Enabling Cost Efficiency in Sensor-Based Human Activity Recognition

Julkaisun otsikon käännös: Enabling Cost Efficiency in Sensor-Based Human Activity Recognition

Tutkimustuotos: Doctoral ThesisCollection of Articles


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. The miniaturization of motion sensors and the commercial availability of low-cost and general-purpose computing devices, such as the Arduino, are the principal factors related to hardware driving the increase in interest for HAR. From the software perspective, the recent employment of deep learning 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. First, we presented a novel solution for alleviating the degradation in performance across subjects without resorting to extensive amounts of data collection sessions with several subjects. Second, we devised novel neural network architectures to improve computational resource efficiency in both inference and training scenarios. Finally, we developed a simulation-driven platform aimed at creating HAR systems in a significantly simpler and lighter way. The results of this work establish a basis for enabling cost efficiency in HAR.
Julkaisun otsikon käännösEnabling Cost Efficiency in Sensor-Based Human Activity Recognition
Myöntävä instituutio
  • Aalto-yliopisto
  • Xiao, Yu, Vastuuprofessori
  • Diaz-Kommonen, Lily, Ohjaaja
Painoksen ISBN978-952-64-1030-2
Sähköinen ISBN978-952-64-1031-9
TilaJulkaistu - 2022
OKM-julkaisutyyppiG5 Artikkeliväitöskirja


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