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  • Drowning is the third leading cause of death worldwide1
  • Children under thirteen are five times more susceptible to accidental drowning2
  • People possessing swimming and water safety knowledge are 90% more likely to survive drowning accidents1

Background

Prior Work

Evaluation

Work in Progress

  • Significantly improve accuracy for application
  • Give corrective advice for stroke technique
  • Determine current skill level
  • Detect indicators of drowning
  • Classify in real time

References

  • Classes are costly and over 50% of Americans overall cannot swim, which limits swimming education4
  • Most stroke classification research revolves around seasoned swimmers
  • Prior works focused on lap swimming rather than practical survival swimming3

[1] Ruth A Brenner, Gitanjali Saluja Taneja, Denise L Haynie, Ann C Trumble, Cong Qian, Ron M Klinger, and Mark A Klebanoff. 2009. Association between swimming lessons and drowning in childhood: a case-control study. Archives of pediatrics & adolescent medicine 163, 3 (2009), 203–210.

[2] Gordon S Smith. 1995. Drowning prevention in children: the need for new strategies.Injury Prevention 1, 4 (1995), 216.

[3] Joana Costa, Catarina Silva, Miguel Santos, Telmo Fernandes, and Sérgio Faria. 2021. Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification. Sensors 21, 15 (2021), 5162.

[4] Red Cross. 2023. American Red Cross: Vigilance, Strong Swimming Skills & Water Safety. https://www.redcross.org/local/texas/north-texas/about-us/news-and-events/press-releases/american-red-cross-vigilance-strong-swimming-skills-water-safety.html

Advisors: Larry Powell, Tracy Hammond

Lawrence Wong, Michael Taylor | Social Science and Human Development

Analyzing Sensor-Driven Activity Recognition in Swimming Schools Using Deep Learning

Method

Methods

  • Partnered with an Austin Swim School
  • Focused on children with minimal swimming knowledge
  • Captured wrist and lower back accelerometer data for 45 participants
  • Recorded 20 activities ranging from Land to Active Swimming for each participant

Land vs Water (20)

All Water Based Activities (12)

  • Run on 10 set random seeds
  • Classes balanced
  • 60% training set
  • 20% validation set
  • 20% testing set
  • Split by participants

Stage 1

Bidirectional

LSTM

Stage 2

Hybrid CNN + Bidirectional

LSTM

Stage 3

Hybrid CNN + Bidirectional

LSTM

Acceleration vs. Time Graph

Passive vs Active Water Based Activities (17)

Objective

Determine The Viability of a Hierarchical Model for Swimming Activity Recognition

Proof of Concept

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