Cross-subject transfer learning in human activity recognition systems using generative adversarial networks
E Soleimani, E Nazerfard - Neurocomputing, 2021 - Elsevier
Application of intelligent systems especially in smart homes and health-related topics has
been drawing more attention in the last decades. Training Human Activity Recognition …
been drawing more attention in the last decades. Training Human Activity Recognition …
Dropout as an implicit gating mechanism for continual learning
SI Mirzadeh, M Farajtabar… - Proceedings of the …, 2020 - openaccess.thecvf.com
In recent years, neural networks have demonstrated an outstanding ability to achieve
complex learning tasks across various domains. However, they suffer from the" catastrophic …
complex learning tasks across various domains. However, they suffer from the" catastrophic …
Human Activity Recognition based on Local Linear Embedding and Geodesic Flow Kernel on Grassmann manifolds
H Wang, J Yang, C Cui, P Tu, J Li, B Fu… - Expert Systems with …, 2024 - Elsevier
Abstract Human Activity Recognition (HAR) plays a crucial role in various applications (eg,
medical treatment, video surveillance and sports monitoring). Transfer learning is a …
medical treatment, video surveillance and sports monitoring). Transfer learning is a …
TransNet: minimally supervised deep transfer learning for dynamic adaptation of wearable systems
Wearables are poised to transform health and wellness through automation of cost-effective,
objective, and real-time health monitoring. However, machine learning models for these …
objective, and real-time health monitoring. However, machine learning models for these …
Source Domain Selection for Cross-House Human Activity Recognition with Ambient Sensors
Human activity recognition using ambient sensors has become particularly important due to
social demands of applications in smart homes. To address the problem of labeling sensing …
social demands of applications in smart homes. To address the problem of labeling sensing …
Personalized activity recognition using partially available target data
Recent years have witnessed a growing body of research on autonomous activity
recognition models for use in deployment of mobile systems in new settings such as when a …
recognition models for use in deployment of mobile systems in new settings such as when a …
Optimal policy for deployment of machine learning models on energy-bounded systems
SI Mirzadeh, H Ghasemzadeh - … of the Twenty-Ninth International Joint …, 2020 - par.nsf.gov
With the recent advances in both machine learning and embedded systems research, the
demand to deploy computational models for real-time execution on edge devices has …
demand to deploy computational models for real-time execution on edge devices has …
Transfer Learning in Human Activity Recognition: A Survey
SG Dhekane, T Ploetz - arXiv preprint arXiv:2401.10185, 2024 - arxiv.org
Sensor-based human activity recognition (HAR) has been an active research area, owing to
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
its applications in smart environments, assisted living, fitness, healthcare, etc. Recently …
Memory-aware active learning in mobile sensing systems
We propose a novel active learning framework for activity recognition using wearable
sensors. Our work is unique in that it takes limitations of the oracle into account when …
sensors. Our work is unique in that it takes limitations of the oracle into account when …
ParaLabel: Autonomous parameter learning for cross-domain step counting in wearable sensors
Wearable step counters, also referred to as activity trackers, have been developed for health
and activity monitoring, as well as for step tracking. These trackers, however, produce …
and activity monitoring, as well as for step tracking. These trackers, however, produce …