Resource-Constrained Federated Learning with Heterogeneous Labels and Models for Human Activity Recognition

2021 ◽  
pp. 57-69 ◽  
Author(s):  
Gautham Krishna Gudur ◽  
Satheesh Kumar Perepu
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Resource Constrained

A Partially Binarized Hybrid Neural Network System for Low-Power and Resource Constrained Human Activity Recognition

2020 ◽  
Vol 67 (11) ◽  
pp. 3893-3904
Author(s):  
Antonio De Vita ◽  
Alessandro Russo ◽  
Danilo Pau ◽  
Luigi Di Benedetto ◽  
Alfredo Rubino ◽  
...  
Keyword(s):  
Neural Network ◽  
Low Power ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Network System ◽  
Resource Constrained ◽  
Hybrid Neural Network ◽  
Neural Network System

Depth-based Human Activity Recognition: vINCI Case Study

2020 ◽  
Author(s):  
Lidia Bajenaru ◽  
Ciprian Dobre ◽  
Radu-Ioan Ciobanu ◽  
Georgiana Dedu ◽  
Silviu-George Pantelimon ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1715
Author(s):  
Michele Alessandrini ◽  
Giorgio Biagetti ◽  
Paolo Crippa ◽  
Laura Falaschetti ◽  
Claudio Turchetti
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Network ◽  
Low Cost ◽  
Low Complexity ◽  
Human Activity Recognition ◽  
Motion Artifacts ◽  
Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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