Interpreting Exercise Data: Guidance and Coaching...

1 Performance Lab Technologies © Updated August 2017

Interpreting Exercise Data: Guidance and Coaching Module *=Unique to Performance Lab

Performance Lab has the only context based AI exercise coaching system in the world.

Performance Lab’s software is able to:

• Detect the activity the user is engaged in,

• Compare the user’s specific activity data with other similar data,

• interpret the information and provide feedback. (Figure 1)

Figure 1. Map of Run Completed with Performance Lab’s AI Coaching Software. Each Dot of the Route Indicates a Coaching Comment Provided by the AI System with one Comment Highlighted to Show the kind of Feedback Supported.

Experiences: There are two possible guided exercise experiences:

• Guidance (metrics, gamified ‘winning the metric’, progress, in zone assistance)

• and coaching. (extra interpretative layer that suggests appropriate behaviour changes as a

coach would provide.)

Unique Functionality That Drives the Coaching Experience: Performance Lab’s system is the first to be able to truly coach a user for two reasons:

• Granular Activity Detection


• Contextual Interpretation

a) Granular Activity Detection:

Being able to detect whether someone is walking, running, cycling or rowing makes for very coarse

identification of activity. Performance Lab’s patented algorithms1 allow much deeper insights to be

obtained due to its ability to contextually analyse a situation. The reason that detailed activity

identification has been very difficult until now is that exercise data is a tangle of interrelationships

between parameters2 (Figure 2):

Figure 2. Graph Showing Tangle of Relationships Between Heart Rate (Red), Speed (Dark Blue), Power (Light Blue), Stride Rate (Orange) and Terrain (Brown) During a Hilly Run.

Performance Lab’s technology can make sense of the data patterns by locating contextual signatures

in the data that identify an activity or sub activity (known as Training Types*)3. Within the mess of

multiple data parameters, when a match occurs, an activity is identified. (Figure 3) See: Detecting

Activity: Contextual Classification Module.


Figure 3. Graph Showing a Simple Contextual Classification (Yellow strip) where Speed and Terrain are Used.

We also see in Figure 3’s example of simple Training Type classifications that there are color coded

segments of different classifications.

• Gray means that the system is paused. (walking, standing)

• Green means Easy running (on the flat)

• Light Blue means Tempo running (on the flat)

• Dark Blue means High Intensity running (at Anaerobic Threshold on the flat)

• Red means climbing

• And Yellow means Unclassified (downhill).

All the data for Figure 3 above is segmented as follows (Figure 4):

Figure 4. Graphic Indicating What Each Colour Coded Segment from Figure 3 means as a Classification for Running.

All the data within each Training Type classification is logged and labelled (Figure 5) for further

analysis:


Figure 5. Logged Data for Each Automatically Detected Activity Classification as: a) Individual Classifications Detected in a Timeline and b) a Summary of All the Data in the Workout for Each Classification.

The Classifications are also logged by location for future locational comparisons: (Figure 6)

Figure 6. Map of Simple Running Activities: a) Easy Running (green) and b) Hill Climbs (red).

The main benefit is that each Training type classification has its own unique and tightly constrained

set of criteria which means very detailed and accurate comparisons* can be made between current

data and historic data of the same classification4. (Figure 7)


Figure 7. Graphic Showing the Data Comparisons Made Possible by Very Specific Tightly Constrained Classifications.

Figure 8 shows a simple comparison between two Training Type classifications; one being from a

current workout and the other being from a workout, 6 weeks previously. Heart rate is higher for a

lower wattage indicating either a loss of fitness or fatigue. Other data context is brought in like

workout air temperatures (thermal stress), number of cumulative watts through the workout (same

point in workout), number of workouts completed and cardio performance in training sessions

between the compared workouts. (loss of fitness, lack of training)

Figure 8. Graphic Showing the Data Comparisons Between Up Tempo Training Type Classifications 6 weeks apart.

We have now arrived at a point where the data can be truly interpreted and this happens through

feedback through to the user.

Feedback:


Exercise Guidance and Coaching provides feedback via commentary.

