Heart Rate Sensing with a Robot Mounted mmWave Radar

Zhao, Peng; Lu, Chris Xiaoxuan; Wang, Qingqing; Chen, Changhao; Xie, Linhai; Wang, Mengyu; Trigoni, Niki; Markham, Andrew

doi:10.1109/icra40945.2020.9197437

Cited by 53 publications

(36 citation statements)

References 33 publications

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“…[193] TI AWR6843 [188], [190] TI IWR1443 [210], [211] subjects and objects in the radar maps (see, e.g., density-based spatial clustering of applications with noise (DBSCAN) in the following sections) and, on the final tracking performance of any signal processing pipeline.…”

Section: Frequency-modulated Continuous Wave Radarmentioning

confidence: 99%

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

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The commercial availability of low-cost millimeterwave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifthgeneration (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices.This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used.We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standardcompliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and do not yet generalize to any indoor environment, limiting their applicability. Manuscript received xxxx xx, xxxx . . .

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Section: Frequency-modulated Continuous Wave Radarmentioning

confidence: 99%

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

Shastri

Valecha

Bashirov

et al. 2022

IEEE Commun. Surv. Tutorials

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show abstract

“…Using a neural network work-based classifier, they got 95.19% accuracy of bulk motion of the body and the micromotions from arms and legs. Zhao et al [66] proposed a system named mBeats, where a robot mounted with mmWave radar system is used to provide periodic heart rate measurements under different user poses. A falldetection system based on mmWave radar was also presented by Sun et al [67] with the support of a recurrent neural network with long short-term memory units.…”

Section: Wave Sensingmentioning

confidence: 99%

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Bian

Liu

Zhou

et al. 2022

Sensors

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Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

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“…Various device-free human sensing modalities have been explored, such as vision [4,5], acoustic [6,7], vibration [3,[8][9][10][11], light [12,13], WiFi [14,15], mmWave [16][17][18][19], RFID [20,21], etc. These sensing modalities acquire human information without requiring the target human subject to wear or to carry any devices.…”

Section: Device-free Human Sensingmentioning

confidence: 99%

Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems

Pan

Nguyen

2020

Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing

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This is a position paper that discusses the challenges of emerging new sensing modalities for both device-free and wearable sensing systems, as well as opportunities lying in the combination of them across multiple information scales. With the development of the Internet of Things (IoT), many devices with sensing-ability have entered people's life. These systems mainly fall into two categories: wearables and infrastructure sensing (device-free). In this paper, we rst briey summarize the state-of-the-art sensing modalities of these two categories, then we discuss the challenges faced by them. We envision a future of IoT human sensing systems that achieves seamless sensing across multiple scales through collaborative information inference by both categories of modalities. Finally, we discuss the opportunities to expand the boundaries of sensing modalities that lie in their collaborative adaptation.

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Heart Rate Sensing with a Robot Mounted mmWave Radar

Cited by 53 publications

References 33 publications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

Opportunities in the Cross-Scale Collaborative Human Sensing of 'Developing' Device-Free and Wearable Systems

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