the whitepaper - Auckland

Transkript

ARDA
Activity
Detection
The cornerstone of
true virtual coaching
A white paper by
ARDA Activity Detection
The cornerstone of true virtual coaching
The underpinning feature of training devices on the market today is their
ability to classify data into ‘training zones’.
A training zone is effectively a range of values that denote a particular
‘mode’for a measured parameter, logged over time or distance and adapted
for a biological factor such as age/height/weight etc. For example, a
30-year-old with a heart rate between 110 and 130 bpm might be referred
to as being in the ‘fat burning zone’ due to the high rate of fat oxidation at
this particular training intensity. Similar training zones can be calculated from
data provided by any tracking device.
Training zones are useful for providing athletes with intensity targets during
a workout. However, there are too many environmental and physiological
variables involved in exercise for training zone analysis to provide any
more complex feedback. Assumptions about these variables can lead to
inaccurate interpretation of exercise data, and consequently inaccurate
coaching advice in an automated system.
For example, asking a user to ‘go into the fast zone’ assumes they are
training on the flat. If the user is running up a particularly large hill, attempting
to comply with the training device’s instructions to speed up could be
frustrating, or could even result in injury. This can cause the user to lose faith
in their training device, or even to stop using it entirely. Further to this, these
assumptions prevent any kind of useful post-hoc workout analysis.
TRAINING
ZONES
HEART
RATE
Figure 1: heart rate training zones
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Does the training zone classification in the above graph help us understand
what actually happened during the workout? We can see the ‘high intensity’
segments, but what terrain was the user on during those segments? If they
were performed on the flat, then the user may have been doing speed work.
If it was a hill, then the user was doing hill training.
This difference may not matter if the goal of a workout is to simply achieve a
particular heart rate, but for true performance analysis, context is absolutely
vital. If a coach knows what kind of training was being conducted when a
set of statistics was gathered, they can compare training segments to similar
segments from previous workouts. This allows them to see which aspects of
performance are improving, and which are deteriorating.
The move to activity detection
The ARDA engine is designed to perform three functions that are missing
from today’s training tools:
1. Provide bespoke coaching to a user in real-time;
2. Modify a user’s training plan based on performance improvement
or deterioration;
3. Help a user achieve very specific performance goals.
To achieve this, raw data that is obtained from a user engaged in exercise
must be processed by breaking up the workout into its component parts
using ARDA’s Activity Classification system.
As the user exercises, the system trawls for patterns in multiple streams of
data generated by linked sensors (such as off-the-shelf accelerometers and
heart rate monitors), waiting for a match to a certain set of criteria.
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An example
This example follows a cyclist training for an upcoming Ironman competition.
The cyclist is male, 29 years old, 74 kg (163lbs), 180cm (5'11") tall. The
athlete is training with a smartphone app powered by the ARDA engine.
As the user cycles, ARDA continually samples the raw data from the cyclist’s
sensors. In this instance a heart rate monitor, a cadence (RPM) meter, and
an embedded GPS receiver. Coordinates from the GPS are matched against
an in-built terrain database to determine current incline.
15 minutes into the workout, ARDA notices the following conditions:
Heart Rate
168 bpm
Cadence 70 rpm
Incline
1%
In ARDA’s classification scheme, these data points are within the
classification range for a ‘big-gear time trial’, or ‘BGTT’ (thresholds for BGTT:
a cadence of 62–77 rpm and a heart rate of 162–178 bpm on an incline of
between -1% and 1%, that occurs for longer than 90 seconds).
If the data remains in this range for 90 seconds, ARDA flags the beginning of
a big-gear time trial activity and starts logging data and providing feedback
based on this activity. In order to detect an activity’s completion in a sensible
way, the software employs ‘edge forgiveness’ criteria for that activity. For
a BGTT, if any parameter falls out of zone for more than 10 seconds, the
big-gear time trial is considered to have ended and the data is labeled
accordingly. This segment can now be compared against historic training of
the same activity.
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HEART
RATE
CADENCE
TERRAIN
POWER
EASY
BGTT
EASY
UP
TEMPO
EASY
UP
TEMPO
EASY
AT
E P E P
EASY
TRAINING
TYPES
WORKOUT
Figure 2: Training Types, or ‘Activities’
The chart above shows classification of cycling data where heart rate,
cadence, and altitude are measured over the duration of a workout. These
parameters have been used to define classifications of different activity
segments, which appear at the bottom of the graphic. (e.g. ‘EASY’, ‘BGTT’,
‘UP TEMPO’ etc).
