The second AffecTech event was held in Lisbon, Portugal at Plux, one of the main AffecTech partners.
Following from a previous post that discussed the Lancaster first AffecTech workshop (1), I will here discuss some of the insights gained from the Plux Biosensing Hackathon held during the Lisbon AffecTech event.
To set the scene, Plux is a Lisbon based company that: “creates innovative products for Physiotherapists and Researchers, by developing an advanced biosignal monitoring platform that integrates wearable body sensors […] with wireless connectivity and software application.” (2). Perhaps, one of the most famous Plux products is BITalino (3). This is a pioneer open-source biosensing platform that brought forward the concept of DIY to the biosensing community (similarly to what Arduino did for microcontrollers). Since the launch of the platform, a
whole community formed around BITalino with an array of amazing projects building upon it (4).
The hackathon was in fact centered around the BITalino, and took place in the last two days of the Lisbon event. Namely, the AffecTech Early Stage Researchers (ESRs) were split in 5 groups, with each group working to develop a prototype for sensing and actuating in an application related to affective disorders. In the specific, I was grouped with Muhammad Umair (HCI researcher at Lancaster University) and Miquel Alfaras (software engineering researcher at Plux) (5).
The first morning of the hackathon was mainly filled with group discussions about: (i) specific application scenario, (ii) sensor and software implementation guidelines, and (iii) feasibility of delivering a prototype by the end of the following day. As a group, we decided to focus on work-related stress, an impressively pervasive problem in our society. For example in the UK, stress and work-related anxiety are the leading cause of sickness absences from work, with about 70 million days lost each year at an estimated cost of 2.4 billion pounds. Often, heavily stressing working days follow each other in a long lasting sequence, so that a person never gets the chance to adequately relax. The key idea behind our group project was that of developing a platform where each user could share his/her “emotional day” with a selected number of friends. As such, the platform would strive to reduce the stress burden from a specifc person by sharing it with his/her close friend, thus relying on social support.
Implementation and Outcome
The wristband prototype we developed is shown in the picture above. The wristband is composed of many BITalino individual components and sensors. Namely, the electrodermal activity sensor is used to compute a rough estimation of the user’s daily stress. We wrote python code for signal processing and thresholding of the signal provided by the sensor, and accumulated the results in a vector that summarised the occurrence of
arousal episodes throughout the day. The wristband was provided with a Bluetooth module that scanned the
user’s proximity for a similar device. When two of these devices happen to be close to each other, the devices link via Bluetooth. Depending on the daily stress level collected on the first user’s device, the second users’ device start to vibrate (we placed in the wristbands a small vibrator motor taken from a mobile phone). After providing this haptic feedback to the second user, the wristband “discharges” and the stress accumulated in the device is reset to zero. Analogously, this whole procedure is repeated with the two users inverting their roles. Crucially the wristband allows for privacy upon the user’s request. As the two devices come in range, the user is notified by the blinking of a LED light. If the user prefers to keep the “emotional day” private, he/she can quickly tap on the device and the sharing explained above does not occur (a single axis accelerometer sensor is included in the wristband to detect the taps).
About the Author
Andrea Patane is involved in the AffecTech project as an Early Stage Researcher PhD Fellow based at the Department of Computer Science of the University of Oxford, where he is a member of the AIMS Centre for Doctoral Training. Working under the supervision of Professor Marta Kwiatkowska, his role in the AffecTech project is to develop personalised technologies for stress monitoring and regulation.
AffecTech is established with support from the Marie Sklodowska-Curie Innovative Training Network funded by European Commission H2020. As well as its focus on innovative research the AffecTech project is committed to raising awareness about mental health issues.