The Systems Approach to Biometric Wearables
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Many people think you can create accurate biometric wearable devices by piecing together some LED’s, some firmware, some algorithms, and calling it a heart rate sensor. It’s not that simple. Valencell has learned over many years of R&D, trial & error, and product development that the systems approach to building biometric wearable devices is most effective. Everything must be designed and developed to work together from the start – the optomechnanics, the firmware, the algorithms, and the testing & validation processes.
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This document discusses lessons learned from over 40 product development cycles for wearable biometrics. Key challenges in building accurate wearables include optical noise, skin tone variations, sensor location, and the "crossover problem". User experience is critical, as wearables need to help users accomplish goals through design, accuracy, comfort and more. Hardware considerations like sensor size, spacing, and geometry impact signal quality. Software using active signal characterization can provide motion-tolerant heart rate sensing. Extensive product testing is needed to validate multiple use cases and biometrics. Choosing the right manufacturer with relevant experience is also important for success.
How consumer wearable tech is driving innovation in health and medical devices
This document discusses how consumer wearable technology is driving innovation in health and medical devices. It notes that optical sensing, like PPG, has improved to the point where wearables can accurately measure biometrics. While consumer fitness trackers have issues with accuracy, studies show wearables can detect conditions like atrial fibrillation. The document argues successful medical wearables will require understanding both consumer and clinical markets and performing rigorous clinical validation of sensor accuracy and use cases before seeking FDA approval.
Best Practices in Testing Biometric Wearables
Wearables and hearables that measure biometric signals like heart rate are different from other devices, because they have to interact with the human body and every human body is different. This makes testing and validation of the devices an important part of the product development process.
Valencell operates one of the most experienced testing labs for biometric wearables and hearables, testing hundreds of devices over thousands of hours of testing every year.
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The Systems Approach to Biometric Wearables
- 1. The Systems Approach to Wearable Biometrics © 2017 Valencell, Inc Ryan Kraudel September 2017
- 2. © 2017 Valencell, Inc The Systems Approach Physical / Hardware Physiological Optical Sensors/AFE Mechanical Design Software Production User Experience Firmware Algorithms Assessments User Interface User testing Production testing IntellectualProperty
- 3. © 2017 Valencell, Inc Physiological & Optical
- 4. © 2017 Valencell, Inc Mechanical Design & Sensors/AFE Epidermis Dermis Subcutaneous Emitter Detector Close (2 mm) source-detector spacing Wide (4 mm) source-detector spacing Emitter Detector S/N 0.05 S/N 0.25 Wider spacing captures less total light, but it captures a much higher ratio of “good light” (blood-flow scatter) to “bad light” (motion- related scatter). Epidermis Dermis Subcutaneous Other considerations: • Geometry of emitters & detectors • Light guiding • Wavelengths • AFE configuration • Electrical noise • Coupling • Gross displacement
- 5. © 2017 Valencell, Inc Firmware & Algorithms • Firmware manages the process from the timing of LED firing and signal extraction to the biometric data output that feeds the user experience. • Active Signal Characterization - optical signal from blood flow, skin motion, body motion, & environmental noise (such as sunlight) needs to be actively characterized in real-time. • Fitness levels • Training load • Stress levels • Overhearing / hydration • Sleep metrics • Core temperature • Diet planning
- 6. © 2017 Valencell, Inc User Testing: Activity-Specific Testing Protocols
- 7. © 2017 Valencell, Inc •Testing protocols that match the use cases: resting, lifestyle activities, mild exercise, aggressive exercise, interval training, etc. •Minimum 30 participants of multiple physical habitus, gender, & skin tone per prototype variant – hundreds of data sets per product launch •Biometric analysis that includes regression analysis (R2) & Bland-Altman analysis •Analysis that includes true positives, false positives, true negatives, false negatives, & total positives & negatives •Ideally, there is independent validation of each metric User Testing: Should be Broad and Deep
- 8. ©2016 Valencell. Inc User Experience is Critical – Start With the End in Mind
- 9. © 2017 Valencell, Inc User Experience is Critical – Start With the End in Mind • What are your users trying to accomplish and how does your device help them do it? • How does UX drive customer expectations for design, accuracy, battery life, comfort, ease of use, social community, etc? • Fitness levels • Training load • Stress levels • Overhearing / hydration • Sleep metrics • Core temperature • Diet planning
- 10. © 2017 Valencell, Inc Production Testing: Picking Your Manufacturer Carefully • Has your CM built a biometric wearable before? • Have they built a Bluetooth wearable before? • How do they test devices coming off the line? • What are labor policies and working conditions?
- 11. ©2016 Valencell. Inc Intellectual Property Landscape Source: http://www.ipwatchdog.com/2016/08/24/iot-ip-landscape-fitness- wearables/id=72137/
- 12. © 2017 Valencell, Inc The Whole > Sum of the Parts Physical / Hardware Physiological Optical Sensors/AFE Mechanical Design Software Production User Experience Firmware Algorithms Assessments User Interface User testing Production testing IntellectualProperty
- 14. ©2016 Valencell. Inc Ryan Kraudel VP, Marketing kraudel@valencell.com Twitter: @kraudel www.valencell.com