Poster Session 3, 1:15 PM - 2:00 PM: Room 163 [C18]

Exploring User Demand and Design Requirements for a Weightlifting-Focused Wearable

Presenter: Jason Huang Alexander

Group Members: Ava Sokolosky

Faculty Sponsor: Madeline Lee Endres

School: UMass Amherst

Research Area: Computer Science

ABSTRACT

Commercial fitness wearables primarily emphasize cardiovascular metrics and general activity tracking, offering limited automated support for structured, set-based resistance training. While prior research has advanced repetition detection and intensity classification using wearable sensors, the real-world demand and design preferences of experienced young adult lifters remain underexplored.

This study investigates user demand, feature preferences, and pricing tolerance for a wearable designed specifically for weightlifting. We conducted an exploratory survey of 32 college-aged adults (mean age = 20.8) who work out an average of 4.38 days per week; 91% identified as intermediate or advanced weightlifters. While 59% reported not currently using a wearable during workouts, 87.6% expressed interest in a lifting-focused device. Frequently requested features included automatic rep detection, intensity classification, heart rate monitoring, set and rest interval tracking, and long-term performance analytics. Median willingness to pay was $100, with a notable subset indicating willingness to pay $200-300, suggesting both budget and premium market segments.

To contextualize these findings, we conducted follow-up semi-structured interviews with 10 participants (5 current wearable users, 5 non-users). This will highlight practical friction points with existing devices and guide the development of interview-informed user personas.

Combined quantitative and qualitative analyses informed two user-centered design concepts: (1) a streamlined ~$100 device tracking core weightlifting features, and (2) a premium $200-300 model offering enhanced sensing and analytics. Our preliminary findings suggest unmet demand for resistance-training-specific wearable technology and provide user-centered design insights for future wearable computing systems.