【AIOT】智能感知--人

From: https://liudongdong1.github.io/

1. 人體存在感知

• 目標：檢測環境中的所有人體，標記出每個人體的坐標位置；不限人體數量，適應中低空斜拍、人體輕度遮擋、截斷等場景

.1. WAYV AIR

• WAYV AIR 智能人體存在感知雷達目前已成功應用於多個智能衛生間項目中，實現廁位的占位及人流量統計

  • 檢測准確率高，不管是靜止、微運動還是運動人員都可以實現准確檢測；

  • 無隱私、敏感問題，無鏡頭設計；

  • 超低功耗，輻射量僅為藍牙十分之一，對人體安全無害；

  • 檢測距離遠，適配各種安裝高度；

  • 可自由設定檢測范圍，適用於不同大小和形狀的空間區域；

  • 不受環境障礙物影響，如煙霧，污垢遮擋、低光照，熱源等，不需要任何維護；

  • 美觀、可隱藏在木材或塑料天花板等非金屬材料后。

Shuai X, Shen Y, Tang Y, et al. milliEye: A Lightweight mmWave Radar and Camera Fusion System for Robust Object Detection[C]//Proceedings of the International Conference on Internet-of-Things Design and Implementation. 2021: 145-157. [pdf]

Bhatia J, Dayal A, Jha A, et al. Object Classification Technique for mmWave FMCW Radars using Range-FFT Features[C]//2021 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2021: 111-115.

Lu, Chris Xiaoxuan, et al. "See through smoke: robust indoor mapping with low-cost mmWave radar." Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services. 2020. [pdf]

Devoti, Francesco, et al. "PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection." IEEE INFOCOM 2020-IEEE Conference on Computer Communications. IEEE, 2020. [pdf]

Gu T, Fang Z, Yang Z, et al. Mmsense: Multi-person detection and identification via mmwave sensing[C]//Proceedings of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems. 2019: 45-50. [pdf]

J. Yan, G. Zhang, H. Hong, H. Chu, C. Li, and X. Zhu, “Phase-basedhuman target 2-D identification with a mobile FMCW radar platform,”IEEE Trans. Microw. Theory Techn., vol. 67, no. 12, pp. 5348–5359,Dec. 2019. [pdf]

M. Zhaoet al., “Through-wall human mesh recovery using radio signals,” inProc. IEEE Int. Conf. Comput. Vis., Oct. 2019,pp. 10112–10121. [pdf]

Zhang Y, Zhang J, Chu X, et al. Effects of Wall Reflection on the Per-Antenna Power Distribution of ZF-Precoded ULA for Indoor mmWave MU-MIMO Transmissions[J]. IEEE Communications Letters, 2020. [pdf]

J. Yan et al., "The Development of Vital-SAR-Imaging with an FMCW Radar System," 2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2019, pp. 1-4, doi: 10.1109/IMBIOC.2019.8777881. [pdf]

Hicheri R, Pätzold M, Youssef N. Estimation of the velocity of a walking person in indoor environments from mmWave signals[C]//2018 IEEE Globecom Workshops (GC Wkshps). IEEE, 2018: 1-7. [pdf]

Huang X, Cheena H, Thomas A, et al. Indoor Detection and Tracking of People Using mmWave Sensor[J]. Journal of Sensors, 2021, 2021. [pdf]

2. 人員計數

• 靜態人數統計：中遠距離俯拍，以頭部為識別目標統計圖片中的瞬時人數；無人數上限，廣泛適用於機場、車站、商場、展會、景區等人群密集場所
• 動態人流量統計：面向門店、通道等出入口場景，以頭肩為識別目標，進行人體檢測和追蹤，根據目標軌跡判斷進出區域方向，實現動態人數統計，返回區域進出人數

• 安防監控：實時監測機場、車站、展會、展館、景區、學校、體育場等公共場所的人流量，及時導流、限流，預警核心區域人群過於密集等安全隱患
• 駕駛檢測：針對客運車輛，實時監控上下車和車內乘客數量，分析站點客流量、車內超載情況，為線路規划、站台設計提供精准參考依據

