Research News

Wearable monitoring is likely to play a key role in the future of healthcare. In many cases, wearable devices may monitor our physiological signals that can indicate mental states, such as emotions. The lab of Rose Faghih has been developing a system called MINDWATCH, algorithms and methods for wearable sensors that collect information from electrical signals in the skin to make inferences about mental activity. While their lab has been successful in translating these physiological signals quickly and effectively, they didn't incorporate direct feedback from the individual’s subjective experiences.

Now, the researchers are incorporating feedback and labels from the users, enhanced with machine-learning, and combining it with their existing model for a fuller, more accurate picture of mental states.

At a high level, the human body can be viewed as a complex dynamical system. It is a complex conglomeration of control systems that each works in turn to manage different variables or states. Unfortunately, a number of the states researchers are interested in — particularly the ones that are more abstract — cannot be measured directly. These include states of emotion, cognition and consciousness. Nevertheless, changes in these unobserved states do give rise to corresponding changes in different physiological signals that can be measured more easily. For instance, we may not be able to directly observe or measure a person’s emotional state, but we can measure subtle changes in a person’s heart rate, breathing or sweat secretions (which in turn affects the conductivity of the skin). These signals can then be used to estimate the states we wish to.

However, the signals researchers use to estimate these unobserved states are “spikey” or “pulsatile” in nature. These spikey signals can be used to estimate the various states of the human body and brain without direct observation. With already-existing methods, you could obtain state estimates, but still wouldn’t have any means to have those estimates agree with the more direct state-related information in possession. This is especially important in experiments involving human subjects where the subjects can indeed provide information related to the unobserved state. This type of more-direct state-related information can be called a “label.”

Incorporating direct feedback from users offers information that can’t be gleaned from biological data alone. For instance, a person with PTSD could have their skin conductance continuously monitored to provide an emotion estimate, but ideally, the final estimate should rely both on these signals and information perhaps obtained on rating scales or through regular questionnaires. It is likewise the case for patients with hormone disorders. Hormone measurements do provide valuable information, but should likely be combined with personal feedback (e.g. regarding feelings of energy/lethargy) to obtain a single complete picture. The authors met this need through a proof-of-principle work on a hybrid type of estimator.

Performing estimation on some data such that what is predicted agrees with available labels falls within the domain of supervised machine learning. This work adapted an existing neural network method for state estimation by adding a penalization term for not agreeing with the labels to enable a hybrid estimator. The proposed hybrid estimator was utilized to determine an aspect of emotion tied to changes in skin conductance (through changes in sweat secretions) and to determine energy states within the body based on pulsatile hormone secretions. A wearable monitoring system that incorporates verbal feedback from the user with physiological signals for hybrid estimation can eventually provide a more complete picture of the user to eventually provide more comprehensive closed-loop care.

Stress has a negative impact on physical health, reduces work productivity, and results in significant annual costs for industries and healthcare. While high stress is known to raise the risk of cardiovascular disease and have negative effects on mental health, It also has key effects on the ability of one to complete tasks by both excessively high or excessively low stress. There has been growing research interest on understanding how real-world stress impacts our body and performance, at work and across life activities

Unfortunately, attempts to simulate their impact in the laboratory or elsewhere are less useful than datasets gathered in real-world circumstances. As a result, researchers have access to fewer real-world stress datasets. Even rarer indeed are such datasets used in longitudinal investigations on the same subjects over time.

Real-world situations are also unrestricted environments. Research-grade equipment is frequently inaccessible, and motion artifact contamination is pervasive. These continue to be some of the biggest barriers to automated emotion decoders outside of the research labs in daily life.

To address the above-mentioned gap, Rose Faghih and her former PhD students Md. Rafiul Amin and Dilranjan Wickramasuriya performed an experiment, in which a set of students' physiological data was gathered over the course of three exams. They used a smartwatch-like wearable device and collected multimodal physiological data. The use of the smartwatch-like wearable device was to provide a seamless data collection experience for the students participating in the experiment.

The investigation shows that it is possible to link the variations in the physiological signals to the exam performance. More details about this study can be found in the corresponding publication titled "A Wearable Exam Stress Dataset for Predicting Grades using Physiological Signals."

To enable other researchers, use this dataset for additional investigations, the research team has made the de-identified data publicly available on the PhysioNet platform. A Wearable Exam Stress Dataset for Predicting Cognitive Performance in Real-World Settings is available at: physionet.org/content/wearable-exam-stress

Ultimately, the researchers believe it would be extremely beneficial to consider how exam performance and the stress that goes along with it interact. It will allow for a wide range of potential applications with the aim of enhancing personal performance. This may, for instance, assist scientists in developing effective interventions to improve each person's performance and increase productivity within a company. Additionally, the knowledge may be used in online and remote learning contexts to connect with students effectively and improve learning outcomes.

