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Collecting Activity Data Using the Open Mhealth Platform an Exploratory Study on Integrating Objective Data with Sport Monitoring Systems

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Collecting Activity Data Using the Open Mhealth Platform an Exploratory Study on Integrating Objective Data with Sport Monitoring Systems Collecting activity data using the Open mHealth platform An exploratory study on integrating objective data with sport monitoring systems Daniel Gynnild-Johnsen , Lars-Erik Holte Master’s Thesis Spring 2017 Collecting activity data using the Open mHealth platform Daniel Gynnild-Johnsen Lars-Erik Holte May 2, 2017 Abstract Football players works together as a unit to perform on an elite competi- tive level, and the most minor abnormalities can determine the outcome of a match. Success can often be the result of healthy, uninjured and rejuve- nated players working together as a collective. Even though it is impossible to control all outcomes and scenarios, the risk of failure might be mini- mized by monitoring players closely on an individual level. If we monitor players over a longer period of time we might discover patterns or abnor- malities in their training. This information can be used to avoid multiple scenarios related to fatigue, injuries and overtraining. In this thesis we present a proof of concept for expanding an existing self- reporting monitoring system called pmSys, and look at how football teams and players can utilize modern technology like phones and wearable de- vices to capture objective data. This system will collect and store the data, which can be processed into useful visualised feedback, and help a team to evaluate their players. This way the coaches can make mitigating mea- sures to improve certain aspect that might be lacking on a player or team level. By eliminating the use of pen and paper, pmSys introduces a simpler way of reporting the players’ health status. By expanding the system with objective data, the team performance can effortlessly be evaluated and ad- justments made if needed. Compared to the subjective data, which are a player’s assessment of themselves and their health, objective data is not as simple to tamper with and rarely lies, and can more accurately determine a player’s performance state. We believe this monitoring system can help giving a team the advantage to succeed. ii Contents 1 Introduction 1 1.1 Background . .1 1.2 Problem definition . .2 1.3 Limitations . .2 1.4 Research methods . .3 1.5 Main contributions . .3 1.6 Outline . .4 2 Related Work 6 2.1 GPS . .6 2.2 Wearables . .7 2.2.1 Accelerometer . .8 2.2.2 Gyroscope . .8 2.2.3 Altimeter . .8 2.3 Mobile health . .8 2.4 Ohmage . .9 2.5 Open mHealth . 10 2.6 Docker . 11 2.6.1 Composing a docker container . 12 2.7 MongoDB . 13 2.8 OAuth 2 . 14 2.9 Third-party data sources . 16 2.10 Athlete Monitoring . 18 2.11 Summary . 19 3 Shimmer 20 3.1 Shimmer components . 20 3.1.1 Shims . 21 3.1.2 Resource server . 21 3.1.3 Console . 21 3.2 Schema . 23 3.2.1 Design principles . 24 3.3 Grant Type: Authorization Code . 26 3.4 Installation . 28 3.5 GPS in Shimmer . 28 3.6 Summary . 30 iv 4 Runkeeper and Health Graph 32 4.1 Runkeeper . 32 4.1.1 Runkeeper application . 32 4.1.2 Wearable hardware . 33 4.1.3 GPS accuracy . 33 4.2 Health Graph . 34 4.2.1 API . 34 4.2.2 Health Graph console . 35 4.2.3 Limitations . 36 4.3 Summary . 36 5 PmSys 38 5.1 Current pmSys . 38 5.1.1 PmSys Mobile Application . 38 5.1.2 PmSys-trainer . 39 5.2 PmSys with objective data . 40 5.2.1 Motivation . 40 5.2.2 Injecting shimmer into the backend . 40 5.3 Summary . 41 6 Testing 42 6.1 Using Shimmer . 42 6.2 Using bash script . 43 6.3 Summary . 44 7 Proof of concept 46 7.1 System requirements . 46 7.1.1 Functional requirements . 46 7.1.2 User stories . 47 7.1.3 Non-functional requirements . 47 7.2 Working with Shimmer . 49 7.2.1 GPS schema . 49 7.2.2 GPS data point mapper . 52 7.3 Connect pmSys user to Runkeeper . 53 7.4 Fetch data from Runkeeper . 58 7.4.1 Scheduling requests . 64 7.5 Database storage . 66 7.5.1 Replication . 66 7.5.2 Concurrency in MongoDB . 68 7.5.3 Big data . 71 7.5.4 Database optimization . 74 7.6 Summary . 75 8 Conclusion 77 8.1 Summary . 77 8.2 Main Contributions . 77 8.3 Future work . 78 8.3.1 PmSys frontend . 79 v 8.3.2 PmSys backend . 79 8.3.3 Aggregation of data . 81 Appendix A Accessing the source code 85 vi List of Figures 2.1 Wearable forecast . .7 2.2 Ohmage system architecture . .9 2.3 Open mHealth architecture . 11 2.4 Difference between Docker and VM . 12 2.5 MongoDB data collection . 14 2.6 JSON team sample . 14 2.7 OAuth 2.0 Protocol flow . 15 2.8 Worldwide smartphone OS market share . 18 3.1 Shim workflow architecture . 20 3.2 Adding client id and secret in the console . 22 3.3 List of added APIs in the console . 23 3.4 JSON schema sample . 26 3.5 OAuth access token example . 27 3.6 Grant type: Authorization code flow . 28 3.7 JSON raw data . 29 3.8 JSON shimmed data . 30 4.1 Actions during activity recording . 33 4.2 JSON team sample . 35 4.3 Open Health developer console . 36 5.1 RPE scale . 38 5.2 PmSys-app flow . 39 5.3 PmSys backend . 39 5.4 Isolated pmSys backend with shimmer . 41 7.1 Calories burned sample data . 50 7.2 Shimmed GPS data from Runkeeper . 52 7.3 Application registration in Runkeeper . 54 7.4 Logical model of the authorization table . 54 7.5 Service data collection . 55 7.6 Service authorization data collection . 55 7.7 Sequence diagram: Connect to Runkeeper . 56 7.8 Connect mockup . 57 7.9 Logical model of the activity table . 58 7.10 Activity data collection . 59 7.11 Sequence diagram: Fetch daily data . 64 7.12 Modified JSON response . 67 viii 7.13 Database deadlock . 69 7.14 Concurrency flowchart . 70 7.15 Horizontal database sharding . 71 7.16 Big data . 72 7.17 Distributed database . 73 ix List of Tables 2.1 Currently supported shims and requestable measures . 16 2.2 Supported APIs and requirements . 17 3.1 Examples of Unified Code for Units of Measure . 25 6.1 Shimmer non-detailed test results . 43 6.2 Shimmer detailed test results . 43 6.3 Bash script non-detailed test results . 44 6.4 Bash script detailed test results . 44 7.1 User stories . 47 7.2 Crontab entry parameters . 65 7.3 Crontab keywords . ..
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