. Yavari, X. Gao, and O. Boric-Lubecke, “Subject count estimation by using Doppler radar occupancy sensor,” inProc. Annu. Int. Conf.IEEE Eng. Med. Biol. Soc., Oct. 2018, pp. 4428–4431

<<<<<<< HEAD

Qi, Delong, et al. "YOLO5Face: Why Reinventing a Face Detector." arXiv preprint arXiv:2105.12931 (2021). [pdf] [code]

.1. NanoDet

.2. Ultra-Light-Fast-Generic-Face-Detector-1MB

3. 人像分割

• 將人體輪廓與圖像背景進行分離，返回分割后的二值圖、灰度圖、透明背景人像前景圖，多人體、復雜背景、遮擋、背面、側面等各類人體姿態
• 證件照片：針對自拍類單人圖片，基於人臉檢測、人體關鍵點先裁剪出符合證件照場景的人像圖片，對裁剪后的圖片進行發絲級精細化分割，一鍵制作證件照
• 視頻人像：可對實時視頻流中的人像背景進行分割，支持背景圖自定義及3D背景圖定制，適用於視頻會議、短視頻及直播場景

• 人像摳圖美化：將原始圖片中的人像分離出來，選擇新的背景圖像進行替換、合成；同時可以對背景進行虛化處理，突出人像，實現大光圈人像拍照效果
• 人體特效：識別用戶的人體輪廓，為人像實時增加各種設定的背景特效、貼紙道具，提供更加豐富的娛樂體驗
• 影視后期處理：識別影視作品中的人像區域，進行一鍵摳像、背景替換、人像虛化等后期處理

Salehi B, Belgiovine M, Sanchez S G, et al. Machine Learning on Camera Images for Fast mmWave Beamforming[C]//2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2020: 338-346. [pdf]

P. Nallabolu and C. Li, "A Novel Radar Imaging Method Based on Random Illuminations Using FMCW Radar," 2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT), 2020, pp. 27-29, doi: 10.1109/WiSNeT46826.2020.9037583. [pdf]

4. 關鍵點檢測

• 體育鍛煉：根據人體關鍵點信息，分析人體姿態、運動軌跡、動作角度等，輔助運動員進行體育訓練，分析健身鍛煉效果，提升教學效率；
• 娛樂互動： 視頻直播平台、線下互動屏幕等場景，可基於人體檢測和關鍵點分析，增加身體道具、體感游戲等互動形式，豐富娛樂體驗
• 安防監控：實時監測定位人體，判斷特殊時段、核心區域是否有人員入侵；基於人體關鍵點信息，進行二次開發，識別特定的異常行為，及時預警管控

S. Li, X. Li, Q. Lv, G. Tian, and D. Zhang, “WiFit: Ubiquitous body weight exercise monitoring with commodity wi-fi devices,” inProc. IEEE SmartWorld, Ubiquitous Intell. Comput., Adv. TrustedComput., Scalable Comput. Commun., Cloud Big Data Comput., Inter-net People Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI, Dec. 2018, pp. 530–537

.1. 人體

• 通過攝像頭捕捉追蹤人體在一段時間內的姿勢變化，檢測人體姿態是否達到預期的角度、幅度、速度，輔助健身鍛煉、體育訓練、康復訓練等應用

.2. 人臉

.3. 手部

.1. 關鍵點檢測

精准定位手部的21個主要骨節點，包括指尖、各節指骨連接處等，返回每個骨節點的坐標信息

• AR特效: 短視頻、直播等娛樂交互場景中，基於指尖點檢測和指骨關鍵點檢測，可實現手部特效、空間作畫等多種創意玩法，豐富交互體驗
• 自定義手勢識別: 根據手部骨節坐標信息，可靈活定義業務場景中需要用到的手勢，例如面向智能家電、可穿戴等硬件設備的操控類手勢，面向內容審核場景的特殊手勢