Commentary Points: Figure 9 shows in a workout viewer, data collected from a running workout of the Performance Lab

coaching system where heart rate, speed and altitude are being measured with power is being

inferred. (more or less sensors can be involved)

The coaching system can provide feedback to the user in real time or post workout. This feedback is

displayed in Figure 9 as ‘Commentary Dots’. Each Dot contains a comment generated by the AI

Coaching system as the user moves through the workout and is based on what the coaching system

detects the user to be doing.

In the workout viewer, mousing over (vertical grey line) a commentary dot displays the comment in

the comments section of the viewer.

Figure 9. Graphic Showing the Workout Viewer with workout data, commentary dots and commentary at a particular point in time. (vertical grey line)

The comments are available in audio or in text as shown on the ARDA phone app below. (Figure 10)


Figure 10. Screen Shots Showing Feedback in a Text Form.

Commentary List There are 131 possible areas of feedback currently with multiple comments for each across 69

different areas*. (Figure 11)


Figure 11. Table Showing all the Possible Commentary Variations Depending on the Activity, Type of Workout and Level of Experience of the User.

Commentary is structured so different forms of coaching are available depending on

• the user’s activity,

• the type of workout they are doing and

• their experience level.

There is also a Verbosity Control which allows a user to turn the amount of commentary up or down

depending on their preferences.

Types of Feedback Feedback can be supplied as discussed in two ways:


• Guidance and as

• Coaching.

In both scenarios, some form of feedback must be provided to the user.

Exercise Guidance: Guidance involves

• information on compliance progress through the workout,

• methods that help a user to remain in a training zone,

• cumulative and real-time workout progress and

• gamified progress information on ‘winning the goal metric’ for the workout (e.g. minutes in

a training zone, vertical meters climbed, distance covered.)

Coaching Coaching* requires an extra interpretation layer of logic where events are detected and coaching

recommendations are formulated and provided to the user as feedback. This can be informative,

motivational and friendly designed as if someone was helping and coaching you as you exercised.

Over 50% of people exercise alone.6 In the Physical Activity Council’s 2017 Participation Report the

main barrier given for not exercising were, having someone to take part with and having a friend

come along.5 The gym company Equinox have taken a similar approach and say: “People who are

going through the chatbot experience are seeing almost a 40 percent increase in their engagement

in subsequent weeks than people who don’t.”6

Coaching:

Compliance

During the Workout During exercise, there are 3 situations where the coaching system uses activity detection for

compliance:

• Performance Lab has an Exercise Plan Generator7 (Figure 12) that provides the coaching

system with knowledge of the types of training scheduled for the workout,

• a custom plan/workout can be set by a coach or trainer providing a workout schedule

• or in an unplanned workout, the system can still detect the different types of training

engaged in.

See: A Machine That Can Write a Custom Training Program: Exercise Plan Builder Module.


Figure 12. Training Plan Showing that within the Workout, 2 Up Tempo Training Type Reps are Scheduled.

Once the runner is doing the workout, the Performance Lab system can detect all the different

Training Types automatically and keep track of them against the plan*. (See Comment’s in Figure 13)

Figure 13. Graphic Showing All the Commentary Dots with the gray vertical line just after a 2nd light blue block (depicting an Up Tempo Training Type Classification). The comment at this point shows that the system detected the Up Tempo rep, knew it was the 2nd occurrence in the workout, summarised the logged data for the segment (distance, duration, stride rate and average pace) which can then be compared to Training Plan Criteria.

After the Workout: After the workout (as shown in comments Figure 14), the coaching system knows what was

scheduled so it knows that the 54 mins of running was longer than planned. In this case, it also

knows that 2 Up Tempo reps of 30 seconds were scheduled. The ability to detect sub activities

(Training Types) within the workout means that the system could tell that 1 min 24s of Up Tempo

was over the prescribed amount but that the correct number of Up Tempo reps were completed*:


Figure 14. Graphic Showing Commentary at the End of the Runner’s Workout Indicating the Systems Knowledge of Both the Workout Schedule and Types of Training Types Performed Within the Workout.

