Draft:Wireless intelligent sensing

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. 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, converting raw data into practical applications in healthcare, smart home, automotive, and other industries.

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

The EM wave layer includes a radar sensor that transmits and receives EM-wave energy, using different wireless technologies such as Bluetooth, Wi-Fi, UWB, 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. 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. 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. 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.

Fall and Presence Detection
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. Intelligence in wireless sensing in-home or in assisted living includes:


 * Real-time fall detection
 * The ability to distinguish between an object falling from an individual falling

Remote Patient Monitoring
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. Leveraging intelligence in wireless sensing, raw vitals data may be turned into information including:
 * Sleep quality or sleep apnea
 * Acute and long-term cardiac or respiratory disease analysis

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.

Autonomous Driving and Smart Vehicles
Wireless intelligent sensing in the automotive industry can be used inside vehicles to improve passenger and driver safety and comfort functions. 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)