.2. 手勢識別

識別24種常見手勢，支持單手手勢和雙手手勢，包括拳頭、OK、比心、作揖、作別、祈禱、我愛你、點贊、Diss、Rock、豎中指、數字等

• 智能家居：智能家電、家用機器人、可穿戴、兒童教具等硬件設備，通過用戶的手勢控制對應的功能，人機交互方式更加智能化、自然化
• 視頻直播：視頻直播或者拍照過程中，結合用戶的手勢（如點贊、比心），實時增加相應的貼紙或特效，豐富交互體驗
• 智能駕駛：將手勢識別應用到駕駛輔助系統中，使用手勢來控制車內的各種功能、參數，一定程度上解放雙眼，將更多的注意力放在道路上，提升駕車安全性

Ren Y, Lu J, Beletchi A, et al. Hand gesture recognition using 802.11 ad mmWave sensor in the mobile device[C]//2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). IEEE, 2021: 1-6.

Wang S, Song J, Lien J, et al. Interacting with soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum[C]//Proceedings of the 29th Annual Symposium on User Interface Software and Technology. 2016: 851-860. [pdf]

.3. 指尖

.1. 指尖檢測

檢測圖像中的手部位置，精准定位食指指尖，返回手部、食指指尖的坐標信息，尤其適用於兒童學習機點讀場景

.2. 定位追蹤

• 鍵盤輸入
• 智能點讀

4. 屬性識別

識別人體的20余類通用屬性，包含性別年齡、服飾類別、服飾顏色、戴帽子（可區分安全帽/普通帽）、戴口罩、背包、手提物、抽煙、使用手機，戴手套等

• Scenarios

• 安防監控：識別人體的性別年齡、衣着外觀等特征，輔助定位追蹤特定人員；監測預警各類危險、違規行為（如公共場所跑跳、抽煙、未佩戴口罩），減少安全隱患
• 人群屬性，廣告投放： 樓宇、戶外等廣告屏智能化升級，采集人體信息，分析人群屬性，定向投放廣告物料，提升用戶體驗和商業效率

5. 粗粒度行為感知

.1. 駕駛行為

識別圖像中的所有人體，將目標最大的人體作為駕駛員，返回坐標位置，同時返回總人數（含駕駛員和乘客）；支持夜間紅外場景

• 營運車輛駕駛監測： 針對出租車、客車、公交車、貨車等各類營運車輛，實時監控車內情況，識別駕駛員抽煙、使用手機、未系安全帶、未佩戴口罩、疲勞、視線偏離等違規行為，及時預警，降低事故發生率，保障人身財產安全
• 社交內容分析審核: 汽車類論壇、社區平台，對配圖庫以及用戶上傳的UGC圖片進行分析識別，自動過濾出涉及危險駕駛行為的不良圖片，有效減少人力成本並降低業務違規風險

C. Dinget al., “Inattentive driving behavior detection based onportable FMCW radar,”IEEE Trans. Microw. Theory Techn., vol. 67,no. 10, pp. 4031–4041, Oct. 2019

.2. 危險行為

• 單人場景行為識別： 針對單人監控視頻片段，可識別4類常見危險行為，包括：情緒性指人、摔倒、激烈抱怨、砸東西 、高空拋物 觸摸去靜電裝置（某些工廠如燃氣場進入前）
• 雙人場景行為識別： 針對雙人監控視頻片段，識別是否有危險行為，如出拳、拉扯、推搡、激烈摟抱、踢踹、砸按等

• 安防監控: 社區、園區、廠房、門店、樓道、電梯等重點區域，檢測人員摔倒、砸按、打斗、肢體沖突等行為，及時預警、管控，避免安全事故
• 智能看護: 家庭、醫院、養老院、幼兒園等場所，實時監控分析人員行為，及時發現老人摔倒、病患摔倒、幼兒摔倒等危險情況，保障人身安全

Y. Tang, Z. Peng, L. Ran and C. Li, "iPrevent: A novel wearable radio frequency range detector for fall prevention," 2016 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), 2016, pp. 1-3, doi: 10.1109/RFIT.2016.7578162. [pdf]

