Transfer learning for multi-channel time-series Human Activity Recognition
Creators: name: Moya Rueda, Fernando
Publisher: name: TU Dortmund
Year: 2023
Subjects: subject: Human Activity Regognition (HAR)
subject: Deep neural networks
subject: Human activity retrieval
subject: Transfer learning
subject: Multi-channel times series
subject: Data augmentation for HAR
subject: 004
subject: Motion Capturing
subject: Bewegungsanalyse
subject: Bewegungsablauf
subject: Merkmalsextraktion
subject: Neuronales Netz
subject: Deep learning

description: Abstract for the PHD Thesis Transfer Learning for Multi-Channel Time-Series Human Activity Recognition Methods of human activity recognition (HAR) have been developed for the purpose of automatically classifying recordings of human movements into a set of activities. Capturing, evaluating, and analysing sequential data to recognise human activities accurately is critical for many applications in pervasive and ubiquitous computing applications, e.g., in applications such as mobile- or ambient-assisted living, smart-homes, activities of daily living, health support and rehabilitation, sports, automotive surveillance, and industry 4.0. For example, HAR is particularly interesting for optimisation in those industries where manual work remains dominant. HAR takes as inputs signals from videos or from multi-channel time-series, e.g., human joint measurements from marker-based motion capturing systems and inertial measurements measured by wearables or on-body devices. Wearables have become relevant as they extend the potential of HAR beyond constrained or laboratory settings. This thesis focuses on HAR using multi-channel time-series. Multi-channel Time-Series HAR is, in general, a challenging classification task. This is because human activities and movements show a large variation. Humans carry out in similar manner activities that are semantically very distinctive; conversely, they carry out similar activities in many different ways. Furthermore, multi-channel Time-Series HAR datasets suffer from the class unbalance problem, with more samples of certain activities than others. This problem strongly depends on the annotation. Moreover, there are non-standard definitions of human activities for annotation. Methods based on Deep Neural Networks (DNNs) are prevalent for Multi-channel Time-Series HAR. Nevertheless, the performance of DNNs has not significantly increased compared to as other fields such as image classification or segmentation. DNNs present a low sample efficiency as they learn the temporal structure from activities completely from data. Considering supervised DNNs, the scarcity of annotated data is the primary concern. Annotated data from human behaviour is scarce and costly to obtain. The annotation process demands enormous resources. Additionally, annotation reliability varies because they can be subject to human errors or unclear and non-elaborated annotation protocols. Transfer learning has been used to cope with a limited amount of annotated data, overfitting, zero-shot learning or classification of unseen human activities, and the class-unbalance problem. Transfer learning can alleviate the problem of scarcity of annotated data. Learnt parameters and feature representations from a specific source domain are transferred to a target domain. Transfer learning extends the usability of large annotated data from source domains to related problems. This thesis proposes a general transfer learning approach to improve automatic multi-channel Time-Series HAR. The proposed transfer learning method combines a semantic attribute representation of activities and a specific deep neural network. It handles situations where the source and target domains differ, i.e., the sensor space and the set of activities change, without needing a large amount of annotated data from the target domain. The method considers different levels of transferability. First, an architecture handles a variate of dataset configurations in regard to the number of devices and their type; it creates fixed-size representations of sensor recordings that are representative of the human limbs. These networks will process sequences of movements from the human limbs, either from poses or inertial measurements. Second, it introduces a search of semantic attribute representations that favourably represent signal segments for recognising human activities in unknown scenarios, as they only consider annotations of activities, and they lack human-annotated semantic attributes. And third, it covers transferability from data of a variety of source datasets. The method takes advantage of a large human-pose dataset as a source domain, which is created during the develop of this thesis. Furthermore, synthetic-inertial measurements will be derived from sequences of human poses either from a marker-based motion capturing system or video-based HAR and pose-based HAR datasets. The latter will specifically use the annotations of pixel-coordinate of human poses as multi-channel time-series data. Real inertial measurements and these synthetic measurements will then be deployed as a source domain for parameter transfer learning. Experimentation on different target datasets demonstrates that the proposed transfer learning method improves performance, most evidently when deploying a proportion of their training material. This outcome suggests that the temporal convolutional filters are rather general as they learn local temporal relations of human movements related to the semantic attributes, independent of the number of devices and their type. A human-limb-oriented deep architecture and an evolutionary algorithm provide an out-of-the-shelf predictor of semantic attributes that can be deployed directly on a new target scenario. Very related problems can directly be addressed by manually giving the attribute-to-activity relations without the need for a search throughout an evolutionary algorithm. Besides, the learnt convolutional filters are activity class dependent. Hence, the classification performance on the activities shared among the datasets improves.