Hello! And thanks for watching this video. I am Unsoo Ha from MIT Media Lab. In this video, I will be telling you about our research on WiStress, a system for Contactless Stress Monitoring using Wireless Signals. This is joint work with my colleagues: Sohrab and Fadel.
We all experience stress during our daily lives, while working on a deadline, worrying about our children, and thinking about the pandemic. But prolonged stress impacts the brain, reduces productivity, and accelerates aging at the cellular level. This has led to a desire for stress monitoring solutions to support mental health and well-being interventions. For example, by monitoring stress, doctors can provide better medications for patients with anxiety or depression. Also, if I can monitor my own stress, then I would want to know when I need to take a break or meditate or go for a walk. The problem, however, is that
Today’s stress monitoring solutions are obtrusive. The gold standard is a cortisol test. It requires extracting saliva and testing for the cortisol hormone, which is correlated with stress. But it’s pretty inconvenient to get a saliva sample every 15minutes. Another approach is to ask users to fill in questionnaires. Which is also pretty annoying, and research has shown that the vast majority of users abandon filling in questionnaires after few days or weeks. To address the challenges with these gold standard approaches, recent research has looked into wearables to monitor stress, for example by tracking breathing and heart rate variability. But wearables for stress monitoring trade-off accuracy for comfort. For example, chest straps achieve high accuracy but are uncomfortable, while wristbands that track heart rate are more comfortable but less accurate So this got us thinking:
Can we monitor stress in a contactless, unobtrusive, and fully-automated way?
In this talk, I will tell you about our system WiStress that is able to monitor stress without any contact with human body. The way we are going to do this is by relying on wireless reflections of the human body. Our device sends a wireless signal, and it reflects off human body and comes back. From the reflections, we extract stress-related features, specifically different movements, heartbeats and breathing features that are associated with stress. Then, these features
are feed into a custom-designed machine learning model in order to infer stress levels. Let me show you a demo of our system working in practice
Here, our system WiStress is located on the desk. We used an RF sensor that operates at 77GHz. The antennas are etched on the board. In step 1, the device locates a nearby user. In the bottom right, you can see the output of the device in this step. The output is shown as a heatmap and the user's location can be seen as a bright spot. As the user starts moving the dot moves with him. The device can automatically detect when the user is sitting nearby and moves on to tracking their stress features
Once the user is nearby, it can start tracking different features. For example, it can track the level of motion, which I show here. When the user starts shaking their leg, it can detect an increase in the motion amount. It can also track heartbeats and breathing. And the reason we can get the response is that these also cause small body movements that can be captured by our system And, if there’s more than 1 person, the system can extract these features for each of them.
one of the biggest challenges in contactless stress monitoring is being able to robustly extract the heart rate variability under natural conditions. Research has repeatedly shown that this variability is highly correlated with stress. But extracting it wirelessly and in a robust way is hard because of interference from different sources of noise in the environment.
Let me show you an example. Here's a simple example of a filtered signal. And what I want to do is to extract the length of individual heartbeats.Now let me show you the rest of the signal: as you can see, the signal suffers from different sources of noise and interference, and it becomes very difficult to extract heartbeats or even identify when the heartbeats are there. To overcome this challenge, we leverage the idea that heartbeats are repetitive and have self-similarity. What I mean by this is that if you take any two consecutive beats, you can see a local similarity between them. In fact, this can be formulated using a self-similarity matrix, which you can see here. In the interest of time, I won't be able to go into it in detail, but the high level idea is that this matrix allows us to find heartbeat intervals automatically even under noise as you can see here . This allows us to robustly extract heart-rate variability.
of course, here I just described one challenge and component of our system, and in the paper we describe how we can also robustly extract breathing and other movements and use them to train a machine learning model that can predict stress.
We evaluated Wistress with 22 subjects in different environments. These figures show some examples where WiStress was located on the desk or table. And the ground-truth stress level was obtained using questiontionnaires.
Our results show that WiStress has an accuracy of 84-95% in long-term and short-term stress monitoring. It worked up to 12 ft and across different orientations. It also works for various activities including sleeping, reading, speaking, and typing
To conclude, Wistress is the first system that can monitor stress levels without any contact with the human body, and achieves up to 95% accuracy across subjects. The system opens up many new capabilities including boosting productivity, user experience evaluation, and sleep quality assessment. With this, I will end my presentation. Thanks so much for watching this video.