.3. 多人活動

D. V. Q. Rodrigues and C. Li, "Noncontact Exercise Monitoring in Multi-Person Scenario With Frequency-Modulated Continuous-Wave Radar," 2020 IEEE MTT-S International Microwave Biomedical Conference (IMBioC), 2020, pp. 1-3, doi: 10.1109/IMBIoC47321.2020.9385031. [pdf]

6. 細粒度行為感知

.1. 眼部

• 眨眼轉頭檢測

E. Cardillo, G. Sapienza, C. Li and A. Caddemi, "Head Motion and Eyes Blinking Detection: a mm-Wave Radar for Assisting People with Neurodegenerative Disorders," 2020 50th European Microwave Conference (EuMC), 2021, pp. 925-928, doi: 10.23919/EuMC48046.2021.9338116.

• aid for people with neurodegenerative disorder.
• silicon Radar TRX_120_002 on-chip frontend

• 瞳孔轉動檢測

.2. 喉嚨

• 聲音識別
• 聲紋識別

Li, Huining, et al. "VocalPrint: exploring a resilient and secure voice authentication via mmWave biometric interrogation." Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 2020. [pdf]

Xu, Chenhan, et al. "Waveear: Exploring a mmwave-based noise-resistant speech sensing for voice-user interface." Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. 2019. [pdf]

.3. vital sign

L. Zhang, C. Ding, X. Zhou, H. Hong, C. Li and X. Zhu, "Body movement cancellation using adaptive filtering technology for radar-based vital sign monitoring," 2020 IEEE Radar Conference (RadarConf20), 2020, pp. 1-5, doi: 10.1109/RadarConf2043947.2020.9266671.

.1.呼吸

H. Zhao et al., "A Noncontact Breathing Disorder Recognition System Using 2.4-GHz Digital-IF Doppler Radar," in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 1, pp. 208-217, Jan. 2019, doi: 10.1109/JBHI.2018.2817258. [pdf]

. M. M. Islam, A. Sylvester, G. Orpilla, and V. M. Lubecke, “Respiratory feature extraction for radar-based continuous identity authentication,” inProc. IEEE Radio Wireless Symp., Jan. 2020, pp. 119–122.

X. Ma, Y. Wang, X. You, J. Lin, and L. Li, “Respiratory pattern recognition of an adult bullfrog using a 100-GHz CW Doppler radar transceiver,” inProc. IEEE MTT-S Int. Microw. Biomed. Conf., 2019,pp. 1–3.

Q. Lvet al., “Doppler vital signs detection in the presence of large-scale random body movements,”IEEE Trans. Microw. Theory Techn.,vol. 66, no. 9, pp. 4261–4270, Sep. 2018.

J. Tu, T. Hwang, and J. Lin, “Respiration rate measurement under 1-D body motion using single continuous-wave Doppler radar vital sign detection system,”IEEE Trans. Microw. Theory Techn., vol. 64, no. 6,pp. 1937–1946, Jun. 2016.

S. M. M. Islam, E. Yavari, A. Rahman, V. M. Lubecke, and O.Boric-Lubecke, “Multiple subject respiratory pattern recognition and estimation of direction of arrival using phase-comparison mono-pulse radar,” inProc. IEEE Radio Wireless Symp., 2019, pp. 1–4.

Cardillo, Emanuele, Changzhi Li, and Alina Caddemi. "Vital Sign Detection and Radar Self-Motion Cancellation Through Clutter Identification." IEEE Transactions on Microwave Theory and Techniques 69.3 (2021): 1932-1942. [pdf] [todo]

• remove a novel technique to remove the radar self-motion effects(RSMs) for accurate detection of human vital signs;
• extracts the RSM from the signals reflected by stationary clutters, and propose two procedures to automatic identification for detecting both small and large radar motions.
  • the autocorrelation applied to the received phase histories for each measured range bin based on the inherent periodicity.
  • the autocorrelation on the cross correlation between the measured range-Doppler pro-files.

.2. Blood pressure

. Hui, T. B. Conroy, and E. C. Kan, “Multi-point near-field RF sensing of blood pressures and heartbeat dynamics,”IEEE Access,vol. 8, pp. 89935–89945, 2020.

