Unobtrusive Activity Recognition and Position Estimation for Work Surfaces Using RF-Radar Sensing

Avrahami, Daniel; Patel, Mitesh; Yamaura, Yusuke; Kratz, Sven; Cooper, Matthew

doi:10.1145/3241383

Cited by 19 publications

(33 citation statements)

References 49 publications

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“…A work proposed by Tapia, M. et al [14] employs numerous simple and binary sensors at a smart home for detection of in-home activities such as bathing, cleaning, etc. Recent work such as [15] employed RF sensors mounted under the work surface for recognizing clerk and desk-work activities under real office settings. In the above works, the wearable devices are completely integrated into the environment and are invisible from the users.…”

Section: A Unobtrusive Technologies For Harmentioning

confidence: 99%

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

Pham¹,

Nguyen-Thai²,

Huy

et al. 2020

IEEE Access

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This research was funded by Vietnam Ministry of Science and Technology under grant number DTDLCN-16/18 ''Automated Respiration Symptoms monitoring and Abnormal Human Activity Detection Using the Internet of Things''. ABSTRACT Recently, the recent advancement of deep learning with the capacity to perform automatic highlevel feature extraction has achieved promising performance for sensor-based human activity recognition (HAR). Among different deep learning methods, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) have been widely adopted. However, scalar outputs and pooling in CNN only allow to get the invariance but not the equivariance. The capsule networks (CapsNet) with the vector output and routing by agreement is able to capture the equivariance. In this paper, we propose a method for recognizing human activity from wearable sensors based on a capsule network named SensCapsNet. The architecture of SensCapsNet is designed to be suitable for spatial-temporal data coming from wearable sensors. Experimental results show that the proposed network outperforms CNN and LSTM methods. The performance of the proposed CapsNet architecture is assessed by altering dynamic routing between capsule layers. The proposed SensCapsNet yields improved accuracy values of 77.7% and 70.5% for 1 routing on two testing datasets in comparison with the baseline methods based on CNN and LSTM that yields the F1score of 67.7% and 69.2% for the first dataset and 65.3% and 67.6% for the second dataset respectively. Moreover, even several human activity datasets are available, privacy invasion and obtrusive concerns have not been carefully taken in to consideration in dataset building. Toward to build a non-obstructive sensing based human activity recognition method, in this paper, a dataset named 19NonSens is designed and collected from twelve subjects wearing e-Shoes and a smart watch to perform 19 activities under multiple contexts. This dataset will be made publicity available. Finally, thanks to the promising results obtained by the proposed method, we develop a life logging application which achieves a real-time computation and the accuracy rate greater than 80% for 5 common upper body activities. INDEX TERMS Human activity recognition, capsule net, wearable sensors.

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Section: A Unobtrusive Technologies For Harmentioning

confidence: 99%

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

Pham¹,

Nguyen-Thai²,

Huy

et al. 2020

IEEE Access

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“…Radar-based vital signs detection has been used for breathing disorder (BD) recognition and sleep stages (SS) [65,66]. In [67], radar is used first to recover individual heartbeats from radio frequency (RF) reflections, and subsequently, it uses the heartbeat sequence along with the breathing signal to recognize the person’s emotions such as sadness, anger, pleasure, and joy.…”

Section: Literature Reviewmentioning

confidence: 99%

A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life

Rahman

Rashid

Kernec

et al. 2019

Sensors

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Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as “after cancer treatment”. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient’s situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one’s home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient’s data available from an in-home secure monitoring system for a better understanding of a patient’s current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development.

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“…A research group of Massachusetts Institute of Technology (MIT) Adib et al designed MIMO antenna sensor with FMCW technique to detect human move [15] and even capture human figure through a wall [16], [17]. Off-the-shelf devices such as ultrasonic sensor or walabot [22] were also investigated feasibility to recognize human activities [19]- [21]. Avrahami et al proposed a human activity recognition scheme based on 2D heat maps generated by walabot, while Zhu et al [20] applied traditional signal processing algorithms to filter and cluster raw data thus recognize human actions.…”

Section: B Rf Solutionsmentioning

confidence: 99%

Real-Time Indoor 3D Human Imaging Based on MIMO Radar Sensing

Guo

Zhang

Shi

et al. 2019

2019 IEEE International Conference on Multimedia and Expo (ICME)

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Most smart systems such as smart home and smart health response to human's locations and activities. However, traditional solutions are either require wearable sensors or lead to leaking privacy. This work proposes an ambient radar solution which is a real-time, privacy secure and dark surroundings resistant system. In this solution, we use a low power, Frequency-Modulated Continuous Wave (FMCW) radar array to capture the reflected signals and then construct to 3D image frames. This solution designs 1)a data preprocessing mechanism to remove background static reflection, 2)a signal processing mechanism to transfer received complex radar signals to a matrix contains spacial information, and 3) a Deep Learning scheme to filter broken frame which caused by the rough surface of human's body. This solution has been extensively evaluated in a research area and captures real-time human images that are recognizable for specific activities. Our results show that the indoor capturing is clear to be recognized frame by frame compares to camera recorded video.

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Unobtrusive Activity Recognition and Position Estimation for Work Surfaces Using RF-Radar Sensing

Cited by 19 publications

References 49 publications

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

SensCapsNet: Deep Neural Network for Non-Obtrusive Sensing Based Human Activity Recognition

A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life

Real-Time Indoor 3D Human Imaging Based on MIMO Radar Sensing

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