Meta-learning meets the Internet of Things: Graph prototypical models for sensor-based human activity recognition

TL;DR: In this paper , a multi-level feature fusion technique for multimodal human activity recognition using multi-head CNN with Convolution Block Attention Module (CBAM) to process the visual data and Convolutional Long Short Term Memory (ConvLSTM) for dealing with the time-sensitive multi-source sensor information.

TL;DR: A novel FSOD taxonomy method is proposed and the plentifully remarkable FSOD algorithms are surveyed based on this fact to report a comprehensive overview that facilitates a deeper understanding of the FSOD problem and the development of innovative solutions.

TL;DR: In this paper , the authors proposed a novel device-free method based on Time-Streaming Multiscale Transformer (TransTM), which leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.

TL;DR: The motive of the proposed concept is to address limitations by connecting the sensors with an Internet of Things network and cloud platform for remote recording and monitoring purposes by utilizing the Blynk IoT application and cloud server for the analytics.

TL;DR: This study presents a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition and proposes a new taxonomy to structure the deep methods by challenges.

TL;DR: This paper provides a framework for few-shot learning by introducing dynamic classifiers that are constructed from few samples and empirically shows that such modelling leads to robustness against perturbations and yields competitive results on the task of supervised and semi-supervised few- shot classification.

TL;DR: A new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection is presented and shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used.

TL;DR: The mHealthDroid as discussed by the authors is an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of biomedical apps, which leverages the potential of mobile devices like smartphones or tablets, wearable sensors and portable biomedical devices.