.3. Cardiac motion

H. Zhao, X. Gu, H. Hong, Y. Li, X. Zhu, and C. Li, “Non-contact beat-to-beat blood pressure measurement using continuous waveDoppler radar,” inIEEE MTT-S Int. Microw. Symp. Dig., Jun. 2018,pp. 1413–1415.

. Saluja, J. Casanova, and J. Lin, “A supervised machine learning al-gorithm for heart-rate detection using Doppler motion-sensing radar,”IEEE J. Electromagn. RF Microw. Med. Biol., vol. 4, no. 1, pp. 45–51,Mar. 2020.VOLUME 1, NO. 1, JANUARY 202177

F. Lin, C. Song, Y. Zhuang, W. Xu, C. Li, and K. Ren, “Cardiacscan: A non-contact and continuous heart-based user authentication system,” inProc. Annu. Int. Conf. Mobile Comput. Netw., Oct. 2017,pp. 315–328.

.4. 步態

. S. Koo, L. Ren, Y. Wang, and A. E. Fathy, “UWB micro doppler radar for human gait analysis, tracking more than one person, and vital sign detection of moving persons,” inIEEE MTT-S Int. Microw. Symp.Dig., 2013, pp. 1–4.

Y. Tang, L. Ran and C. Li, "A feasibility study on human gait monitoring using a wearable K-band radar," 2016 46th European Microwave Conference (EuMC), 2016, pp. 918-921, doi: 10.1109/EuMC.2016.7824494. [pdf]

.5. 書寫

. Lienet al., “Soli: Ubiquitous gesture sensing with millimeter waveradar,”ACM Trans. Graph., vol. 35, no. 10, pp. 1–19, 2016

.6. 睡眠

H. Hong et al., "Microwave Sensing and Sleep: Non contact Sleep-Monitoring Technology With Microwave Biomedical Radar," in IEEE Microwave Magazine, vol. 20, no. 8, pp. 18-29, Aug. 2019, doi: 10.1109/MMM.2019.2915469.

L. Zhang, J. Xiong, H. Zhao, H. Hong, X. Zhu and C. Li, "Sleep stages classification by CW Doppler radar using bagged trees algorithm," 2017 IEEE Radar Conference (RadarConf), 2017, pp. 0788-0791, doi: 10.1109/RADAR.2017.7944310. [pdf]

H. Honget al., “Microwave sensing and sleep,”IEEE Microw. Mag.,vol. 20, no. 8, pp. 18–29, Aug. 2019.

. Baboli, A. Singh, B. Soll, O. Boric-Lubecke, and V. M. Lubecke,“Good night: Sleep monitoring using a physiological radar monitoring system integrated with a polysomnography system,”IEEE Microw.Mag., vol. 16, no. 6, pp. 34–41, Jul. 2015.

. Baboli, A. Singh, B. Soll, O. Boric-Lubecke, and V. M. Lubecke,“Wireless sleep apnea detection using continuous wave quadrature Doppler radar,”IEEE Sensors J., vol. 20, no. 1, pp. 538–545,Jan. 2020

H. Hong, L. Zhang, C. Gu, Y. Li, G. Zhou, and X. Zhu, “Noncontact sleep stage estimation using a CW Doppler radar,”IEEE J. Emerg. Sel.Topics Circuits Syst., vol. 8, no. 2, pp. 260–270, Jun. 2018.

F. Linet al., “SleepSense: A noncontact and cost-effective sleep monitoring system,”IEEE Trans. Biomed. Circuits Syst., vol. 11, no. 1,pp. 189–202, Feb. 2017.

.7. 摔倒

Y. Tang, Z. Peng, L. Ran and C. Li, "iPrevent: A novel wearable radio frequency range detector for fall prevention," 2016 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), 2016, pp. 1-3, doi: 10.1109/RFIT.2016.7578162. [pdf]

Jin F, Sengupta A, Cao S. mmFall: Fall Detection using 4D MmWave Radar and Variational Recurrent Autoencoder[J]. arXiv preprint arXiv:2003.02386, 2020. [pdf]

Sun Y, Hang R, Li Z, et al. Privacy-Preserving Fall Detection with Deep Learning on mmWave Radar Signal[C]//2019 IEEE Visual Communications and Image Processing (VCIP). IEEE, 2019: 1-4.

