Loris Emanuelli
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2026 / UC Berkeley

Stride Recover Hamstring

Human-AI wearable concept for hamstring strain recovery

Physical wrap prototype with live dashboard

Overview

A low-friction smart wrap concept for recreational athletes recovering from hamstring strain, combining user research, AI-assisted synthesis, sEMG-inspired sensing, and simple return-to-play feedback.

Problem / Context

Recreational athletes often manage hamstring injuries without professional supervision. The core gap was not only measurement, but trustworthy guidance that fits existing training and recovery routines.

Role

Team project (Human-AI design) - UC Berkeley

Institution

UC Berkeley

Team

Loris Emanuelli and Human-AI design team

Tags

Human-AI / Wearables / Sports Engineering

Process

  • - Ran semi-structured interviews and field observations with recreational athletes
  • - Used AI to cluster interview themes, map concepts, and challenge design assumptions
  • - Generated and screened roughly forty concepts for feasibility, novelty, and user fit
  • - Mapped the recovery system around confidence, adherence, feedback accuracy, and alert fatigue
  • - Built a physical wrap prototype with sensing electronics and dashboard mockups
  • - Defined status states and exercise recommendations around readable recovery feedback

Key design decisions

  • - Focused on confidence and routine integration instead of a purely technical sensing product
  • - Used bilateral activation comparison and simple risk states to reduce interpretation burden
  • - Kept the wearable form familiar so setup friction stayed low
  • - Used AI as a synthesis and critique tool while keeping final decisions grounded in user evidence

Engineering details

  • - Wearable sleeve / wrap prototype with electrode-style sensing points
  • - Dashboard states for normal rest, attention, and danger
  • - Adaptive feedback model linking user profile, regression modeling, live data inference, and suggested exercises
  • - Prototype electronics and visual interface used to demonstrate recovery feedback behavior

Outcomes

  • - Produced a complete design arc from research to physical prototype and interface concept
  • - Identified trust, comfort, and setup effort as critical adoption constraints
  • - Translated injury recovery uncertainty into a clearer feedback and exercise recommendation flow
  • - Built portfolio-ready artifacts including poster, dashboard, prototype photos, and system model

Gallery

Physical wrap prototype with live dashboard
Physical wrap prototype with live dashboard
Normal-rest dashboard state
Normal-rest dashboard state
Danger dashboard state
Danger dashboard state
Adaptive recovery feedback model
Adaptive recovery feedback model
Early sensing sleeve prototype
Early sensing sleeve prototype
Stride Recover brand and concept poster
Stride Recover brand and concept poster

What I would do next

  • - Validate sensor placement and signal quality during controlled hamstring exercises
  • - Run user testing on comfort, trust, and interpretation of risk states
  • - Refine the dashboard into a mobile-first recovery workflow
  • - Connect guidance recommendations to clinician-reviewed recovery protocols