scholarly journals AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition

2019 ◽  
Author(s):  
HaoJie Ma ◽  
Wenzhong Li ◽  
Xiao Zhang ◽  
Songcheng Gao ◽  
Sanglu Lu
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Domain Knowledge ◽  
Research Problem ◽  
Human Activity Recognition ◽  
Attention Mechanism ◽  
The Rich ◽  
Public Datasets ◽  
Gated Recurrent Units

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.

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.

scholarly journals Similarity Embedding Networks for Robust Human Activity Recognition

2021 ◽  
Vol 15 (6) ◽  
pp. 1-17
Author(s):  
Chenglin Li ◽  
Carrie Lu Tong ◽  
Di Niu ◽  
Bei Jiang ◽  
Xiao Zuo ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Real Space ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Activity Data ◽  
Extensive Evaluation ◽  
Sensor Signals ◽  
Public Datasets

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.

scholarly journals Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

2021 ◽  
Author(s):  
Muhammad Muaaz ◽  
Ali Chelli ◽  
Martin Wulf Gerdes ◽  
Matthias Pätzold
Keyword(s):  
Neural Network ◽  
Information Systems ◽  
Convolutional Neural Network ◽  
Health Information ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Information Systems ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Ratio Method

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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