Phuc Nguyen

Continuous and fine-grained breathing volume monitoring from afar using wireless signals

In this work, we propose for the first time an autonomous system, called WiSpiro, that continuously monitors a persons breathing volume with high resolution during sleep from afar. WiSpiro relies on a phase-motion demodulation algorithm that reconstructs minute chest and abdominal movements by analyzing the subtle phase changes that the movements cause to the continuous wave signal sent by a 2.4 GHz directional radio. These movements are mapped to breathing volume, where the mapping relationship is obtained via a short training process. To cope with body movement, the system tracks the large-scale movements and posture changes of the person, and moves its transmitting antenna accordingly to a proper location in order to maintain its beam to specific areas on the frontal part of the persons body. It also incorporates interpolation mechanisms to account for possible inaccuracy of our posture detection technique and the minor movement of the persons body. We have built WiSpiro prototype, and demonstrated through a user study that it can accurately and continuously monitor users breathing volume with a median accuracy from 90% to 95.4% (or 0.0581 to 0.111 of error) to even in the presence of body movement. The monitoring granularity and accuracy are sufficiently high to be useful for diagnosis by clinical doctor.

Battery-Free Identification Token for Touch Sensing Devices

This paper proposes the design and implementation of low-- energy tokens for smart interaction with capacitive touch-- enabled devices by associating the tokens identity with its contact, or touch. The proposed tokens design features two key novel technical components: (1) a through--touch--sensor low--energy communication method for token identification and (2) a touch--sensor energy harvesting technique. The communication mechanism involves the token transmitting its identity (ID) directly through the touch--sensor by artificially modifying the effective capacitance between the touch-- sensor and token surfaces. This approach consumes significantly lower energy compared to traditional electrical signal modulation approaches. By enabling the token to harvest energy from touch--screen sensors or touch--surfaces the token is rendered battery--free. Through experimental evaluations using a prototype implementation, the proposed design is shown to achieve at least 95% identification accuracy. It is also shown to consume less energy than competitive techniques (NFC P2P and Bluetooth Low--Energy) for communicating a short ID sequence. The adoption of this technology among users is evaluated through a user study on 12 subjects.

Investigating Cost-effective RF-based Detection of Drones

Beyond their benign uses, civilian drones have increasingly been used in problematic ways that have stirred concern from the public and authorities. While many anti-drone systems have been proposed to take them down, such systems often rely on a fundamental assumption that the presence of the drone has already been detected and is known to the defender. However, there is a lack of an automated cost-effective drone detection system. In this paper, we investigate a drone detection system that is designed tao autonomously detect and characterize drones using radio frequency wireless signals. In particular, two technical approaches are proposed. The first approach is active tracking where the system sends a radio signal and then listens for its reflected component. The second approach is passive listening where it receives, extracts, and then analyzes observed wireless signal. We perform a set of preliminary experiments to explore the feasibility of the approaches using WARP and USRP software-defined platforms. Our preliminary results illustrate the feasibility of the proposed system and identify the challenges for future research.

Real-Time Tidal Volume Estimation Using Iso-surface Reconstruction

Breathing volume measurement has long been an important physiological indication widely used for the diagnosis and treatment of pulmonary diseases. However, most of existing breathing volume monitoring techniques require either physical contact with the patient or are prohibitively expensive. In this paper we present an automated and inexpensive non-contact, vision-based method for monitoring an individuals tidal volume, which is extracted from a three-dimensional (3D) chest surface reconstruction from a single depth camera. In particular, formulating the respiration monitoring process as a 3D space-time volumetric representation, we introduce a real-time surface reconstruction algorithm to generate omni-direction deformation states of a patients chest while breathing, which reflects the change in tidal volume over time. These deformation states are then used to estimate breathing volume through a per-patient correlation metric acquired through a Bayesian-network learning process. Through prototyping and implementation, our results indicate that we have achieved 92.2% to 94.19% accuracy in the tidal volume estimations through the experimentation based on the proposed vision-based method.

Poster: Continuous and Fine-grained Respiration Volume Monitoring Using Continuous Wave Radar

An unobtrusive and continuous estimation of breathing volume could play a vital role in health care, such as for critically ill patients, neonatal ventilation, post-operative monitoring, just to name a few. While radar-based estimation of breathing rate has been discussed in the literature, estimating breathing volume using wireless signal remains relatively intact. With the presence of patient body movement and posture changes, long-term monitoring of breathing volume at fine granularity is even more challenging. In this work, we propose for the first time an autonomous system that monitors a patients breathing volume with high resolution. We discuss the key research components and challenges in realizing the system. We also present an initial system design encompassing a continuous wave radar, motion tracking and control system, and a set of methods to accurately derive breathing volume from the reflected signal and to address challenges caused by body movement and posture changes. Our implementation shows promising results in estimating breathing volume with fine granularity.

