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From Uncertainty to Innovation: Wearable Prototyping with ProtoBot

İhsan Ozan Yıldırım, Cansu Çetin Er, Ege Keskin, Murat Kuşcu, Oğuzhan Özcan

TL;DR

This work designed ProtoBot, leveraging large language models, and conducted a case study with four professionals from different disciplines through playful interaction, demonstrating the use of large language models in rapid prototyping of wearable electronics for the first time.

Abstract

Despite AI advancements, individuals without software or hardware expertise still face barriers in designing wearable electronic devices due to the lack of code-free prototyping tools. To eliminate these barriers, we designed ProtoBot, leveraging large language models, and conducted a case study with four professionals from different disciplines through playful interaction. The study resulted in four unique wearable device concepts, with participants using Protobot to prototype selected components. From this experience, we learned that (1) uncertainty can be turned into a positive experience, (2) the ProtoBot should transform to reliably act as a guide, and (3) users need to adjust design parameters when interacting with the prototypes. Our work demonstrates, for the first time, the use of large language models in rapid prototyping of wearable electronics. We believe this approach will pioneer rapid prototyping without fear of uncertainties for people who want to develop both wearable prototypes and other products.

From Uncertainty to Innovation: Wearable Prototyping with ProtoBot

TL;DR

This work designed ProtoBot, leveraging large language models, and conducted a case study with four professionals from different disciplines through playful interaction, demonstrating the use of large language models in rapid prototyping of wearable electronics for the first time.

Abstract

Despite AI advancements, individuals without software or hardware expertise still face barriers in designing wearable electronic devices due to the lack of code-free prototyping tools. To eliminate these barriers, we designed ProtoBot, leveraging large language models, and conducted a case study with four professionals from different disciplines through playful interaction. The study resulted in four unique wearable device concepts, with participants using Protobot to prototype selected components. From this experience, we learned that (1) uncertainty can be turned into a positive experience, (2) the ProtoBot should transform to reliably act as a guide, and (3) users need to adjust design parameters when interacting with the prototypes. Our work demonstrates, for the first time, the use of large language models in rapid prototyping of wearable electronics. We believe this approach will pioneer rapid prototyping without fear of uncertainties for people who want to develop both wearable prototypes and other products.

Paper Structure

This paper contains 15 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Wearable Prototyping Kits
  4. Playful Wearables
  5. Method
  6. Procedure
  7. Participants
  8. ProtoBot
  9. Electronic Components
  10. Concepts Generated During the Case Study
  11. Lessons Learned & Opportunities for Future
  12. Turning Uncertainty Into a Positive Experience
  13. ProtoBot Acting as a Reliable Guide
  14. Adjusting Design Parameters When Interacting With the Prototypes
  15. Conclusion

Figures (3)

  • Figure 1: ProtoBot User Interface
  • Figure 2: Sensors used in the case study: S1 - Line Following Sensor, S2 - Microphone, S3 - APDS-9960 Digital RGB, Ambient Light, Proximity, and Gesture Sensor, S4 - SHTC3 Temperature and Relative Humidity Sensor, S5 - LSM6DSM IMU, S6 - MSP430G2352 Touchpad, S7 - PIR Sensor, S8 - VL53L0CXV0DH/1 Time of Flight Sensor, S9 - IR Transmitter and Receiver, S10 - LTR-553ALS-01 Ambient Light Sensor. Actuators and main components used in the case study: A1 - Addressable RGB LED (on main module), A2 - Speaker, A3 - 5x7 LED Matrix, A4 - OLED Display, A5 - Relay, Main Module - Deneyap G, Battery - 3.7V 1800mAh Lithium Polymer.
  • Figure 3: Wearable device concepts generated by participants: GuidingSteps (upper left), PedalPulse (upper right), BakeHero Glove (lower left), and FitFit (lower right).