In ARDA’s view, a workout is made up of a series of activity segments which
together form the complete workout. The activity detection process can be
applied to any cyclic sport. The ARDA engine currently supports running,
cycling, swimming, and rowing.
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Reliability from Research
The sheer number of possibilities – overlapping data ranges, edge cases,
and unexpected scenarios – makes activity detection by data analysis a very
difficult problem to solve. If a virtual coach determines training context with
a low level of reliability, any feedback given to the user or analysis performed
on the data would be very difficult to trust.
A virtual coach cannot in good conscience tell an athlete that they are
suffering from muscular fatigue unless it knows what other athletes’
biometric data looks like on a similar incline, in similar weather conditions,
with a similar training history, weight, height, and gender etc.
The ARDA engine activity detection system is based on 20 years of research
conducted during the professional coaching of over 3,000 men and women
of all ages, from elite athletes training to win world championships, to
sedentary individuals training for health and wellbeing.
The techniques forming the basis of the ARDA software have been in use by
PL Tech for many years, with a long list of successes in the field of remote
performance training for elite athletes.
Activity Detection revolutionises workout analysis
Accurately and reliably classifying workout segments into training types
allows a training device to start doing much more with the data it receives.
The main areas of difference are true coaching advice, smart plan
adjustment, and user-guided workouts.
True coaching advice
The difficulty of generating coaching advice using an automated training
device is in determining the reasons why changes to the athlete’s biometric
data have occurred. A particular change, or combination of changes, in
heart rate, stride rate, or pace, might be entirely expected during a hill
workout, but on the flat the same changes might indicate a problem that
needs correction.
Contextualised activity data allows ARDA to compare a runner’s data to
a ‘baseline’ for a particular activity. This baseline corresponds to what
an athlete ‘should’ be accomplishing based on their personal goals, their
biometrics, and their training history.
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In addition, contextualised raw data allows ARDA to keep track of
performance trends over multiple workout sessions, augmenting a device’s
existing ability to track general trends such as pace or general fitness.
ARDA’s analysis engine detects the gap between a user’s ideal performance
for an activity (based on their goals), as well as any trending areas of
weakness based on prior workouts, and generates advice designed to close
those gaps.
Generated advice is prioritized, favouring more significant events over
less significant events, and favouring broader trend summaries over more
specific single issues. This prioritized coaching direction can then be given
to the user audibly or visually.
Without activity detection, today’s devices are forced to limit their feedback
to effort correction, i.e. ‘go faster’, or ‘go into the easy zone’. Activity
Detection allows a device to make much more detailed corrections. ARDA
can suggest corrections to a user’s stride length, or make subtle pace
corrections based on comparative performance on different inclines.
Activity Detection shifts the focus of a training device from enforcing generic
training plans to analysing how well an athlete is performing a particular task.
This feature allows for a change in the paradigm of workout prescription.
Users of training devices commonly complain that they are forced to enter
speedwork sessions at inappropriate times. ARDA switches this around, and
simply informs the user that they have to do a speedwork session at some
point during their run. ARDA will detect when speedwork is taking place,
and check it off the list.
Additionally, a workout plan can now call for more detailed behaviours that
depend on a user finding the correct environment, such as ‘5 steep hill
climbs’.
Smart Plan Adjustment
Because ARDA’s activity detection engine takes into account a user’s
biometric data (such as height and weight) and training history when a
person starts using the device, it has the ability to generate highly specific
training plans for users. In fact, there are dozens of questions that can be
asked to refine an initial workout plan for a user.
ARDA views these training plans in a unique way. Instead of a static,
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cookie-cutter programme, an ARDA training plan will adapt over time
based on the user’s performance trends. For example, if a user is showing
excellent performance improvement on steep hill climbs, but is regularly
demonstrating poor speedwork, the engine can shift the focus of future
workouts onto race-pace training or pacing techniques.
Furthermore, if an athlete is showing high levels of fatigue, ARDA can modify
or postpone workouts to aid recovery.
ARDA is a software engine capable of providing true performance analysis,
coaching advice, and adaptive plans to amateur users and pro athletes
by integrating with existing training devices. Performance Lab is currently
approaching hardware vendors to license the technology.
For more information, or to schedule a tech demo
of the ARDA engine, please contact PLTech:
Email [email protected]
Phone Kerri McMaster +64 9 480 1422
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