Technology and IP Overview *=Unique to Performance Lab

Technology and IP Overview *=Unique to Performance Lab What does Performance Lab do? Performance Lab (PL) has designed a software engine (ARDA) that makes use of available sensors, for example in a smart phone, to identify, process and manage situations that involve physical activity. Physical activity encompasses recreational exercise, professional competitive sports, management of blood pressure and diabetes. What is Performance Lab s goal? To partner with organizations that are interested in making sense of health, exercise and activity data with particular respect to management of exercise in relation to an outcome. (e.g. fitness change, blood pressure reduction, blood glucose control) Who are Performance Lab s consumers? PL has two target consumers currently: Exercise: ARDA can currently manage activities such as walking, running, cycling; the 3 largest participatory forms of exercise in the US, which involve recreational and competitive activity but also include health management. The current participation rates in the US for cycling are 47 million, running is 56 million 1 and walking is 30.4% 2 of the US population 3 which is approximately 98 million walkers. Recent exercise app acquisitions show the interest in digital exercise tracking particularly for walking, running and cycling. Adidas purchased Runtastic which has 105 million registered users 4. Underarmor purchased Mapmyfitness 5 and Endomondo 6 who have 20 million each and Asics bought Runkeeper which has 50 million 7 users. Each of these are tracking apps with no sophisticated algorithms. See: New Exercise Paradigm Closing the Loop : Consumer Experience Health Management: In terms of managing a health condition, ARDA can offer value in both management of blood pressure and blood glucose. (diabetes) High blood pressure involves 1 in 3 US adults numbering 75 million with another 75 million prehypertensives costing the nation $46 billion each year 8 and is the most common chronic medical problem resulting in 55 million primary health care visits in 2009 8. Exercise can be a powerful tool in reducing blood pressure particularly for pre-hypertensives. 9 See: System for Controlling/Preventing Hypertension 29 million Americans have diabetes 10, with another 86 million showing signs of pre-diabetes 11. Studies show that with regard to pre-diabetics, 15 to 30% will develop type 2 diabetes within the next 5 years. 10 Research shows that a structured lifestyle change program including exercise can cut the risk of developing type 2 diabetes by as much as 58%. 12 See: System for Controlling/Preventing Type 2 Diabetes 1

One of the great strengths of ARDA s algorithms is they can be used in the many exercise and activity management environments like health, weight loss, sport and exercise management. Where is the opportunity? There are 3 opportunities in the digital exercise market currently: Actions not Numbers: Fitbit provides numbers, Garmin provides numbers, Nike provides numbers. To achieve a goal, most people need more than numbers. They need a plan of action and ideally a way of seeing that they are making progress. The market currently shows a propensity to provide metrics but almost no genuine positive action towards achieving a goal. In exercise, we have devices that count steps or track heart rate and sleep. We also have sports devices that measure almost everything a user does. We have apps that track a user s running, walking or cycling. But then what? How does the user work towards their goal? The answer is obviously that without having a lot of expertise, the metrics don t help, they just pile up in your phone or devices memory and you get pretty graphs to keep you placated. ARDA can provide the most complete solution. Managing Key Health Conditions: The second opportunity involves the difficulty in managing 2 of the most prevalent medical conditions in the western world; high blood pressure and Type 2 diabetes. A dilemma experienced by the physician talking to a patient in a 15-minute appointment discussing pre-hypertension or pre-diabetes is how to convey everything that is needed to create a positive health outcome in such a short time. They might have 10mins listening to the patient and taking measurements, which leaves 5 minutes to develop the exercise plan. What if the doctor could provide an app for the user s smart phone that provides a managed prescription and highly detailed information that was sent back to the doctor on the patient s progress? ARDA has all the features required to achieve this. Data Mining: The final opportunity involves extracting more data out of certain situations where physical data is already being captured in two diverse areas; large apparel companies and entertainment: i. For large apparel companies that have mobile apps that track exercise, there a real possibility to extract a lot more information about their user s activities and behaviours for the purposes of building the best new features or marketing the right kinds of products. ii. Finally, endurance sports like the Tour de France, marathons and the Ironman triathlon are starting to make more data available on screen (TV or mobile) for users following what has already been common place in many other professional sports for years. ARDA can extract significantly more information than is currently available in these types of sports. Once again ARDA is completely suited to this kind of analysis due to the high amount of context used in classifying and interpreting situations making insights deeper and more accurate*. (Figure 1) 2