One in six deaths from cardiovascular disease is due to stroke. Caused by a blood clot in the brain, a stroke can have severe consequences, even minutes after initially occurring. That is what makes prevention so important.

Now, thanks to NYU researchers, including a collaborative effort between NYU Assistant Professor S. Farokh Atashzar, and NYUAD Assistant Professor Farah Shamout, early monitoring may soon be available at an unlikely location: your eye doctor.

Their project, called EyeScore, is developing a technology that uses non-invasive scans of the retina to predict the recurrence of stroke in patients. They use optical coherence tomography — a scan of the back of the retina — and track changes over time. The retina, attached directly to the brain through the optic nerve, can be used as an indicator for changes in the brain itself.

Atashzar and Shamout are currently formulating their hybrid AI model, pinpointing the exact changes that can predict a stroke and recurrence of strokes. The outcome will be able to analyze these images and flag potentially troublesome developments. And since the scans are already in use in optometrist offices, this life-saving technology could be in the hands of medical professionals sooner than expected.

In a first-of-its-kind study, Center for Cybersecurity researchers led by Damon McCoy have found that YouTube channels with conspiracy content are fertile ground for predatory advertisers — with conspiracy channels having nearly 11 times the prevalence of likely predatory or deceptive ads when compared to mainstream YouTube channels and being twice as likely to feature non-advertising ways to monetize content, such as donation links for Patreon, GoFundMe and PayPal.

Researchers also discovered that:

• Certain scams were more common. Self-improvement ads, many of them get-rich-quick schemes, were seen more frequently vs. mainstream content. So were lifestyle, health and insurance ads — including two advertisers unique to conspiracy channels that were generating leads for insurance scammers. Ads promoting questionable products were also common, such as a supplement that claimed to cure Type 2 diabetes.
• Affiliate marketing was a constant. Among those marketing low-quality products, for example, almost 95 percent used some form of affiliate marketing.
• Videos with ads got far more views. In the conspiracy channels, monetized videos had almost four times as many views as demonetized ones. Since YouTube’s business model relies on advertising, this may be because its recommender algorithm prioritizes videos that contain ads.
• Content pointed to alternative social media sites. Sites like Gab, Parler and Telegram were mentioned more commonly in conspiracy channels than in mainstream ones; Facebook and Twitter were also frequently referenced.

The study was conducted with support from the National Science Foundation.

Electric vehicles are one of the best tools we currently have to combat the effects of climate change. There is one outstanding problem though: it takes a significant amount of time to charge a vehicle. Even fasting-charging units take much longer to “fill up” an EV than do gas pumps to fill a tank. This may seem like a mere annoyance, but for people pressed for time this is no small acceptance hurdle between internal combustion and EVs.

There have been numerous efforts at the material-level to improve charging rates through engineered electrode coatings and nanostructuring of active materials, which limit energy density in the battery pack. Additionally, the study of light interaction with energy storage materials has gained interest for use in photo-rechargeable batteries, and integrated solar energy storage systems, though there have been few studies specifically investigating light interaction with commercially-relevant battery materials. Facilitation of electron movement in a battery material is a key for lowering the resistance to flow of charge. Administration of light to induce light-matter interactions is a possibility to locally alter the electronic nature of the material. In particular, Spinel LiMn2O4 (LMO), a cathode material, was shown to dramatically increase the charging current when exposed to white light during a voltage hold.

Now, a team of researchers led by André D. Taylor, professor of Chemical and Biomolecular Engineering and member of the Sustainable Engineering Initiative, as well as graduated PhD student Jason Lipton and Christopher Johnson of the Argonne National Laboratory, have discovered how different wavelengths of light can change the current in LMO cathodes. The team showed that illuminating with red light results in a higher charging rate compared to both ultraviolet illumination of equal optical power and dark conditions.

The team analyzed the effect of red light in the context of the electronic structure and possible excitations, and showed that Mn d-d electronic transitions occurring under red light illumination are largely responsible for the increased charging rate. They further demonstrated through X-ray absorption spectroscopy methods that LMO Mn-Mn bond distances shorten after d-electron excitation. The shrinkage in the crystal volume beneficially contributes to delithiation kinetics by lowering the resistance to lithium-ion conduction.

