Millimeter-wave sensing

As the analog and digital circuits technologies have been developed, semiconductor manufacturers have integrated both digital and analog parts on a single chip. This integration not only reduces the size and the cost but also the power consumption, and makes it attractive for energy-harvesting and portable systems such as internet of things and smart homes.

One popular kind of radars is frequency modulated continuous wave (FMCW) which have their unique features:

  • Being a mm-wave radar: the high attenuation in mm-wave frequencies provides a high isolation between the co-located operating radars even if they are separated in a few meters. Indeed, tiny displacements in mm are comparable to the wavelength thus they can be detected. This high sensitivity is required to detect the chest wall movement, which is in mm order.
  • Discriminating range or localizing: because the radar can distinguish the reflections from different ranges, potentially it can be used for multi-subject vital signs detection. This feature is recognized as the main advantage of an FMCW radar. Indeed, high propagation attenuation reduces the possibility of having an echo signal, which is bounced off multiple reflectors. Most probably, the echo signal is reflected off a single object if the environment is not rich scattering. In that area, the received signal at particular range experienced a line of sight wireless channel. In contrast, CW radars suffer from multipath fading because they collect all reflections from all objects at all visible ranges in a one sinusoid signal.
  • Being robust against thermal noise: FM signals are more robust against noise in comparison to AM signals. Also, in FMCW radars the vital sign information is encoded in the received phase similar to FM signals. Thus, FMCW radar is less affected by the noise in comparison to impulse radars.

There are many various fields of study in the mm-wave radars our team has engaged in such as:

  • Wireless vital signs monitoring: detecting chest motions due to cardiorespiratory activities and estimating breathing rate and heart rates remotely can greatly help in protecting patients with severe incidence like sleep apnea (vital signs monitoring video).
  • Autonomous driving for advanced driving assistance system (ADAS): showing the blind-spot, lane-departure warning when the traffic is approaching from the sides, forward collision avoidance such as in a sudden stop of the front traffic, automatic emergency braking when reaching a high sharp bend, lane-keep assist like driving in a wavy highway etc.
  • Occupant detection: for controlling lighting and heating in a smart home and in office environments, a low-cost and low-power sensor with high accuracy can be a good candidate to save energy consumption. In addition, monitoring of people presence enhances safety and security in vehicles while maintains privacy of passengers without revealing their identities like cameras do (VOD video).
  • Gesture recognition:  is becoming a more prominent form of human-computer interaction and can be used in the automotive industry to provide a safe and intuitive control interface that will limit driver distraction.  Specific gesture features can be extracted and used to build a machine learning engine that can perform real-time gesture recognition.

 For more information about the radar, please refer to the Introduction to FMCW radar  .pdf file.

Remote Human Identification

This research involved the use of wireless waves for biometric identification of humans. It proved that sensors can differentiate between people based on their individual electromagnetic signature.

Remote Human Identification - 1

Remote Human Identification - 2

Wireless Wearable Free Fall Detector

The goal of this project is to detect a fall in a room filled with 5 or more people, while ensuring no reflections or shadows are detected. The project is currently in progress with the following achievements already made:

  • Successfully detected a fall in the presence of 2  people

Fall detection

  • Eliminated detection of reflections and shadows.

Reflection analysis

The next steps will be working on detecting 10 or more people accurately in a room, regardless of if they are stationary or moving.

Wireless Gait Monitor

This research involves monitoring the gait of a person through implementing a low-cost solution.

Gait graph

Gait graph

Gait setup