Figure 1. Graph Showing the Statistical Power of Performance Lab s Patented Data Mining Abilities (right hand 3 bars) Of these, PL has initially chosen to focus on recreational and competitive endurance exercise. What makes Performance Lab unique? Performance Lab have a number of best in class and world s first features built into an overall development and IP strategy. The modules that make up this strategy in order are: Plan Builder: PL s unique Plan Builder uses 6 questions plus the weekly schedule assistant tied to various plan building algorithms to develop a highly customised plan for a user with the flexibility to set exercise around their personal schedule. Depending on how each question is answered determines how that component of the plan is built. This means a user has access to over 270,000 personalised programs that suit them better and fit more precisely into their lives*. 13 See: A Machine That Can Write a Custom Training Program: Exercise Plan Builder Module Adaptive Planner: The Adaptive Planner is a layer over the Plan Builder where a user can add, skip, cancel or move a workout in their program and the system will automatically re-optimise the balance of the workouts and recovery to maintain the integrity and the intention of the program. There is nothing like this in market*. 14 See: Making Your Plan Fit into Life: Adaptive Planner Module Effort Calibration: Knowing what hard means to a user is very important in both exercise and health management. Everyone is different. What is intense for one user, is easy for another. Establishing a user s personalised effort is extremely important for example in situations like analysing blood pressures or controlling effort in exercise where a pre-diabetic needs to exercise often without getting too fatigued. ARDA has the only automated effort calibration system in the world that is established from general exercise*. Heart rate can be calibrated if available but we can also use the sensors in a standard smart phone to determine effort zones for power, step rate or speed. 15 ARDA can either provide precise effort levels for a user to exercise at or monitor exercise levels to ascertain how much effort the user is putting in. See: Detecting Intensity: Automated Training Zone Calibration Module 3

Power Algorithms: Performance Lab has proprietary power algorithms for both walking and running. We are the only company in the world that can accurately infer power using solely a mobile phone without the need of other sensors*. This is particularly important when measuring physiological changes like fitness, fatigue, heart rate and blood pressure response. The reason for this is that there must be an action (e.g. power) that the reaction (e.g. heart rate, heart rate variability or blood pressure) is benchmarked against. Once you have power, all kinds of medical and physiological data can be more accurately assessed because you have a benchmark. 16 See: New Metric Revolution: Running & Walking Inferred Power Module Micro-Classifications: Identification of Different Activities: Each unique aspect builds on top of the next. The previous effort calibration and power algorithms are used as part of the context that makes up our patented Micro-Classifications. ARDA tracks all the data parameters available in a smart phone or smart watch and looks for situations where all the context matches a particular activity or behaviour. If there is a match, ARDA identifies the activity and begins to log all the data specific to that activity. Once there ceases to be a match in the data context, ARDA stops the classification, packages all the data and labels the data chunk. Activity, environmental and medical data is a tangle of multiple inter relationships between each of the parameters all reacting to multiple influences at the same time. (Figure 2) It is very difficult to untangle this to access the meaning behind it. Micro-classifications achieve this so the mess of cycling data: Figure 2. The Tangle of Data Common in Sports, Fitness and Health Metrics. turns into this, (see below Figure 3) where each coloured coded block is a micro-classified cycling behaviour which is identified, chunked up with all the associated data and labelled*: 4