The results provide a roadmap for rapid discovery of candidate materials for photo-accelerated fast charging, through the review of calculated density of states data. Besides LMO, there is a wealth of materials to investigate with strong potential for photo-electrochemically induced activity. Once photo-acceleration is optimized for a given materials system, it is possible to envision using a small amount of the source current from a fast-charging station to power thin, flexible LEDs built into the spiral wound 18650 cells in a battery pack, enhancing the fast-charging capabilities in next-generation EVs while minimizing impact to energy density.

Since it appeared in 2019, COVID-19 has claimed over 6 million lives and upended society across the globe. The condition, caused by the SARS-CoV-2 virus, attacks cells in the lungs, heart and brain, among other organs. Researchers soon realized that the disease affected these organs so dramatically because its distinctive spikes binded to the angiotensin-converting enzyme 2, or ACE2 receptor. The protein - common in those organs - provides the entry point for the coronavirus to hook into and infect cells.

ACE2 receptors were thus the obvious choice when testing for or treating COVID-19. By recreating the ACE2 and introducing it to an infected body, the virus would bind to the protein, revealing itself in a test or occupying itself with a ‘fake’ receptor. But relying on the ACE2 protein alone may not provide sufficient binding to find and fight the virus.

Now, researchers from across NYU and led by Jin Kim Montclare, Professor of Chemical and Biomolecular Engineering at NYU Tandon, have created a new protein that has an increased ability to bind to viruses, creating a more efficient tool in the fight against COVID-19. The secret is creating a version of ACE2 that mimics a multivalent assembled protein (MAP). Multivalent assembled proteins are like naturally occurring antibodies. Their bodies have multiple sites that can link and bind to the viruses they are trying to attack, making them far more effective at hooking into their targets.

The ACE-MAP the team designed utilizes a coil-shaped cartilage oligomeric matrix protein, a nanomaterial that Montclare’s lab has used before in different applications. When fused with part of ACE2 across the coils surface, they found that the new materials greatly increased the valency compared to ACE2 alone, potentially binding to multiple virus bodies at a time rather than a single one.

This new material has potential uses in both detection and treatment. Because the biomaterial is so much more effective at attaching themselves to viral bodies, it would require fewer of them compared to the natural antibodies currently used in tests and therapeutics. This technology has possible uses in testing for and treating other diseases with known receptors and a similar structure, such as HIV. Ongoing research will confirm the effectiveness of ACE-MAP in other models, and may be a key component of the fight against COVID-19 in the future.

This work was supported by the National Science Foundation and the Army Research Office.

This research was performed under the direction of Maurizio Porfiri, Institute Professor of Mechanical and Aerospace Engineering, Biomedical Engineering, Civil and Urban Engineering and incoming Director of the NYU Center for Urban Science and Progress (CUSP) at NYU Tandon. Collaborators were Peng Zhang, former researcher in Porfiri’s group and now faculty member at Tennessee Tech, and John Ohanian, research scientist at Luna Innovations.

Webbing structures — from chin straps and parachute material, to space habitats — are extensively employed in engineering systems as load-bearing components. They are frequently subjected to extended ultraviolet (UV) light irradiation, which can affect their integrity and reduce their mechanical strength. Despite technological advancements in structural health monitoring, long-term UV sensing techniques for webbings remain under-developed.

In the study, "Spiropyran-functionalized photochromic nylon webbings for long-term ultraviolet light sensing," published as the lead research (and featured cover) in the Journal of Applied Physics, the investigators explored an enticing solution: a photochromic nylon webbing that, because it comprises spiropyran (SP) functionalized polymers, demonstrates color variation in response to extended UV exposure with controlled, color variation over multiple time scales that is conducive to UV sensing.

The team developed a mathematical model grounded in photochemistry to interpret experimental observations, unveiling the photochromic phenomenon as a multi-step, multi-timescale photochemical process involving several chemical species offering the basis for the inference of the webbing’s color

In their research, the team found that the decay rate of the webbings’ color demonstrated a dependence on the initial concentration of the SP dye. Webbings with the lowest dye concentration maintained sensitivity for four weeks, whereas at the highest dye concentration, they exhibited sensing capability after eight weeks. Thus dye concentration could be customized to meet the lifetime of the targeted applications.

The proposed photochromic webbing and the photochemistry-based mathematical model could inform future designs of UV-sensitive structures that maintain sensitivity under weeks of continuous sunlight UV exposure.

Emotions directly influence the way we think and interact with others in different situations. Additionally, when it interferes with rationality in our decision-making or perception, emotions play an undeniable role.

