Draft:Wireless intelligent sensing

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  • Comment: From WP:MEDRS, and semi-applicable here: Cite review articles, don't write them. This article appears to consist of primary sources, which do not establish notability.
  • Comment: A press release and researchgate? both of which are not reliable sources? One is obviously primary, the other is deprecated. Please learn your trade if you expect to receive a reward for editing here. 🇺🇦 FiddleTimtrent FaddleTalk to me 🇺🇦 20:37, 6 December 2023 (UTC)
  • Comment: I have noted from your user page that you are paid to edit Wikipedia. You are expected to be able to have the article ready for the second reviewer to accept. This is your challenge. 🇺🇦 FiddleTimtrent FaddleTalk to me 🇺🇦 10:04, 29 November 2023 (UTC)
  • Comment: The lack of sources in several sections and areas of sections suggests that notability has not been established in this draft 🇺🇦 FiddleTimtrent FaddleTalk to me 🇺🇦 09:59, 29 November 2023 (UTC)

Wireless Intelligent Sensing is a form of wireless sensing that uses a systematic framework to introduce digital intelligence into contactless electromagnetic-wave sensor hardware design.[1] The introduction of deep learning into current wireless sensing applications aids in minimizing a sensor's signal-to-noise ratio and improves digital signal processing,[2] converting raw data into practical applications in healthcare, smart home, automotive, and other industries.[3]

Design[edit]

The framework of wireless intelligent sensing currently consists of four layers: electromagnetic (EM) wave, signal processing, data analytics, and smart applications.[1]

The EM wave layer includes a radar sensor that transmits and receives EM-wave energy, using different wireless technologies such as Bluetooth,[4] Wi-Fi,[5] UWB,[6] or mmWave for data collection. The design of this EM wave layer is important for accurate data collection, each with their own benefits and challenges, tailored to the application.

The signal processing layer consists of artificial intelligence (AI) algorithms and digital signal processing tailored to specific applications of the technology and the type of wireless technology used.[1] Methods of signal processing may include signal transformation and digital filtering.

The data analytics layer consists of analytic algorithms and tools used to analyze and mine data from the signal processing layer.[1] These algorithms can also vary based on the use-case and type of wireless technology employed in the EM wave layer. Analytics may be computed on the edge, or cloud levels, depending on the application.

The top layer of intelligent sensing uses smart applications to dictate how information from the previous layers can meet the requirements of the application.[1] It utilizes specific machine learning algorithms such as k-means clustering for uses requiring real-time reporting, or deep learning algorithms for applications in non-real-time applications.

Applications[edit]

Fall and Presence Detection[edit]

Wireless intelligent sensing can be used at home or in assisted living homes, for example, to assist individuals aging in place, seniors, individuals with intellectual disabilities, and caregivers.[7] Intelligence in wireless sensing in-home or in assisted living includes:

  • Real-time fall detection[8]
  • The ability to distinguish between an object falling from an individual falling[1]

Remote Patient Monitoring[edit]

Wireless sensing of vitals can monitor respiration rate, heart rate, heart rate variability (HRV)), and more, in the remote care, telehealth, and aging-in-place settings.[9] Leveraging intelligence in wireless sensing, raw vitals data may be turned into information including:

  • Sleep quality or sleep apnea[10]
  • Acute and long-term cardiac or respiratory disease analysis[11]

Wireless intelligent sensing has impact in accessible real-time and in long-term monitoring, increasingly recognized in advancing the management of chronic diseases at earlier stages.[12]

Autonomous Driving and Smart Vehicles[edit]

Wireless intelligent sensing in the automotive industry can be used inside vehicles to improve passenger and driver safety and comfort functions.[13] One example uses mmWave sensing for child presence detection (CPD) for the Euro NCAP safety program. Intelligence in this application means differentiating children from adults, as well as child seat positioning within the vehicle. Other uses of intelligence in the automotive industry includes:

  • Optimized airbag deployment
  • Occupancy detection
  • Vital sign monitoring (e.g. breathing, heart rate, HRV)
  • Driver monitoring of emotional states (e.g. motion sickness, drowsiness, intoxication)[14]

References[edit]