Figure 3. Performance Lab s Patented Training Type Sub Activity Detection for Cycling. Notice that the blocks of identified Training Type micro-classifications are shown in both of the previous 2 graphics. Comparisons Between Two Like Micro-classifications: Many health and exercise decisions require comparisons and this is where the Micro-classifications really come into their own. The secret to these data chunks is that each Micro-classification is very like other chunks of the same Micro-classification because of the rigid criteria for identification. Micro-classifications are highly granular and contain a lot of context. One Micro-classification chunk can be compared to another chunk of the same Micro-classification which is almost the same, being separated by minutes, hours, weeks, months or years. This means that very subtle variations like changes in fitness, fatigue and behaviour can be identified*. (Figure 4) Figure 4. Graphic Depicting Performance Lab s Patented the Granular Ability to Detect Subtle Changes in Physiological Behaviour. Once a user, supervisor or AI machine has greater context for an activity, they have unprecedented clarity and a highly accurate understanding of a user s situation, activity, behaviour and physiological and medical responses. 17 See: Detecting Activity: Contextual Classification Module 5

The precise comparison of a like chunk of data to another like chunk of data has three key benefits: Accurate measurement of compliance to an activity/exercise plan. Accurate measurement of physiological or medical status including differentiations not previously possible. (e.g. cardiovascular status, muscular status) A greater and more diverse collection of distinctions within an Activity. I.P. Classification System and Method: US 20130053990 A1, EP 2539837 A1 Status: Patent US 9,665,873, EU patent application Priority Date: 24 Feb 2010 Module Applicability in graphic above: (IP1, IP2, IP4 in Figure 8.) https://www.google.com/patents/us9665873 This is a very broad specification and there are many opportunities for continuances particularly around contextualising blood pressure, glucose, and ECG. Physiological Status: As each link in the chain builds a greater capacity in the system, micro classifications provide access to accurately identify a user s physiological status. Seeing fatigue and improvement in a user is another crucial piece of information in both health and exercise. Performance Lab has a proprietary system that can estimate physiological status which is pervasive in everything we do. It is now genuinely possible to see if someone is improving their fitness, if they are fatigued and identify the onset of illness whether this is during exercise or in a sedentary life situation*. There are two types of fatigue; Short and Long-term fatigue. Short Term fatigue is characterised by a drop in form on a particular day often due to effects like illness, overworking, poor sleep and stress. Long Term fatigue usually means the user s fitness has peaked having followed an upward trend for a number of weeks and now begins a slow downward trend for a number of weeks. Longer term illnesses will also become evident. 18 See: Measuring Performance: Fitness and Fatigue Measures (Cardio Performance Module) In Figure 5, we see tracking of short and long-term fatigue and tracking of improvement: 6

Figure 5. Performance Lab s Short and Long Term Cardio Performance Measures. In Figure 6, we see someone improving, falling ill and recovering. Figure 6. Performance Lab s Cardio Performance Measures Detecting Fitness Improvement and Fatigue (physiological and illness) I.P. Automated Prescription of Activity Based on Physical Activity Data: US20160263439 A1 Status: Current PCT, US & EU patent application Priority Date: 11 Oct 2014 Module Applicability in graphic above: (IP3, IP4 in Figure 8) http://www.freepatentsonline.com/y2016/0263439.htmls ARDA Workout Manager: PL has built the ARDA AI engine which is a real-time tracking/coaching system that can be embedded in a device, on a mobile phone as an app or remotely process data from a server. It can identify the different activities and behaviours of the user and provide useful timely and relevant audio or text feedback in real time or post workout. 7

