New Development Tool for Bluetooth 5

Nordic Semiconductor’s Bluetooth 5 developer solution for its nRF52840 SoC comprises the Nordic S140 v5.0 multi-role, concurrent protocol stack that brings Bluetooth 5’s long range and high throughput modes for immediate use to developers on the Nordic nRF52840 SoC. The Nordic nRF5 SDK offers application examples that implement this new long-range, high-throughput functionality. The existing Nordic nRF52832 SoC is also complemented with a Bluetooth 5 protocol stack.NordicBluetooth5Board

Bluetooth 5’s high throughput mode offers not only new use cases for wearables and other applications, but also significantly improves user experience with Bluetooth products. Time on air is reduced and thus leads to faster more robust communication as well as reduced overall power consumption. In addition, with 2 Mbps, the prospect of audio over Bluetooth low energy is possible.

The new Preview Development Kit (nRF52840-PDK) is a versatile, single-board development tool for Bluetooth 5, Bluetooth low energy, ANT, 802.15.4m, and 2.4-GHz proprietary applications using the nRF52840 SoC. The kit is hardware compatible with the Arduino Uno Revision 3 standard, making it possible to use third-party-compatible shields. An NFC antenna can be connected to enable NFC tag functionality. The kit gives access to all I/O and interfaces via connectors and has four LEDs and four buttons which are user-programmable.

Source: Nordic Semiconductor

New Scalable Biometric Sensor Platform for Wearables and the IoT

Valencell and STMicroelectronics recently launched a new development kit for biometric wearables. Featuring STMicro’s compact SensorTile turnkey multi-sensor module and Valencell’s Benchmark biometric sensor system, the platform offers designers a scalable solution for designers building biometric hearables and wearables.

The SensorTile IoT module’s specs and features:

  • 13.5 mm × 13.5 mm
  • STM32L4 microcontroller
  • Bluetooth Low Energy chipset
  • a wide spectrum of MEMS sensors (accelerometer, gyroscope, magnetometer, pressure, and temperature sensor)
  • Digital MEMS microphone

Valencell’s Benchmark sensor system’s specs and features:

  • PerformTek processor communicates with host processor using a simple UART or I2C interface protocol
  • Acquires heart rate, VO2, and calorie data
  • Standard flex connector interface

Source: Valencell

Mini Multi-Sensor Module for Wearables & IoT Designs

STMicroelectronics’s miniature SensorTile sensor board of its type comprises an MEMS accelerometer, gyroscope, magnetometer, pressure sensor, and a MEMS microphone. With the on-board low-power STM32L4 microcontroller, the SensorTile can be used as a sensing and connectivity hub for developing products ranging from wearables to Internet of Things (IoT) devices.

The 13.5 mm × 13.5 mm SensorTile features a Bluetooth Low-Energy (BLE) transceiver including an onboard miniature single-chip balun, as well as a broad set of system interfaces that support use as a sensor-fusion hub or as a platform for firmware development. You can plug it into a host board. At power-up, it immediately starts streaming inertial, audio, and environmental data to STMicro’s BlueMS free smartphone app.

Software development is simple with an API based on the STM32Cube Hardware Abstraction Layer and middleware components, including the STM32 Open Development Environment. It’s fully compatible with the Open Software eXpansion Libraries (Open.MEMS, Open.RF, and Open.AUDIO), as well as numerous third-party embedded sensing and voice-processing projects. Example programs are available (e.g., software for position sensing, activity recognition, and low-power voice communication).

The complete kit includes a cradle board, which carries the 13.5 mm × 13.5 mm SensorTile core system in standalone or hub mode and can be used as a reference design. This compact yet fully loaded board contains a humidity and temperature sensor, a micro-SD card socket, as well as a lithium-polymer battery (LiPo) charger. The pack also contains a LiPo rechargeable battery and a plastic case that provides a convenient housing for the cradle, SensorTile, and battery combination.

