which had a major impact on classification results. In the human activity recognition (HAR) of healthy individuals, transfer learning has been researched, showing promising results [ 23 , 24 , 25 ]. Mainly, these projects show that complex personalisation methods improve recognition for healthy individuals.…”

Sub-transfer Learning in Human Activity Recognition: Boosting the Outlier User Accuracy

“…In recent HAR research, the scientists have been exploring transfer learning [25], where a model developed for a task is repurposed as the starting point for a model on a second task. Transfer learning is a machine learning method where the knowledge from the prior training is transferred to perform a new classification task [26]. In [27], the authors propose a transfer learning framework for HAR, which analyzes common and user-specific features to transfer the reusable portion of the online classifier to new users and fine-tune only the rest.…”

Sub-transfer Learning in Human Activity Recognition: Boosting the Outlier User Accuracy

“…In recent HAR research, the scientists have been exploring transfer learning [25], where a model developed for a task is repurposed as the starting point for a model on a second task. Transfer learning is a machine learning method where the knowledge from the prior training is transferred to perform a new classification task [26]. In [27], the authors propose a transfer learning framework for HAR, which analyzes common and user-specific features to transfer the reusable portion of the online classifier to new users and fine-tune only the rest.…”