The goal of ARDA is to: support the user while they are out exercising, substituting as someone who is also out there, to supply gentle coaching nudges and feedback, and to manage the plan. See: Interpreting Exercise Data: Guidance and Coaching Module ARDA detects contextualised activity and tracks compliance in real time. It can tell the user what they have left to do, provide guidance, coaching and is uniquely flexible so a user can choose when and where to conduct each scheduled type of training or activity when it suits rather than following a rigid timeline. In Figure 7, we see the effect of walking exercise is that the users blood pressures begin to reduce through the workout as vasodilation improves. Figure 7. Data Measured for a Runner Using the System Which Shows Detected Types of Activity and Guidance and Coaching Feedback Events. We are also trialling a gamification aspect to the ARDA experience. Dynamic Plan Updater: Performance Lab has recently built the final module to close the loop on measuring then actioning positive exercise and health outcomes. There is no effective management system available to do this until now. Utilising applying physiological status algorithms ARDA can interpret the strengths and weaknesses or the cause and effect relationships of a user s activity with key parameters and then automatically adapt a plan to further personalise the plan of action. 19 For sport and exercise, this can currently happen where ARDA will either: 8

Reduce or cancel the next workout Change the reps and durations of different kinds of workout activities Adjust the emphasis of the types of training scheduled for the user. See: Exercise Plan Updater Module Overall Development and IP Strategy: Everything discussed so far leads up to and is a component of the overall strategy which is to provide support and meaningful actions to accessing positive health and fitness outcomes for everyone using Performance Lab s superior algorithms. 20 See: Metric Review: Comparing ARDA to 1. TrainingPeaks, 2. Firstbeat. Here is what completes the story. All meaningful analysis and actioning of purposeful outcomes in health and fitness requires 3 questions to be answered: What should I do? (a recommendation to illicit an action or behaviour) What did I do? (a knowledge of the activity engaged in by the user) How did I do? (an interpretation based on analysis the users contextualised activity.) This follows a cyclic format, the last question, how you do flows back into the first question, what you should do so the 3 questions are a feedback loop. Our focus is to protect this loop. (Figure 8) Figure 8. Performance Lab s IP and Development Strategy Solving the Most Important Questions to Guided Activity; What Should I do? What Did I Do? How Did I Do? The graphic above shows the 9

core software modules we have built that run the IP stated below, which of the 3 crucial questions applies to which module, and the relationship of our IP to the modules. To summarise: Because we can automatically identify a user s personal effort, And because we can benchmark their physiological data against power, And because we can identify what a user is doing, We can see how key physiological variables (e.g. BP) react to different situations And we can assess what their current physiological status is, And because we have a plan builder (and adaptive planner) And can use the micro-classifications to determine compliance to a plan We can use a dynamic planner to adapt the plan specifically to the user Which closes the loop on actioning a positive health and exercise outcome plan, measure, change! Instead of numbers, you get meaningful change that is the difference! Does it work? To manually test the value of our training plans, in 2002, PL conducted an in-house study for a large corporation involving 550 employees engaging in training for a Half Marathon. The participants were mainly sedentary. All the exercise was unsupervised. We achieved an 89% completion rate for the training up to the event as opposed to the industry average of 10% and 100% completed the Half Marathon. The project continued for 5 years with as many as 1300 people training for each event in multiple countries. A random sample from the study were physiologically post tested for a number of health factors. Unfortunately, blood pressure was not tracked but we present the data to demonstrate the fact that the programs offer positive, real health outcomes. The average change in aerobic fitness (VO2max) was 22% (from 38.07 to 46.63ml/kg/min). Cholesterol dropped by 4.9% (4.45 to 4.23mmol/l), average body weight dropped by 4.7% (84.18 to 80.2kg) and heart rate after 3mins of controlled exercise dropped by 11%. (100 to 89b/min) In 2016, PL conducted another smaller trial, this time to investigate whether an unsupervised prototype ARDA app would support our methodology. The prototype was not optimised for compliance or UX. In the 2016 trial, participants using the Training Programs got consistently good gains (5-15%), implying 22% gains for sedentary users, 15% for moderately fit users and 5% for very fit users. Training without a program gave consistently bad gains (0-5%) Regarding churn, we had a 60% completion rate of the programs using the ARDA app unsupervised. This is a significantly higher than the compliance statistics in unsupervised scenarios, which are estimated to be no higher than 20%. ARDA guided training programs resulted in a 300% higher completion rate than unsupervised programs. 10

