Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer Learning

Abstract

Human activity recognition (HAR) aims at recognizing activities by training models on the large quantity of sensor data. Since it is time-consuming and expensive to acquire abundant labeled data, transfer learning becomes necessary for HAR by transferring knowledge from existing domains. However, there are two challenges existing in cross-dataset activity recognition. The first challenge is source domain selection. Given a target task and several available source domains, it is difficult to determine how to select the most similar source domain to the target domain such that negative transfer can be avoided. The second one is accurately activity transfer. After source domain selection, how to achieve accurate knowledge transfer between the selected source and the target domain remains another challenge. In this paper, we propose an Adaptive Spatial-Temporal Transfer Learning (ASTTL) approach to tackle both of the above two challenges in cross-dataset HAR. ASTTL learns the spatial features in transfer learning by adaptively evaluating the relative importance between the marginal and conditional probability distributions. Besides, it captures the temporal features via incremental manifold learning. Therefore, ASTTL can learn the adaptive spatial-temporal features for cross-dataset HAR and can be used for both source domain selection and accurate activity transfer. We evaluate the performance of ASTTL through extensive experiments on 4 public HAR datasets, which demonstrates its effectiveness. Furthermore, based on ASTTL, we design and implement an adaptive cross-dataset HAR system called Client-Cloud Collaborative Adaptive Activity Recognition System (3C2ARS) to perform HAR in the real environment. By collecting activities in the smartphone and transferring knowledge in the cloud server, ASTTL can significantly improve the performance of source domain selection and accurate activity transfer.

Designing ECG monitoring healthcare system with federated transfer learning and explainable AI

“…The key idea is to reduce the distribution divergence between different models. To this end, there are mainly two general approaches: instance reweighting [44] and feature matching [45]. Recently, deep transfer learning methods have made considerable success in many application fields.…”

Unsupervised Domain Adaptation in Activity Recognition: A GAN-Based Approach

“…The knowledge transfer is performed per activity class by using multiple transfer kernels to project the source and target domain's feature spaces to a common subspace. Qin et al [32] propose Adaptive Spatial-Temporal Transfer Learning (ASTTL) to allow more accurate source selection to perform domain adaption. Chang et al [4] have looked into feature matching and adversarial learning in adapting the activity model from one sensor position to another.…”

“…Chang et al [4] have looked into feature matching and adversarial learning in adapting the activity model from one sensor position to another. These recent techniques are built on a similar assumption that both source and target domains share the same feature space so that they can share the same activity model [6] or feature extractor [4], [32]. Domain adaptation on binary sensor data is different from accelerometer data as the main challenge is to tackle heterogeneity of feature spaces.…”

Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things

“…The goal of transfer learning is to transfer information from known related fields to new fields, so as to achieve the purpose of analogical reasoning, and the main goal is to reduce the distribution differences between different fields. Therefore, there are two main implementation methods: instance reweighting [19] and feature matching [20]. Recently, deep transfer learning technology has made great achievements in many applications.…”