What it does
Our wearable helps people with eczema become aware of subconscious scratching using EMG sensors. It gently alerts users with vibrations and soft finger splints, encouraging gradual behaviour change through subtle, non-intrusive feedback that fits daily life.
Your inspiration
Three out of seven people in our team have eczema: a chronic skin condition that affects both children and adults, and the impact it has on physical and mental health is often unacknowledged. Scratching is a subconscious action that worsens eczema symptoms, leading to infection and emotional distress. Recognising the lack of real-time behavioural interventions, we were inspired to create a solution that could break the itch-scratch cycle in a non-invasive, user-friendly way.
How it works
The device is worn on the forearm. It integrates an MPU6050 motion sensor to capture acceleration and gyroscopic data, as well as data from EMG sensors. A microcontroller (Raspberry Pi Pico and Raspberry Pi 5) processes these signals and identifies motion patterns consistent with scratching using a trained machine learning model. When scratching is detected, a vibration motor is triggered to alert the user along with a feedback mechanism. Data can be logged for caregivers or dermatologists to track behaviour over time.
Design process
We followed an iterative design process beginning with a research phase, where we reviewed literature on eczema behaviour, existing interventions, and wearable feedback systems. From this, we defined the problem by identifying user needs such as comfort, low weight, responsiveness, and unobtrusiveness. In prototyping, we integrated motion and EMG sensors, a microcontroller, and a haptic motor into a glove-like wearable and developed a lumped parameter model of the feedback loop to simulate system response. During the machine learning phase, we collected motion data and trained a classifier to distinguish scratching from other motions like tapping or waving. Finally, we validated the system’s accuracy through trial runs and user feedback, refining sensitivity thresholds to balance false positives and negatives.
How it is different
Unlike passive eczema treatments such as creams or wraps, our invention actively engages the user through real-time feedback. Key differentiators include machine learning detection that improves accuracy over time, non-disruptive alerts designed not to interfere with sleep, and a comfortable, adjustable design that minimises stigma. Additionally, the modular system has the potential to integrate with mobile apps for tracking and personalisation, enhancing user experience and long-term management.
Future plans
We envision scaling the design into a fully commercial wearable featuring enhanced battery life and wireless connectivity, along with integration into mobile health platforms for data tracking and dermatologist feedback. The device’s purpose could also expand to support habit tracking and behavioral therapy for conditions such as nail biting or hair pulling. Clinical trials and partnerships with dermatologists are planned as part of our roadmap. In the long term, this technology could evolve into a versatile platform for various real-time behavioral interventions in healthcare.
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