Remote photonic detection of human senses using secondary speckle patterns

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano‐vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self‐interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns.

Wi-CAS: A Contactless Method for Continuous Indoor Human Activity Sensing Using Wi-Fi Devices

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

Exploring LoRa for Sensing

Wireless sensing received a great amount of attention in recent years and various wireless technologies have been exploited for sensing, including WiFi [1], RFID [2], ultrasound [3], 60 GHz mmWave [4] and visible light [5]. The key advantage of wireless sensing over traditional sensing is that the target does not need to be equipped with any sensor(s) and the wireless signal itself is being used for sensing. Exciting new applications have been enabled, such as passive localization [6] and contactless human activity sensing [7]. While promising in many aspects, one key limitation of current wireless sensing techniques is the very small sensing range. This is because while both direct path and reflection path signals are used for communication, only the weak target-reflection signals can be used for sensing. Take Wi-Fi as an example: the communication range can reach 20 to 50 meters indoors but its sensing range is merely 4 to 8 meters. This small range further limits the through-wall sensing capability of Wi-Fi. On the other hand, many applications do require long-range and through-wall sensing capability. In a fire rescue scenario, the sensing device cannot be placed close to the building, and the long-range through-wall sensing capabilities are critical for detecting people deep inside the building. Table I summarizes the sensing range of existing wireless technologies. We can see that long-range through-wall sensing is still missing with wireless sensing.

Remote Photonic Detection of Human Senses Using Secondary Speckle Patterns

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, current methods of brain activity sensing require expensive equipment and physical contact with the subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano‐vibrations generated due to the transient blood flow to specific regions of the brain. We have found that combination of defocused, self‐interference random speckle patterns with a spatiotemporal analysis using Deep Neural Network (DNN) allows associating between the activated sense and the seemingly random speckle patterns.