Human Activity Classification Based on Dynamic Time Warping of an On-Body Creeping Wave Signal

Li, Yang; Dong, Xue; Forrister, Erik; Lee, George; Garner, Brian; Kim, Youngwook

doi:10.1109/tap.2016.2598199

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…This type of propagation channels occurs under great chance in nearfield range and is heavily coupled by the body [2][3][4], i.e., the propagation environment and the antennas. Conventional far-field propagation theories may not fully describe the complex distribution of on-body channels in the form of surface wave [5,6] and may fail to optimize the communication by missing the propagation components. Studies as [7,8] have shown the high sensitivity of on-body channels to the orientation of the transmission and receiving antennas, implying the effectiveness of polarization diversity with a wearable BAN.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Static Narrowband On-Body Channel Polarization Distribution at 2.4 GHz

Liu

Zhang

Wang

et al. 2018

International Journal of Antennas and Propagation

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The polarization of on-body propagation channels has shown significant spatial selectivity and full-space distribution in numerous measurements due to the near-field characteristics and strong body scattering effect. To get a comprehensive image of such polarization distribution, in this work, we measured polarized on-body channels on static body at 2.4 GHz by monopole antennas, where standing and sitting postures are included. The polarization of the measured on-body antennas covers the full-space in Z-, H-, and V-direction relative to the skin of the body. Consequently, an extended cross-polarization discrimination (XPD) is also proposed to describe the depolarization and copolarization in the measurements. In the statistical characteristics of the channels, the V-polarization direction of the receiving antenna is more conducive for receiving signal and reducing the path losses. The results show that strong depolarization of on-body channels is more easily caused in sitting posture due to scattering effects from the legs. Simplified finite-difference-time-domain (FDTD) simulations are conducted to investigate the field polarization distribution of point sources on a static torso in cylindrical shape. The simulation results are in general consistent with the analysis of measured on-body channels.

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Section: Introductionmentioning

confidence: 99%

Analysis of Static Narrowband On-Body Channel Polarization Distribution at 2.4 GHz

Liu

Zhang

Wang

et al. 2018

International Journal of Antennas and Propagation

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“…Compared with these methods, the LSTM-based technique used in this paper requires no feature engineering. Compared to dynamic time warping (DTW) [42], LSTM features a much better capability to handle complex relations. Compared to deep convolutional neural networks [43], LSTM is essentially better for processing time sequence information and no preparation processing is needed.…”

Section: B Neural Network Processingmentioning

confidence: 99%

“…''Stand'' and ''sit'' have the highest confusion rate because the sensor on the lap is less sensitive due to the larger distance to the receiver on the head and because both activities feature virtually no change in the upper body as it is essentially hard to detect a ''no change''. Compared to other works using RF signal for HAR [42], [43], this paper is the first realization using a truly wireless system with miniaturized antennas that demonstrates a high accuracy to detect and identify a variety of individual activities and an excellent adaptability to different situations. While this method requiring a significantly less amount of information (no need for phase information or multiple frequencies), the accuracy is still very high and comparable to or even better than those using much more information (e.g.87.5/88.3/95.8/75.0% in [42], 97.1/98.8% in [43] (e.g.…”

Section: (C)and(d) With 10 Calibration Samples For Each Activitymentioning

confidence: 99%

Wearable Antennas for Cross-Body Communication and Human Activity Recognition

Zhu

Liao

et al. 2020

IEEE Access

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This paper proposes a novel miniaturized wearable antenna (mosaic antenna) for cross-body communication featuring a superior performance compared to five commonly utilized wearable antenna topologies. The proposed mosaic antenna optimally excites the surface waves to enable cross-body communication yet still with a low profile and a miniaturized size. As a proof-of-concept of the unique capabilities of this antenna, an inkjet-printed flexible wearable prototype on Polytetrafluoroethylene (PTFE, also known as Teflon) has been fabricated in a highly customizable, scalable and low-cost process and has been used as an enabler for human activities recognition coupled with machine learning techniques powered by Google TensorFlow. By utilizing proof-of-concept on-body networks consisting of mosaic antennas, human activity can be detected with an accuracy 91.9% for RF signal-based recognition techniques, despite the variability among different users and environments. INDEX TERMS Additive manufacturing, flexible antenna, human activity recognition, machine learning, minimized antennas, wearable antennas.

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“…However, the work did not look at identifying the user from other users. Interestingly [12], concluded that, measurement at 915 MHz provided higher accuracy for body movement activities as compared with 433 MHz and 2.4 GHz [12]. In an alternative study presented in [13], four types of patch antennas were implemented on a transmitter and receiver to detect heart rate biosignal for a non-contact monitoring application.…”

Section: Introductionmentioning

confidence: 99%

Physical Layer Biometrics Using Antennas for Secure Wearable Wireless Communication

Saadat

Raurale

Conway

et al. 2019

IEEE Trans. Antennas Propagat.

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This paper combines a study of human body morphology with physical layer characteristics to introduce a novel biometric identity feature for security in wearable communication applications. The physical layer characteristics of close fitting wearable devices are shown to vary in a unique manner through electromagnetic interactions between the tissue morphology and the antenna. Experimental measurement results demonstrate the new biometric concept using return loss characteristics to identify individuals on multiple body parts. An optimised directional coupler design is implemented with the antenna to optimize the characteristic feature detection range for human identification. Experiments conducted on human subjects using a prototype standalone test-bed and sensing circuitry at 2.45 GHz, shows that, classification accuracies of over 98% are achieved for stationary subjects and 93% for mobile subjects. The new physical layer biometric, has the potential to be used for authentication and authorization by using return loss as an indicator for secure user applications, using circuitry already implemented in wireless wearable communication systems. Index Terms-biometric, antenna characteristics, return loss, directional coupler, wearable antenna, physical layer security. John McAllister (S02-M2004-SM12) received the Ph.D. degree in Electronic Engineering from Queens University Belfast, U.K., in 2004. He is currently a member of academic staff in the Institute of Electronics, Communications and Information Technology (ECIT) at the same university and an Associate Researcher at LInstituit National des Sciences Appliques (INSA) Rennes, France. His research interests are in the design and implementation of embedded data, signal and image processing applications. He is Vice-Chair of the IEEE

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Human Activity Classification Based on Dynamic Time Warping of an On-Body Creeping Wave Signal

Cited by 32 publications

References 11 publications

Analysis of Static Narrowband On-Body Channel Polarization Distribution at 2.4 GHz

Analysis of Static Narrowband On-Body Channel Polarization Distribution at 2.4 GHz

Wearable Antennas for Cross-Body Communication and Human Activity Recognition

Physical Layer Biometrics Using Antennas for Secure Wearable Wireless Communication

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