  1. ^ a b c d e f Qi, Alex; Ma, Muxin; Luo, Yunlong; Fernandes, Guillaume; Shi, Ge; Fan, Jun; Qi, Yihong; Ma, Jianhua (2023). "WISe: Wireless Intelligent Sensing for Human-Centric Applications". IEEE Wireless Communications. 30 (2): 106–113. doi:10.1109/MWC.012.2100656. S2CID 248610920.
  2. ^ "Signal to noise ratio: Filtering the Noise to Uncover Actionable Insights".
  3. ^ Wang, Jie; Gao, Qinhua; Ma, Xiaorui; Zhao, Yunong; Fang, Yuguang (2020). "Learning to Sense: Deep Learning for Wireless Sensing with Less Training Efforts". IEEE Wireless Communications. 27 (3): 156–162. doi:10.1109/MWC.001.1900409. S2CID 219003117.
  4. ^ Bai, Lu; Ciravegna, Fabio; Bond, Raymond; Mulvenna, Maurice (2020). "A Low Cost Indoor Positioning System Using Bluetooth Low Energy". IEEE Access. 8: 136858–136871. Bibcode:2020IEEEA...8m6858B. doi:10.1109/ACCESS.2020.3012342. S2CID 261898471.
  5. ^ Halperin, Daniel; Hu, Wenjun; Sheth, Anmol; Wetherall, David (January 22, 2011). "Tool release: gathering 802.11n traces with channel state information". ACM SIGCOMM Computer Communication Review. 41 (1): 53. doi:10.1145/1925861.1925870. S2CID 13561174 – via ACM Digital Library.
  6. ^ Chia, M.Y.W.; Leong, S.W.; Sim, C.K.; Chan, K.M. (2005). "Through-Wall UWB Radar Operating within FCC's Mask for Sensing Heart Beat and Breathing Rate". European Radar Conference, 2005. EURAD 2005. p. 283. doi:10.1109/EURAD.2005.1605615. ISBN 2-9600551-3-6. S2CID 20897951.
  7. ^ Cook, Diane (September 25, 2020). "Sensors in Support of Aging-in-Place: The Good, the Bad, and the Opportunities". Mobile Technology for Adaptive Aging: Proceedings of a Workshop. National Academies Press (US) – via www.ncbi.nlm.nih.gov.
  8. ^ Hang, Tianmeng; Zheng, Yue; Qian, Kun; Wu, Chenshu; Yang, Zheng; Zhou, Xiancun; Liu, Yunhao; Chen, Guilin (October 2019). "WiSH: WiFi-based real-time human detection". Tsinghua Science and Technology. 24 (5): 615–629. doi:10.26599/TST.2018.9010091.
  9. ^ Yilmaz, Tuba; Foster, Robert; Hao, Yang (December 6, 2010). "Detecting vital signs with wearable wireless sensors". Sensors (Basel, Switzerland). 10 (12): 10837–10862. Bibcode:2010Senso..1010837Y. doi:10.3390/s101210837. PMC 3231103. PMID 22163501.
  10. ^ Koley, Bijoylaxmi; Mandal, Saradindu (2022). "Wireless Sensor and Smartphone-based System for Detection of Sleep Apnea". 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON). pp. 374–378. doi:10.1109/CATCON56237.2022.10077634. ISBN 978-1-6654-7380-4. S2CID 257797700.
  11. ^ Murali, Srinivasan; Brugger, Nicolas; Rincon, Francisco; Mashru, Manoj; Cook, Stéphane; Goy, Jean-Jacques (December 6, 2020). "Cardiac Ambulatory Monitoring: New Wireless Device Validated Against Conventional Holter Monitoring in a Case Series". Frontiers in Cardiovascular Medicine. 7. doi:10.3389/fcvm.2020.587945. PMC 7733961. PMID 33330650.
  12. ^ https://healthworkforce.ucsf.edu/sites/healthworkforce.ucsf.edu/files/REPORT_FINAL_RemoteMonitoring.pdf
  13. ^ "Detecting vehicle occupancy with mmWave sensors - Automotive - Technical articles - TI E2E support forums". e2e.ti.com. April 30, 2018.
  14. ^ "Virtual Reality in Your Vehicle - IDTechEx Explores Advanced New Vehicle Technologies". www.prnewswire.com (Press release).
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