POSTER: Mobile Device Identification by Leveraging Built-in Capacitive Signature

This work presents on-top, a new device identification method that exploits off-the-shelf capacitive touchscreens to extract its capacitive signatures. The method relies on a key observation that each capacitive touch screen has a unique capacitive signature which are caused by either the difference in touch sensing technologies or the imperfections of the sensor during its fabrication. In particular, the voltage pattern generated by commercial of-the-shelf (COTS) capacitive touchscreens during finger touch sensing are uniquely identifiable. Our preliminary evaluation with actual hardware prototype on 14 mobile touchscreens shows that on-top achieves a promising performance of 100% detection rate without any false positive. We also show that on-top can be used to securely trigger wireless communication while it consumes a very little amount of power (3.5 times lower than triggering using NFC and 2 times lower than using Bluetooth low energy (BLE)).

WiKiSpiro: non-contact respiration volume monitoring during sleep

Respiration volume has been widely used as an important indication for diagnosis and treatment of pulmonary diseases and other health care related issues such as critically ill patients neonatal ventilation, post-operative monitoring and various others. Most of existing technologies for respiration volume monitoring require physical contact with the human body. While wireless-based approaches have also been discussed in the literature, there are still limitations in terms of estimation accuracy and time efficiency preventing these approaches from being realized in practice. In this paper, we present an automated, wireless-based, vision-supervised system, called WiKiSpiro, for monitoring an individuals respiration volume. In particular, we present a system design encompassing a wireless device, motion tracking and control system, and a set of methods to accurately derive breathing volume from the reflected signal and to address challenges caused by body movement and posture changes. We present our preliminary results of WikiSpiro, and identify possible challenges for future research and development.

Thermal-depth fusion for occluded body skeletal posture estimation

Reliable occluded skeletal posture estimation is a fundamentally challenging problem for vision-based monitoring techniques. This is due to several imaging related challenges introduced by existing depth-based pose estimation techniques that fail to provide accurate joint position estimates when the line of sight between the imaging device and the patient is obscured by an occluding material. In this work, we present a new method of estimating skeletal posture in occluded applications using both depth and thermal imaging through volumetric modeling and introduce a new occluded ground-truth tracking method inspired by modern motion capture solutions. Using this integrated volumetric model, we utilize Convolutional Neural Networks to characterize and identify volumetric thermal distributions that match trained skeletal posture estimates which includes disconnected skeletal definitions and allows correct posture estimation in highly ambiguous cases. We demonstrate this approach by correctly identifying common sleep postures that present challenging cases for current skeletal joint estimations, obtaining an average classification accuracy of ~94.45%.

The Growth and Lipid Accumulation of Spirulina sp. Under Different Light Conditions

Spirulina is a filamentous, spiral-shaped cyanobacterium (blue green alga), known as a great resource of natural and bioactive compounds. The colour of Spirulina sp. cell under the red and white light conditions rapidly transferred from green to yellow after 5 days of cultivation. High biomass and lipid accumulation of Spirulina sp. were achieved after 5 days of culture under the red light condition. The results showed that the red and white light conditions induced the growth and biosynthesis organic compounds such as carotenoid and lipid with high concentration compared to the blue condition in Spirulina sp.

Effect of Osmotic Stress and Nutrient Starvation on the Growth, Carotenoid and Lipid Accumulation in Dunaliella salina A9

Dunaliella salina A9 is unicellular green alga isolated from the saltern, Khanh Hoa province, Viet Nam. The effect of halostress and nutrient starvation was studied in this alga to estimate the growth, chlorophyll content and capacity of carotenoid and lipid accumulation. The results showed decrease in cell number and chlorophyll content as Dunaliella salina in response to a change from the optimal medium 1.5M NaCl to hypo-osmotic medium (0.5M NaCl) and hyper-osmotic medium (3.5M NaCl). We also observed decrease in cell count in nutrient starvation after 9 days of culture in MD4 medium. Salinity stress has more severe effect on the growth of Dunaliella salina A9 with greater decrease in cell number compared to nutrient starvation. The stress induced increasing carotenoid and lipid accumulation in cells. However the carotenoid and lipid accumulation in hypo-osmotic stress and the nutrient starvation were higher than in hyper-osmotic stress. The results suggested negative relationship between the growth rate, chlorophyll content and carotenoid, and lipid accumulation of Dunaliella salina under stress conditions.