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Improving Gait Activity Detection from GPS and IMUs with Machine Learning (GRA-041)

Improving Gait Activity Detection from GPS and IMUs with Machine Learning is a documented award listed in the DePaul Resource Map (starting 2023). Funder: DePaul ↔ RFUMS partnership.

Summary ~

  • Sponsor / funder: DePaul ↔ RFUMS partnership
  • Program: AI in Biomedical Discovery and Healthcare
  • Status: awarded
  • Period: 2023 – —
  • Lead unit: LAS / Jarvis CDM
  • Amount: $65,898

Access (availability): 🟢 Green — open or routine · 🟡 Yellow — appointment or affiliation · 🔴 Red — restricted · ⚪/⚫ White/Black — not yet classified

Verification: ✓ confirmed · ~ likely · ? needs review · ✕ archived

Award details

  • Topics: gait, machine learning, wearable sensors
  • Award type: research grant

People

  • Sungsoon (Julie) Hwang (PI)
  • Ilyas Ustun (Co-PI)
  • Muhammad Umer Huzaifa (Co-PI)
  • Chris Connaboy (Collaborator)

None documented.

No related outputs documented yet.

No related publications documented yet.

Verification

  • Status: ~ Likely current
  • Notes: 2023 RFUMS collaborative award; parent program GRA-029. No DePaul facility named in source.

Sources

  • DePaul–Rosalind Franklin awarded projects summary, 2022–23

Notes

Award metadata compiled from published DePaul research reporting. Listing here records documented activity; it is not an evaluation of the award, the people, or the unit.