Active Pen
ADHD Customized Tutor For students to Unleash Unidentified Potential
The smart pen is designed to help ADHD students aged 12-18 regain focus and unlock their potential through advanced, affordable technology. It addresses key challenges like early symptom identification and long-term care by offering real-time data tracking, AI-driven behavioral analysis, and personalized learning support. The sleek, ergonomic design minimizes sensory overload while interactive exercises simplify complex tasks.
Inattention
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Have trouble holding attention on tasks or play activities.
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Often not listen when spoken to directly
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Forgetful in daily activities
Hyperactivity and Impulsivity
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Have trouble holding attention on tasks or play activities.
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Often not listen when spoken to directly
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Forgetful in daily activities
Others
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OthersMood disorders such as depression or anxiety
Product Design Logic:
We aim to keep track of…
1.How many times the student leaves the chair (big movements)
2.How much the student’s hand moves by measuring the wrist
3.How long the student can focus for the longest time -> combined with various tasks
Problem
Analysis
OUR DESIGN
Our solution package entails a hardware (smartpen), an APP, and a cloud network for students, parents, and ADHD coaches to enhance concentration as “addictive”as gaming
ACTive Hardware
ACTive APP
ACTive Cloud
Utilizes advanced machine learning algorithms such as KNN, random forest, and Neural Networks to analyze users’ behavioral data
ACTive APP
Users can also choose to enjoy the full functions at a relatively low price (beginning at $4.99/mo)
ACTive APP
With the ACTive pen, users can enjoy the basic function of our service for free, to enlarge our social impact.
Design Challenges & Next Steps
Although still under development, the smart pen project has made significant progress in both hardware selection and data system design.
After extensive research, I identified the Raspberry Pi paired with the SenseHAT as an ideal toolkit for capturing rich motion data — including 3-axis acceleration, orientation (roll/pitch/yaw), and gesture patterns — which are crucial for analyzing focus-related behaviors in students with ADHD.
However, during the prototyping phase, a major challenge emerged:
The physical size of the Raspberry Pi and sensor board made integration into a pen-like form factor nearly impossible.
Mounting the system externally compromised portability and user comfort — both of which are essential for real-world usability.
Moving forward, I’m exploring two main directions:
1. Miniaturization – Sourcing more compact sensors with equivalent precision and data fidelity.
2.Redesign – Customizing or restructuring the existing components to better fit within the constraints of a handheld device.
This phase marks a critical turning point — shifting from functionality to feasibility — as I work toward building a truly wearable, user-friendly smart tool for attention monitoring and support.