From another perspective, studies from different fields indicate that human emotions could be impacted by any changes in the environment. Thus, having a solid understanding of the dynamics of emotions could benefit individuals in multiple ways: from the enhancement of therapeutic solutions and maintaining well-being to the prevention of unpleasant emotions and inducing favorable ones. Decoding emotions has been an ongoing research among researchers, dictating joint efforts from behavioral, physiological, and computational angles.

According to Russell's circumplex model, emotions are defined in a two-dimensional arousal-valence circular space. Compared to the arousal index, which designates the severity of the emotional state, internal valence levels determine the quality of emotions. In a high arousal mood, feeling excited or happy is opposed to nervousness and feeling stressed. On the flip side and in low arousal conditions, staying calm stands on the opposite side of feeling bored. Hence, feeling pleasant, which refers to the positive valence, is one of the key factors in one’s health condition and could result in a better quality of life.

In a new piece of research, a team led by Rose Faghih, Associate Professor of Biomedical Engineering, follows the goal of establishing a mechanism to track an individual's emotional valence level and regulate it in a closed-loop framework. Since the emotional valence state is hidden and cannot be measured directly, the authors need to measure it indirectly.

The use of wearable devices and designing decoders to gain insight into the internal brain states provides a good alternative to directly measuring brain activity. Inspired by recent advances in wearable technologies which could measure various physiological signals from humans in the loop, there exists possibilities to track emotional valence state indirectly and in a more practical manner. Among different physiological signals, it has been shown that facial electromyography (EMG) carries valuable information about the internal valence state. According to various research, facial EMG measurements of the zygomaticus muscle (zEMG) give the most distinct indicator of emotional valence state. In fact, changes in the zygomaticus muscle, which could be captured via wearables, could provide researchers with valuable information regarding one’s valence level.

In this research, the authors employ a mathematical framework to relate changes in zEMG signal for inferring the internal valence state. They extract corresponding features from the zEMG signal and estimate the internal valence state. Taking advantage of tracking of emotional valence state in a systematic way, they then propose a knowledge-based control architecture to close the loop and keep the estimated valence state within a favorable range. Within a closed-loop approach, biomarkers are collected to provide information regarding the individual's valence state to then automatically adjust the actuators in response to the current emotional state.

In silico results verify that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, it is envisioned that the future outcomes of this research will include supporting alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare. This study is the very first closed-loop control framework for regulating emotional valence state using facial muscle activity. This research suggests an important new step toward new clinical applications and the self-management of mental health. In the practical implementation of the proposed architecture, a wearable device would collect zEMG signal, and the appropriate practices (e.g., listening to music, breathing exercises) would be suggested to maintain the emotional valence levels within the favorable range.

Faghih, who joined NYU earlier in 2022, has been integrating research into her classroom teaching. The research covered in this Frontiers in Computational Neuroscience paper started as a course project in Faghih’s “State- Space Estimation with Physiological Applications” course at the University of Houston. In this class students had the opportunity to use advanced methods learned in the course to analyze physiological data in hands-on biomedical engineering projects designed by Faghih. Student authors Luciano Branco and Arian Ehteshami were graduate students in the course, and Hamid Fekri Azgomi, who recently completed his PhD under the supervision of Faghih, was a PhD student mentor for the project.

This research was led by Maurizio Porfiri, Institute Professor of mechanical and aerospace engineering, civil and urban engineering, and biomedical, and a member of the Center for Urban Science and Progress (CUSP) at NYU Tandon. Co-authors are Post-Doctoral Associate Peng Zhang of the department of mechanical and aerospace engineering at NYU Tandon and CUSP, and Sean D. Peterson of the University of Waterloo.

For over a century, scientists have sought to understand how fish orient against an incoming flow, even without visual and flow cues. In this study the researchers explore a potential hydrodynamic mechanism of fish rheotaxis — movement away or toward water currents — through the study of the bidirectional coupling between fish and the surrounding fluid.

The researchers point out that a major contribution of the proposed model is the treatment of the fish as an invasive sensor that both reacts to and influences the background flow, thereby establishing a coupled interaction between the fish and the surrounding environment.

By modeling a fish as a vortex dipole, a jet flow with a system of two vortices, in an infinite channel with an imposed background flow, they established a dynamical system that captures the existence of a critical flow speed for fish to successfully orient while performing cross-stream, periodic sweeping movements.

The researcher's juxtaposed their models with experimental observations in the literature on the rheotactic behavior of fish deprived of visual and lateral line cues. The crucial role of bidirectional hydrodynamic interactions unveiled by this model points at an overlooked limitation of existing experimental paradigms to study rheotaxis in the laboratory.