Dec 28, 2021 · We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs.
Robust Human Activity Recognition by Integrating Image and ... - PubMed
pubmed.ncbi.nlm.nih.gov › ...
We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer ...
Jul 24, 2024 · We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer ...
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Jul 23, 2024 · We propose Centaur, a multimodal fusion model for human activity recognition (HAR) that is robust to these data quality issues. Centaur combines ...
The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy.
The experimental results indicate that the proposed scheme achieves better recognition results as compared to the state of the art, and the feature-level ...
Mar 8, 2023 · Noise and missing data impose a significant challenge for the effective fusion of data from multiple sensors with different modali- ties. This ...
Jul 1, 2022 · Wearable sensor based human activity recognition (HAR) has a broad range of applications in healthcare, fitness, smart home, ...
We assemble signal sequences of accelerometers and gyroscopes into a novel activity image, which enables Deep Convolutional Neural Networks (DCNN) to ...
Jul 4, 2024 · This research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data.