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Mixing temporal experts for Human Activity Recognition
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-6780-7755
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2022 (English)In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 11-18Conference paper, Published paper (Refereed)
Abstract [en]

Temporal patterns are encoded within the time-series data, and neural networks, with their unique feature extraction ability, process those patterns to provide a better predictive response. Ensembles of neural networks have proven to be very effective Human Activity Recognition (HAR) tasks with time-series data, e.g., wearable sensors. The combination of predictions coming from the individual models in the ensemble helps boost the overall classification metric through efficient temporal pattern recognition. Currently, the most common strategy for combining the predictions coming from the individual models is simple averaging. However, since each ensemble model learns different temporal patterns of the timseries classification problem, a simple averaging strategy is sub-optimal. This sub-optimality is addressed in this paper through a neural network-based adaptive learning framework. The method's core is training a neural gate that ingests the same input time-series data fed to the other temporal models. The goal of the training process is to adaptively learn scaler values against each temporal model by looking at the input data. These scaler values weigh each temporal model while combining the ensemble. The framework obtains superior predictive performance as compared to the standard ensembling techniques. The framework is evaluated on a benchmark HAR dataset called PAMAP2 [3] with two popular state-of-the-art ensemble architectures namely DTE [1] and LSTM-ensemble [2]. In both cases, the classification performance of the framework in HAR tasks surpasses the state-of-the-art models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 11-18
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-319433DOI: 10.1109/SAIS55783.2022.9833028ISI: 000855561800002Scopus ID: 2-s2.0-85136111184OAI: oai:DiVA.org:kth-319433DiVA, id: diva2:1699926
Conference
34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Stockholm, 13 June 2022, through 14 June 2022
Note

QC 20220929

Part of proceedings: ISBN 978-1-6654-7126-8

Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-04-12Bibliographically approved
In thesis
1. Towards Trustworthy Machine Learning For Human Activity Recognition
Open this publication in new window or tab >>Towards Trustworthy Machine Learning For Human Activity Recognition
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Human Activity Recognition presents a multifaceted challenge, encompassing the complexity of human activities, the diversity of sensors used, and the imperative to safeguard user data privacy. Recent advancements in machine learning, deep learning, and sensor technology have opened up new possibilities for human activity recognition. Wearable sensor-based human activity recognition involves collecting time-series data from various sensors, capturing intricate aspects of human activities. The focus of the above activity recognition problem is classifying human activities from the time-series data. Hence, this time-series classification problem demands efficient utilization of temporal properties. Moreover, while accurate prediction is crucial in human activity recognition, the reliability of predictions often goes unnoticed. Ensuring that predictions are reliable involves addressing two issues: calibrating miscalibrated predictions that fail to accurately represent the true likelihood of the data and addressing the challenges around uncertain predictions. Modern deep learning models, used extensively in human activity recognition, often struggle with the above issues. In addition to reliability concerns, machine learning algorithms employed in Human Activity Recognition are also plagued by privacy issues stemming from the utilization of sensitive activity data during model training. While existing techniques such as federated learning can provide some degree of privacy protection in these scenarios, they tend to adhere to a uniform concept of privacy and lack quantifiable privacy metrics that can be effectively conveyed to users and customized to cater to their individual privacy preferences. Hence, in the thesis, we identify the challenges around the effective use of temporal data, reliability, and privacy issues of machine learning models used for wearable sensor-based human activity recognition. To tackle these challenges, we put forth novel solutions, striving to enhance the overall performance and trustworthiness of machine learning models employed in human activity recognition.

Firstly, to improve classification performance, we propose a new temporal ensembling framework that uses data temporality effectively. The framework accommodates various window sizes for time-series data and trains an ensemble of deep-learning models based on that. It enhances classification accuracy and preserves temporal information.

Secondly, we address reliability through calibration and uncertainty estimation. The aforementioned temporal ensembling framework is used for calibration and uncertainty estimation. It provides well-calibrated predictions for human activity recognition and detects out-of-distribution activities, an important task of uncertainty estimation. Furthermore, we apply these methods to real-world scenarios, enhancing the reliability of human activity recognition models.

Thirdly, to address the privacy concern, we introduce a differentially private framework for time-series human activity recognition, quantifying privacy. Additionally, we develop a collaborative federated learning framework, allowing users to define their privacy preferences, advancing privacy preservation in human activity recognition.

These contributions address major challenges and promote improved classification, reliability, and privacy preservation in human activity recognition. It helps us to move towards trustworthy machine learning in human activity recognition, facilitating their usage in realistic and practical scenarios.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2024. p. xii, 56
Series
TRITA-EECS-AVL ; 2024:12
National Category
Computer Sciences
Research subject
Computer Science; Information and Communication Technology
Identifiers
urn:nbn:se:kth:diva-343130 (URN)978-91-8040-826-4 (ISBN)
Public defence
2024-03-06, https://kth-se.zoom.us/j/63687967257, Sal C, Kistagången 16, Kista, Stockholm, 13:00 (English)
Opponent
Supervisors
Funder
EU, Horizon 2020, 813162
Note

QC 20240207

Available from: 2024-02-07 Created: 2024-02-07 Last updated: 2024-02-29Bibliographically approved

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Roy, DebadityaGirdzijauskas, Sarunas

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