We are performing long-term measurements of autonomic nervous system (ANS) activity on patients with epilepsy. In certain cases, autonomic symptoms are known to precede seizures. Usually in our data, the autonomic changes start when the seizure shows in the EEG, and can be measured with a wristband (much easier to wear every day than is wearing an EEG.) We found that the larger the signal we measure on the wrist, the longer the duration of cortical brain-wave suppression following the seizure. The duration of the latter is a strong candidate for a biomarker for SUDEP "Sudden Unexpected Death in Epilepsy", and we are partnering with scientists and doctors to better understand this. In addition, bilateral changes in ANS activity may provide valuable information with regards to seizure focus localization and semiology.

A smile is a multi-purpose expression. We smile to express rapport, polite disagreement, delight, sarcasm, and often, even frustration. Is it possible to develop computational models to distinguish among smiling instances when delighted, frustrated, or just being polite? In our ongoing work, we demonstrate that it is useful to explore how the patterns of smile evolve through time, and that while a smile may occur in positive and in negative situations, its dynamics may help to disambiguate the underlying state.

The wide availability of low-cost, wearable, biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a new challenge: in order to interpret the collected longitudinal data, we require the matching contextual information as well. Collecting weeks, months, and years of continuous biophysiological data makes it unfeasible to rely solely on our memory for providing the contextual information. Many view maintaining journals as burdensome, which may result in low compliance levels and unusable data. We present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information.

Complex and expensive medical devices are mainly used in medical facilities by health professionals. IDA is an attempt to disrupt this paradigm and introduce a new type of device: easy to use, low cost, and open source. It is a digital stethoscope that can be connected to the Internet for streaming physiological data to remote clinicians. Designed to be fabricated anywhere in the world with minimal equipment, it can be operated by individuals without medical training.

Can we recognize stress, mood, and health conditions from wearable sensors or mobile phone usage data? We analyze long-term multi-modal physiological and behavioral data (electro-dermal activity, skin temperature, accelerometer, how often you use your mobile phone, how often you make calls/sms) during day and night with wearable sensors and mobile phones to extract bio-markers related to health conditions, interpret inter-individual differences, and develop systems to keep people healthy.

Physiological arousal is an important part of occupational therapy for children with autism and ADHD, but therapists do not have a way to objectively measure how therapy affects arousal. We hypothesize that when children participate in guided activities within an occupational therapy setting, informative changes in electrodermal activity (EDA) can be detected using iCalm. iCalm is a small, wireless sensor that measures EDA and motion, worn on the wrist or above the ankle. Statistical analysis describing how equipment affects EDA was inconclusive, suggesting that many factors play a role in how a child’s EDA changes. Case studies provided examples of how occupational therapy affected children’s EDA. This is the first study of the effects of occupational therapy’s in situ activities using continuous physiologic measures. The results suggest that careful case study analyses of the relation between therapeutic activities and physiological arousal may inform clinical practice.

We are developing a mobile phone-based platform to assist people with chronic diseases, panic-anxiety disorders, or addictions. Making use of wearable, wireless biosensors, the mobile phone uses pattern analysis and machine learning algorithms to detect specific physiological states and perform automatic interventions in the form of text/images plus sound files and social networking elements. We are currently working with the Veterans Administration drug rehabilitation program involving veterans with PTSD.

In the next year, roughly 26 million Americans will suffer from depression. Many more will meet the clinical diagnosis for an anxiety disorder. While psychotherapies like cognitive-behavioral therapy are known to be effective for these conditions, the demand for these treatments exceeds the resources available. There are simply not enough clinicians available. Access is also limited by cost, stigma, and the logistics of scheduling and traveling to appointments. What if we could crowdsource this problem? Panoply is a crowd-based platform for mental health and emotional well-being. In lieu of clinician oversight, Panoply coordinates therapeutic support from anonymous online workers who are trained on demand. The system utilizes advances in collective intelligence and crowdsourcing to ensure that feedback is timely and vetted for quality.

Have you ever wondered which emails, phone calls, or meetings cause you the most stress or anxiousness? Well, now you can find out. A wristband sensor measures electrodermal activity (EDA), which responds to stress, anxiety, and arousal. Each time you read an email, place a call, or hold a meeting, your phone will measure your EDA levels by connecting to the sensor via Bluetooth. The goal is to design a tool that enables the user to attribute levels of stress and anxiety to particular events. FEEL allows the user to view all of the events and the levels of EDA that are associated with them: with FEEL, users can see which event caused a higher level of anxiety and stress, and can view which part of an event caused the greatest reaction. Users can also view EDA levels in real time.

StoryScape is a social illustrated primer. The StoryScape platform is being developed to allow for easy creation of highly interactive and customizable stories. In addition, the platform will allow a community of content creators to easily share, collaborate, and remix each others' works. Experimental goals of StoryScape include its use with children diagnosed with autism who are minimally verbal or non-verbal. We seek to test our interaction paradigm and personalization feature to determine if multi-modal interactive and customizable stories influence language acquisition and expression.