I would like to say a big thank you to Element14 for hosting this challenge. I would also like to say thank you to the supporters, Analog Devices, Tektronix, Electrolube and Leeds Becket University for selecting my proposal and giving me the opportunity to take this idea further.
Project Outline
This objective of this project is to monitor an athlete to determine if any critical harm may have occurred or will have the potential to occur. The technologies that will be incorporated are as follows.
- Temperature sensor - to look for heat stroke or other temperature related issues
- 2 or 3 Accelerometers - concussion sensing using sensor fusion
- ECG front end - for potential or occurring heart attack detection
- ECG front end - Detect sudden heart rate increase that could be potentially harmful
- Wi-Fi/Bluetooth - to relay information back to a monitoring station
The exception of the system is to have a battery life of 1 ~ 2 years and to be self contained in a sports helmet (hockey, football or bicycle). If some components are not possible to incorporate directly into the helmet a chest strap may be added that will communicate wirelessly with the rest of the unit.
Deliverables
To keep the project at an achievable level the deliverables I propose will be inline with what I hope to be able to achieve in 3 months. While this list is somewhat smaller than the initial proposal, it will still deliver a decent overview of the athlete and will also provide a platform for potential future development.
- Heat stroke will be fully implemented looking for abnormal body temperatures.
- Heart attack will be implemented on a simple level; this would be only looking at abnormally high heart rate or abnormally high climb in heart rate. There is the potential to look for an abnormal heart rhythm time permitting. However, learning the athletes unique heart rhythm will most likely not be a possibility due to time constraints and potential complexity in doing such.
- Individual concussion severity will be fully implemented and have a high degree of accuracy this should use some form of filtering to ensure only actual concussions are registered and measured
- Cumulative concussion force may not be fully or correctly implemented. As it is still not fully clear in the scientific community how this should be measured, my attempts to do so may appear flimsy and illogical. While there may be an attempt at this it may be incorrect and inaccurate and therefore left out in the final product.
- Monitoring and reporting, for this the CC3100 in conjunction with a MSP430F5529 or similar will be used to post the data to a cloud based system such as Plot.ly to give real time information on the athletes sensor measurements.
Next Steps
The immediate next steps is to better understand the proposed devices to be used. This includes reading through and understanding the data sheets and mapping what each component needs in the way of power (3.3V or some other value) communication (I2C, SPI, etc.) and what its limitations are in terms of performance and usability (FIFO, interrupt driven, etc)
At this point there is no plan for any software implementation or outline as there needs to be a better understanding of the individual components and what each one needs and is capable of. There is however an intention to layout a data flow and processing diagram in the coming steps to better help the layout of the hardware and the outline of the software.
Data Sheets
For convenience I have linked the data sheets for the proposed components to be used, as more components are added their data sheets may be added below.
- ADuCM350, 16-Bit Precision, Low Power Meter On A Chip with Cortex-M3 and Connectivity (link)
- AD8232, Heart Rate Monitor Front End (link)
- ADT7320, ±0.25°C Accurate, 16-Bit Digital SPI Temperature Sensor (link)
- ADXL375, 3-Axis, ±200 g Digital MEMS Accelerometer (link)
- ADXL377, Small, Low Power, 3-Axis ±200 g Accelerometer (link)
- ADXL362, Micropower, 3-Axis, ±2 g/±4 g/±8 g Digital Output MEMS Accelerometer (link)