Wang K, Zhan G, Chen W. A New Approach for IoT-based Fall Detection System using Commodity mmWave Sensors[C]//Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City. 2019: 197-201.

7. 追蹤軌跡

Palacios, Joan, et al. "LEAP: Location estimation and predictive handover with consumer-grade mmWave devices." IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019. [pdf]

J. Wang, D. Nolte, K. Tanja, J. Muñoz-Ferreras, R. Gómez-García and C. Li, "Trade-off on Detection Range and Channel Usage for Moving Target Tracking using FSK Radar," 2020 IEEE Topical Conference on Wireless Sensors and Sensor Networks (WiSNeT), 2020, pp. 38-41, doi: 10.1109/WiSNeT46826.2020.9037618. [pdf]

Zeng, Yunze, et al. "Human tracking and activity monitoring using 60 GHz mmWave." 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2016. [pdf]

8. HotTopic

.1. MOTION SEPARATION&CLASSIFICATION IN DYNAMIC ENVIRONMENT

• random motions of both human subject&radar platform

. Mercuri, I. R. Lorato, Y. H. Liu, F. Wieringa, C. Van Hoof, and T.Torfs, “Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor,”Nature Electron., vol. 2, no. 6,pp. 252–262, Jun. 2019

Z. Guet al., “Blind separation of Doppler human gesture signals based on continuous-wave radar sensors,”IEEE Trans. Instrum. Meas.,vol. 68, no. 7, pp. 2659–2661, Jul. 2019.

S. M. M. Islam, E. Yavari, A. Rahman, V. M. Lubecke, and O. Boric-Lubecke, “Separation of respiratory signatures for multiple subjectsusing independent component analysis with the JADE algorithm,”inProc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., Oct. 2018,pp. 1234–1237.

A high-dynamic-range radar can be aided with algorithmssuch as matched filters to retrieve signals concealed by bodymotion noise [107].

In [108], the direction of body motion is extracted alongwith the new position of the respiration peaks in the frequencyspectrum and respiration rate was calculated

a low-IFSIMO system employed a two-step blind motion separation tosequentially tackles signal separation and nonlinear demodu-lation [109

[111], the vital signs signal fidelitywas improved using RSS indicator and Direction of Arrival(DOA) to compensate for the platform motion via a closedloop control system that modulates the UAV electronic speedcontroller. In addition, an optical tracking system [112] or anRF tag [113] can be used to achieve adaptive platform motionnoise cancellation.

a precise phase-based humantarget 2-D SAR imaging and recognition system based onvital sign tracking was demonstrated [114]. It first relies onFMCW phase detection to extract the vital signs of multiplehuman targets, then applies a SAR algorithm to obtain the 2-Dimaging of the scene and labels human targets.

.2. CROWD DETECTION AND SIGNAL-OF-INTEREST EXTRACTION

. An SNR-basedintelligent decision algorithm integrated two different ap-proaches to isolate respiratory signatures of two subjectswithin the radar beamwidth [115]: Independent ComponentAnalysis with the JADE algorithm (ICA-JADE) [116] andDOA [117],

.3. interaction of microwave technology &artificial intelligence

. Li and J. Lin, “Wavelet-transform-based data-length-variation technique for fast heart rate detection using 5.8-GHz CW Doppler radar,”IEEE Trans. Microw. Theory Techn., vol. 66, no. 1, pp. 568–576,Jan. 2018

. Tu and J. Lin, “Fast acquisition of heart rate in non-contact vital sign radar measurement using time-window-variation technique,”IEEE Trans. Instrum. Meas., vol. 65, no. 1, pp. 112–122, Jan. 2016.

C. Dinget al., “Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar,”IEEE Trans.Geosci. Remote Sens., vol. 57, no. 9, pp. 6821–6831, Sep. 2019.