Performance: Performance Lab’s proprietary Cardio Performance algorithms8 can also measure day to day Cardio

Performance*. (fitness)

During the Workout:

In each workout, the Performance algorithm measures a user in two ways; (Figure 15)

• the Cardio Performance Capacity (fitness) or

• the Cardio Performance Endurance (measures endurance and fatigue rate)

Figure 15. Cardio Performance Capacity and Cardio Performance Endurance measures.


The coaching can provide feedback in real time if it detects in-workout fatigue. (See Comments in

Figure 16)

Figure 16. Graphic Showing Real Time In-Workout Fatigue Detection by Performance Lab’s Coaching System.

After the Workout: Inter-workout Cardio Performance presents two values*, which align with the Two-Factor Theory9 of

performance.

Long Term Cardio Performance (slow time course) represents physical fitness, which is relatively

stable over time – it doesn’t vary over minutes or hours, but over weeks and months. (Figure 17A)

Short Term Cardio Performance (fast time course) represents fatigue, psychological overstress, and

illness, and can vary much faster. (Figure 17B)

Figure 17. Measures of Short and Long Fitness Measures for a Runner Over Several Months.

The coaching system therefore has a knowledge of the user’s physical state: (Figure 18)

See: Measuring Performance: Fitness and Fatigue Measures (Cardio Performance Module)


Figure 18. Cardio Performance Measures Over Months of Exercise Exhibiting Effective Training, Overreaching (precursor to over training), and Overtraining.

Adapting the Plan: Finally, the exercise schedule can be adapted10 in real time* based on data that is measured and

interpreted during the workout. (as shown in Figure 19)

The training schedule can also be adapted post workout based on data collected.

Figure 19. Examples of Real Time In Workout Plan Adaptions.

Technique: The Coaching system can detect technique issues within a runner or cyclist’s activity. Figure 20

shows the coaching feedback around running at the correct stride rate:


Figure 20. Graphic Showing Real Time In-Workout Running Stride Rate Monitoring and Coaching Feedback.

Figure 21 shows the Coaching system commenting on cycling cadence within a hilly ride:

Figure 21. Graphic Showing Real Time In-Workout Cycling Cadence Monitoring and Coaching Feedback.

Figure 22 shows cycling technique issues that can be detected by the Coaching system.


Figure 22. Graphic Showing Real Time In-Workout Cycling Cadence Monitoring and Coaching Feedback.

Strategy: Performance Lab’s Coaching system can even measure some aspects of an athlete’s mental

approach to their exercise. Aspects of strategy like starting the workout at too high an intensity or

pushing too hard up the hills (as shown in comments Figure 23) can also be monitored and relevant

coaching feedback provided.

Figure 23. Screenshots Showing Real Time In-Workout Strategy Commentary.


Guidance:

Start of the Workout: At the start of the workout, the plan is presented to the athlete. This entails the volume of the

workout (in time or distance) and the types of training to be conducted within the workout volume.

(Figure 24)

Figure 24. Graphic Showing Workout Viewer with the Workout Introduction Displayed.


This will also include the users exercise intensities that are automatically determined during the

athlete’s general exercise without invasive tests. (see Figure 25) See: Detecting Intensity: Automated

Training Zone Calibration Module

Heart rate, speed/pace and power can all be detected as training zones11 to be used in the plan to

guide the effort levels for each training type (shown as power in Figure 24 introduction).

Figure 25. Performance Lab’s Automatic Calibration of Training Intensity Zones; Heart Rate, Speed and Power for Running.

Intermediate Information: As the runner moves through their workout regular updates are provided on the

• current metrics: pace, heart rate, stride rate, stride length and power and

• averages so far: distance covered, elapsed time, average speed and vertical meters climb so

far. (Figure 26)

Figure 26. Examples of Intermediate Information; Workout Progress and Current Metrics Provided as Feedback.

In Zone Feedback: One of the great difficulties of training is knowing how to get the effort correct. Using Performance

Lab’s calibration module, the users training zones can be calculated*.


