Occupational stress is on the rise and it has negative impacts on one’s health and productivity. Stress is the leading cause of dissatisfaction at workplace and is also a major contribution to depression. All these tend to decrease the quality of life of an employee and consequently, the productivity of the organization. Therefore, if we can find a way to quantify stress in individuals, day in and day out, we can take precautionary steps to battle it. Being exposed to such work stress on a daily basis we have decided to tackle the issue with an elegant solution.
How it works
We propose our solution- Swipe in Your Stress: This can be done through a pulse meter embedded into the mouse /swiping machines . The pulse signal is picked up when the user places his finger on the sensor(Photoplethysmography techniques-using amount of light reflected back by the blood).The signal is pre-processed using basic filtering techniques and applying a moving average algorithm . Key features like Heart Rate Variability (ratio of low frequency to high frequency components of pulse signal) and statistical parameters of the signal are computed. Based on these features extracted, the stress level can be identified using Affective Computing techniques like the Polyvagal theory which states that every psychological effect has a physiological basis to it. The stress data will be made available to the user over a web app where he can monitor and manage his personalized stress levels. Stress can be controlled by the person experiencing it. And there are myriad ways and techniques to unwind and reduce the load on the brain. The app plays a role in communicating these techniques to the user. Recommendations can range from coffee at the nearest café to music therapy to playing Candy Crush. By recording the vital stats of the user over an extended period of time, the app would be able to map his or her health conditions to their lifestyle.
Challenges we ran into
Hardware connectivity took up some time due to different micro-controllers used but once it was resolved we were back on track.
Accomplishments that we are proud of
We integrated 3 modules in a very successful way - hardware,signal processing module and the UI app. We felt accomplished that we completely used open source software to implement this software
What we learned
How to debug hardware issues and nodeJS concepts
What's next for GlobSol
We are looking to enhance the solution and incorporate it in different domains (like Healthcare).