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Scuba Diving – Example Application. Yaji Sripada. Scuba. Scuba – Self Contained Under-water Breathing Apparatus Scuba diving – popular form of recreational diving 1 million divers get certified every year Safety of all these divers is a serious issue

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Scuba diving example application l.jpg

Scuba Diving – Example Application

Yaji Sripada

Dept. of Computing Science, University of Aberdeen


Scuba l.jpg
Scuba

  • Scuba – Self Contained Under-water Breathing Apparatus

  • Scuba diving – popular form of recreational diving

    • 1 million divers get certified every year

  • Safety of all these divers is a serious issue

    • By diving carelessly, divers suffer from decompression illness (DCI), more popularly ‘the bends’

SCUBA Diver

Dept. of Computing Science, University of Aberdeen


Decompression illness dci l.jpg
Decompression Illness (DCI)

  • Possible explanation of DCI – bubble theory

    • Nitrogen from air absorbed by body tissue because of the water pressure

    • When diver rapidly ascends the dissolved Nitrogen forms bubbles

    • Large bubbles that clog joints cause pain and bends

  • Individual variation of DCI

    • Some don't get bent when they "should," others do get bent when they "shouldn't."

Dept. of Computing Science, University of Aberdeen


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Dive Tables

  • Traditionally dive tables are used for planning dives to avoid risk of DCI

  • Dive tables contain safety limits of depth and time

  • There are many standard dive tables,

    • Original UK, US Navy etc.

  • Dive tables can be generated by an algorithm – many variations possible

  • Dive computers run these algorithms and generate dive tables on the fly

Dept. of Computing Science, University of Aberdeen


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Dive Tables

Dept. of Computing Science, University of Aberdeen


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Dive Computer (DC)

  • Scuba divers wear dive computers

  • DC guides the divers to carry out safe dives

  • DC records logs of dives consisting of

    • Log of all dives and

    • For each dive the following data

      • Time series of dive depth called dive profile

      • Time series of Tissue saturation

      • Temperature

      • SCUBA equipment

      • etc

  • Dive logs from a DC can be downloaded to a PC

Dept. of Computing Science, University of Aberdeen


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Dive Computer Data

  • DCs are equipped with software to view dive log data as shown here

  • Divers are expected to analyse their dives to learn about their safety.

    • For example, the dive shown here is an unsafe dive because the diver performed a deeper dive the second time.

Dept. of Computing Science, University of Aberdeen


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Safety of a Dive

  • Is complicated to determine

  • Depends at least on

    • Diver characteristics – such as age, general health, history of dive related illnesses

    • Dive characteristics – dive profile (depth-time) data, gas mix and dive plan

    • Dive environment – temperature and altitude

Dept. of Computing Science, University of Aberdeen


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Project Dive Exploration (PDE)

  • PDE is a large scale research project sponsored by Divers Alert Network (DAN)

  • PDE collects data (medical history, dive profile etc) corresponding to large numbers of real dives and their medical outcomes.

  • PDE analyses these data to learn the relationship between features of dive data and DCI (or any other medical condition)

  • PDE hopes to develop the science required to label dives as SAFE or UNSAFE (binary classifier)

Dept. of Computing Science, University of Aberdeen


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Understanding Dive Computer Data

  • PDE is ongoing and results are expected in the future

  • Until then divers have to manually inspect dive data to determine the safety of their dives

  • The community of divers is very diverse

    • Many of them may not possess the skills required to use the vendor supplied software

  • We need to help divers better understand their dives.

  • We use this application as one of the example domains in this course

Dept. of Computing Science, University of Aberdeen


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Analysis of dive data

  • To determine unsafe dives

  • The following patterns in dive profiles are known to cause DCI

    • Rapid ascent

    • Sawtooth

    • Unnecessary stops

    • Reverse dive profile etc.

Dept. of Computing Science, University of Aberdeen


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Segmenting a profile into zones

Dept. of Computing Science, University of Aberdeen


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Rapid Ascent

  • A pattern in the dive profile caused by the diver rising rapidly to the surface

  • Rapid ascent is the most critical factor causing bubbles in body tissues

  • Therefore has higher chance of causing DCI

  • Most dive computer software detect rapid ascents and sound alarms

Dept. of Computing Science, University of Aberdeen


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Sawtooth

  • A pattern in the dive profile caused by the diver going down and up in quick succession

  • This may not happen very frequently, but when it happens it may cause the tissues to absorb excess gas bubbles

  • Therefore may cause DCI

  • Dive computer software does not detect them

Dept. of Computing Science, University of Aberdeen


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Reverse Profile

  • This is a pattern observed at the level of a whole dive profile.

  • Ideally a diver is required to initially reach the planned maximum depth and then all the subsequent dive should be performed at a depth shallower than the maximum

  • A reverse profile is a dive profile where the diver performs the reverse of the ideal

Dept. of Computing Science, University of Aberdeen


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ScubaText: Communication of dive data

  • A research project in the department

  • We explore effective ways of presenting the results of data analysis

    • For scuba divers

    • For scuba instructors

    • For health professionals attending to divers

    • For general public (dive blogs)

    • Using visualizations and Text

Dept. of Computing Science, University of Aberdeen


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Dive Computer Data

Data Analysis

Data Interpretation

Text Generation

Graph Generation

Textual Description

Annotated Line Graph

ScubaText Prototype

Data Analysis – analysing raw data for required features/patterns

Data Interpretation – mapping the data features/patterns to the actual dive features and rating the dive based on the dive features

e.g. long bottom times receive low rating

Dept. of Computing Science, University of Aberdeen


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Example Dive- Dive Context

Date: Mon, 04/10/1993 Location: Elba

Time: 15:37 Site: Capo d’Arco

Altitude range: 0m…900m Interval: 00:05

Weather: Cloudy Air Temp: not recorded

Dive suit: two pc. Wetsuit Tank Size: 14.0l

Maximum Depth: 48.0m Dive Time: 00:47

Min. temperature: 190C Airconsumption:131bar

Dive Type: Decompression, single ascent, sea water

Activities: Sightseeing

Alarms: None

Buddies: YYY

Max ascent time: 10’

Dept. of Computing Science, University of Aberdeen


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Text+Annotated Graphics (D)

Risky dive with some minor problems. Because your bottom time of 12.0min exceeds no-stop limit by 4.0min this dive is risky. But you performed the ascent well. Your buoyancy control in the bottom zone was poor as indicated by ‘saw tooth’ patterns marked ‘A’ on the depth-time profile.

Dept. of Computing Science, University of Aberdeen


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Text and Graph

  • Text mainly communicates a safety message

    • Risky dive, Safe dive etc.

  • Uses dive features inferred from raw dive data to explain the main message

  • Links data features to dive features as further explanation

    • E.g. saw tooth pattern linked to poor buoyancy control

  • But not all the terms referring to data features do not have universally accepted definitions

    • Bottom time, bottom zone etc

  • Graph provides semantic grounding for these terms

  • Text and graph are linked

    • References to annotations in the graph

Dept. of Computing Science, University of Aberdeen


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Summary

  • Dive computers record dive data

  • Not possible to label dives SAFE or UNSAFE automatically

  • Divers need help of novel technology to explore their dives

    • Detect unsafe patterns

    • Present the results

      • Graphically

      • Textually

Dept. of Computing Science, University of Aberdeen


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