In the post project 2002 survey, 86% said the overall experience was very good to excellent. 87% reported being more motivated after the event. 82% said they had higher energy levels through the training, 86% felt healthier, 90% felt fitter and 93% wanted to try something similar again. 92% believed the experience had a positive impact on your health and finally 62% were surprised with what they had achieved. In the 2016 trial, 86% believed ARDA had either a moderate or major impact on them completing their program and 72% said they would want to use an AI coaching product for future training, after using ARDA. Beginner and recreational trial participants reported that they felt lonely at the end of the trial without the ARDA app prototype. Who is interested? Currently we have Oakley, Intel, Mio, Lifebeam and Amazon using our technology and IP. Figure 9. Performance Lab s Current Integration Partners. What is Performance Lab s mission? To help improve every person s long-term health, fitness and performance by providing easy access to intelligent exercise. Approximately 87% of people who exercise have minimal guidance on how to exercise effectively. See: New Exercise Paradigm Closing the Loop : Consumer Experience, System for Controlling/Preventing 1. Hypertension, 2. Type 2 Diabetes, Making Sense of Team Sport Data: Introduction References: 1 http://www.outdoorfoundation.org/pdf/researchparticipation2014.pdf 2 https://www.bls.gov/opub/ted/2016/sports-and-exercise-among-americans.htm 3 https://www.census.gov/quickfacts/table/pst045216/00 4 https://www.runtastic.com/en/career/facts-about-runtastic 5 http://about.mapmyfitness.com/2013/11/underarmour/ 6 https://blog.endomondo.com/endomondo-joins-under-armour-continues-operating-as-endomondo/ 7 https://www.theatlantic.com/technology/archive/2015/11/the-technology-that-created-a-new-generationof-runners/417126/ 8 Centre for Chronic Disease Prevention. "High Blood Pressure Facts." Centers for Disease Control and Prevention. Centers for Disease Control and Prevention, 30 Nov. 2016. Web. 30 May 2017. <https://www.cdc.gov/bloodpressure/facts.htm>. 9 Pescatello, L. S., Fargo, A. E., Leach, C. N., & Scherzer, H. H. (1991). Short-term effect of dynamic exercise on arterial blood pressure. Circulation, 83(5), 1557-1561. and Stamler J, Stamler R, Neaton JD. Blood pressure, systolic and diastolic, and cardiovascular risks. Arch Intern Med. 1993;153: 598 615. 11

10 Centers for Disease Control and Prevention. At a Glance 2016: Diabetes. 2016. Web. 31 May 2017. <https://www.cdc.gov/chronicdisease/resources/publications/aag/pdf/2016/diabetes-aag.pdf>. 11 Centers for Disease Control and Prevention. About Prediabetes & Type 2 Diabetes. 26 May 2017. Web. 31 May 2017. < https://www.cdc.gov/diabetes/prevention/prediabetes-type2/index.html>. 12 Knowler, W. C., Barrett-Connor, E., Fowler, S. E., Hamman, R. F., Lachin, J. M., Walker, E. A., & Nathan, D. M. (2003). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. Scandinavian Journal of Medicine & Science in Sports, 13(3), 208. 13 Performance Lab Whitepaper: Performance Lab s AI Plan Builder User Experience 14 Performance Lab Whitepaper: Performance Lab Adaptive Planner User Experience 15 Performance Lab Whitepaper: Performance Lab: The World s First Automated Calibration of Training Intensities 16 Performance Lab Whitepapers: Performance Lab Inferred Running and Walking Power 17 Performance Lab Whitepapers: Context, Cardiovascular Delay and Comparisons 18 Performance Lab Whitepapers: The World s first Day to Day Cardiovascular Performance Measurement Module 19 Performance Lab Whitepaper: Performance Lab Dynamic Planner Future Roadmap 20 Performance Lab Whitepapers: Metric Review: TrainingPeaks and Metric Review: Firstbeat VO2max and VO2max Dependent Metrics 12