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Time Series Analysis – Example Application. 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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Time series analysis example application l.jpg

Time Series Analysis – Example Application


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


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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."


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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


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Dive Tables


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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


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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.


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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


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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

    • Training data

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

    • For example a decision tree

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


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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

  • Divers need better tools to understand their own dives.

  • Dive profile data and tissue saturation data are time series

    • They contain information about the diving behaviour of a scuba diver


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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.


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Segmenting a profile into zones


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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


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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


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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


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Fact Vs Interpreted

  • Data analysis produces factual information such as:

    • The diver ascended rapidly for a while and

    • The diver stopped twice on the ascent

  • What is missing is the domain knowledge:

    • The rate of ascent is more than the recommended rate and

    • One of the stops is unnecessary

  • Without such domain knowledge it is impossible to communicate useful and interesting information (interpreted information) to the user


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Dive Data Interpretation

  • Features and patterns (abstractions) extracted using data analysis methods cannot be communicated to the user directly

    • No use in saying “you have a saw tooth pattern on your dive profile”

      • If the user is ignorant of the implications of that pattern

  • Abstractions need to be mapped to domain features

    • Useful to say “Your buoyancy control in the bottom zone was poor as indicated by ‘saw tooth’ patterns”

  • A given abstraction may have more than one interpretation

    • Saw tooth pattern could be either due to poor buoyancy control or the result of the diver moving up and down inspecting a ship wreck

    • Knowledge of the dive context and user modelling can help in disambiguating data interpretation


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ScubaText

  • ScubaText is a research project in our department

  • Task: produces reports on interpretations of dive data

  • Input: Dive Computer Data – dive profile and tissue saturation series

  • Output: Reports contain textual presentations combined with annotated graphical presentations (visualizations) of dive data

    • Text presents an interpretation of the data/graph

    • Annotations on the graph provide interpretations of some of the phrases in the text

  • Users:

    • scuba divers

    • scuba instructors

    • health professionals attending to divers

    • general public (dive blogs)


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Dive Computer Data

Data Analysis

Data Interpretation

Text Generation

Graph Generation

Textual Description

Annotated Line Graph

ScubaText Prototype

  • Prototype architecture is a four stage pipeline

Data Analysis

analysing raw data for required features/patterns

Data Interpretation

mapping the data features/patterns to the actual dive features

e.g. saw tooth pattern (data pattern) mapped to poor buoyancy control

rating the dive based on the dive features

e.g. long bottom times receive low rating


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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.


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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 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


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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


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