UltaNet: A antithesis neural network for Recognizing Human Activity Using Inertial Sensors Signals

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

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

Electronics ◽

10.3390/electronics10141715 ◽

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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RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2021.3073969 ◽

2021 ◽

pp. 1-1

Author(s):

Zhe Chen ◽

Chao Cai ◽

Tianyue Zheng ◽

Jun Luo ◽

Jie Xiong ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition

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

annals of telecommunications - annales des télécommunications ◽

10.1007/s12243-021-00865-9 ◽

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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