The Coaching system can detect when a user is nearing a higher intensity training zone with for

example, commentary of “approaching Up Tempo zone”. This gives the user feedback that they

should either slow down if doing Up Tempo training is not planned for the workout. Or it provides

information that they can ease off accelerating so they can gently move into the Up-Tempo zone

without overshooting.

Once in the zone, the guidance follows by providing the pace or heart rate as audio feedback* (e.g.

4:41, 4:42, 4:41, 4:40) so the user knows where they are in the zone and they do not have to look at

their watch or bike computer (see Figure 27, Pace comment). Every 15 seconds the elapsed time for

the rep is also provided indicating progress through the rep.

Figure 27. Examples of In Zone Guidance for Higher Intensity Training.

A similar progress update is also used for hill climbing during a workout where the number of

vertical meters ascended is provided as feedback. (Figure 28)

Figure 28. Example of Progress Guidance for Hill Climbing.


Rep Completion: Once the scheduled duration for the rep is completed, the system says, “duration completed” and

then provides a rep summary once the user has dropped out of the zone. The summary (Figure 29)

provides*:

• the rep count,

• distance covered,

• time taken for the rep,

• stride rate,

• and pace.

Figure 29. Example of a Rep Summary for Speed Training (higher intensity effort).

Each rep can be conducted at any time and the system will detect it.

The same type of summary (Figure 30) can occur for hill climb training* where the

• total number of vertical meters climbed,

• the duration of the climb,

• the stride rate (which often slows on hills),

• and the average power output.


Figure 30. Example of a Rep Summary for Hill Training.

Post Workout: The post workout summary involves (Figure 31):

• the duration of the workout,

• the distance of the workout,

• the vertical meters climbed,

• the average pace,

• the average stride rate and stride length,

• and the compliance to the plan.


Figure 31. Example of a Post Workout Summary.

See:

• Exercise Plan Builder Background/Validation

• Measuring Performance: Fitness and Fatigue Measures (Cardio Performance Module)

References

1 Performance Lab Whitepaper: 2017. Performance Lab IP Portfolio 2 Performance Lab Whitepaper: 2017. Context, Cardiovascular Delay and Comparisons 3 Performance Lab Whitepaper: 2017. Performance Lab Activity Classification IP Summary 4 Performance Lab Whitepaper: 2017. Performance Lab Data Analysis: Cardiovascular Data 5 http://www.physicalactivitycouncil.com/pdfs/current.pdf 6 https://www.washingtonpost.com/lifestyle/wellness/they-offered-to-pay-people-to-go-to-the-gym-guess-what-happened/2017/08/01/b6c58704-75fd-11e7-8f39-eeb7d3a2d304_story.html?utm_term=.79fabfce29b4 7 Performance Lab Whitepaper: 2017. Performance Lab’s AI Plan Builder User Experience 8 Performance Lab Whitepaper: 2017. ARDA Cardiovascular Performance Module 9 Zatsiorsky V. 1995. Science and Practice of Strength Training. Champaign (IL) U.S.A. Human Kinetics p. 15-18

http://www.physicalactivitycouncil.com/pdfs/current.pdf

https://www.washingtonpost.com/lifestyle/wellness/they-offered-to-pay-people-to-go-to-the-gym-guess-what-happened/2017/08/01/b6c58704-75fd-11e7-8f39-eeb7d3a2d304_story.html?utm_term=.79fabfce29b4

https://www.washingtonpost.com/lifestyle/wellness/they-offered-to-pay-people-to-go-to-the-gym-guess-what-happened/2017/08/01/b6c58704-75fd-11e7-8f39-eeb7d3a2d304_story.html?utm_term=.79fabfce29b4


10 Performance Lab Whitepaper: 2017. Performance Lab Adaptive Planner User Experience and Performance Lab Dynamic Planner Future Roadmap 11 Performance Lab Whitepaper: 2017. Performance Lab: Automated Calibration of Training Intensities

Interpreting Exercise Data: Guidance and Coaching...

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