Public defence in Interactive systems, M.Sc. Clayton Souza Leite

The title of the thesis is Enabling Cost Efficiency in Sensor-Based Human Activity Recognition

M.Sc. Clayton Souza Leite will defend the thesis "Enabling Cost Efficiency in Sensor-Based Human Activity Recognition" on 19 January 2023 at 1 p.m. (EET) in Aalto University School of Electrical Engineering, Department of Communications and Networking, in lecture hall TU1, Maarintie 8, Espoo.

Opponent: Prof. Thomas Plötz, Georgia Institute of Technology, USA
Custos: Prof. Yu Xiao, Aalto University School of Electrical Engineering, Department of Communications and Networking

Thesis available for public display 10 days prior to the defence at:
Doctoral theses in the School of Electrical Engineering:

Public defence announcement:

Sensor-based human activity recognition (HAR) involves artificial intelligence methods to automatically infer the class of movements a user is conducting based on sensor readings. It has recently experienced a surge in interest from both the industry and academic sectors, with applications ranging from entertainment systems to healthcare technologies.

From the software perspective, the recent employment of deep learning — a class of artificial intelligence methods — in HAR has enabled unprecedented recognition performance and facilitated the feature engineering process. However, despite these recent advances, HAR still faces limitations and challenges, particularly from the software perspective. First, since data collection is an onerous task, the data-hungry characteristic of deep learning results in a challenging process of developing activity recognition algorithms. Second, the enormous demand for computational resources in deep learning constitutes a barrier to enabling ubiquitous HAR since the deployment of HAR algorithms in resource-constrained devices like wearables is severely hindered. Additionally, the creation process of HAR systems is immensely labor-intensive, comprising several iterative sessions of designing, prototyping, deploying, and evaluating both algorithm and hardware design.

In this dissertation, the focus is on developing methods to enable cost-efficient HAR from the standpoints of data collection, computational resources, and the creation process of HAR systems. The results of this work establish a basis for enabling cost efficiency in HAR.

Contact information of doctoral candidate:

Email [email protected]
Mobile 0504716045
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