SensorTile kit’s main features, specs, and benefits:

  • Cradle/expansion board with an analog audio output, a micro-USB connector, and an Arduino-like interface that can be plugged into any STM32 Nucleo board to expand developers’ options for system and software development.
  • Programming cable
  • LSM6DSM 3-D accelerometer and 3-D gyroscope
  • LSM303AGR 3-D magnetometer and 3-D accelerometer
  • LPS22HB pressure sensor/barometer
  • MP34DT04 digital MEMS microphone
  • STM32L476 microcontroller
  • BlueNRG-MS network processor with integrated 2.4-GHz radio

Source: STMicroelectronics

Ultra-Small hSensor Platform for Wearable Apps

Maxim Integrated Products’s ultra-small hSensor Platform enables you to quickly develop wearable fitness and wellness-related prototypes. With it, you have all the necessary hardware on one PCB along with readily-accessible hardware functionality with the ARM mbed hardware development kit (HDK).Maxim health sensor

The hSensor Platform (MAXREFDES100# reference design) comprises an hSensor board that comes complete firmware with drivers, a debugger board, and a graphical user interface (GUI). The platform enables you to load algorithms for different applications.

The hSensor Platform includes the following: a MAX30003 ultra-low power, single-channel integrated biopotential AFE; a MAX30101 high-sensitivity pulse oximeter and heart-rate sensor; a MAX30205 clinical-grade temperature sensor; a MAX32620 ultra-low power ARM Cortex-M4F microcontroller optimized for wearables; a MAX14720 power management integrated circuit (PMIC); inertial sensors (three-axis accelerometer, six-axis accelerometer/gyroscope); a barometric pressure sensor; flash memory; and a Bluetooth Low Energy (BLE) radio.

The MAXREFDES100# costs $150. Hardware and firmware files are free.

Source: Maxim Integrated Products

The Future of Ultra-Low Power Signal Processing

One of my favorite quotes comes from the IEEE Signal Processing magazine in 2010. They attempted to answer the question: What does ultra-low power consumption mean? And they came to the conclusion that it is where the “power source lasts longer than the useful life of the product.”[1] It’s a great answer because it’s scalable. It applies equally to signal processing circuitry inside an embedded IoT device that can never be accessed or recharged and to signal processing inside a car where the petrol for the engine dominates the operating lifetime, not the signal processing power. It also describes exactly what a lot of science fiction has always envisioned: no changing or recharging of batteries, which people forget to do or never have enough batteries for. Rather, we have devices that simply always work.Figure 1

My research focuses on healthcare applications and creating “wearable algorithms”—that is, signal processing implementations that fit within the very small power budgets available in wearable devices. Historically, this focused on data reduction to save power. It’s well known that wireless data transmission is very power intensive. By using some power to reduce the amount of data that has to be sent, it’s possible to save lots of power in the wireless transmission stage and so to increase the overall battery lifetime.

This argument has been known for a long time. There are papers dating back to at least the 1990s based on it. It’s also readily achievable. Inevitably, it depends on the precise situation, but we showed in 2014 that the power consumption of a wireless sensor node could be brought down to the level of a node without a wireless transmitter (one that uses local flash memory) using easily available, easy-to-use, off-the-shelf-devices.[2]

This essay appears in Circuit Cellar 316, November 2016. Subscribe to Circuit Cellar to read project articles, essays, interviews, and tutorials every month!

Today, there are many additional benefits that are being enabled by the emerging use of ultra-low power signal processing embedded in the wearable itself, and these new applications are driving the research challenges: increased device functionality; minimized system latency; reliable, robust operation over unreliable wireless links; reduction in the amount of data to be analyzed offline; better quality recordings (e.g., with motion artifact removal to prevent signal saturations); new closed-loop recording—stimulation devices; and real-time data redaction for privacy, ensuring personal data never leaves the wearable.

It’s these last two that are the focus for my research now. They’re really important for enabling new “bioelectronic” medical devices which apply electrical stimulation as an alternative to classical pharmacological treatments. These “bioelectronics” will be fully data-driven, analyzing physiological measurements in real-time and using this to decide when to optimally trigger an intervention. Doing such as analysis on a wearable sensor node though requires ultra-low power signal processing that has all of the feature extraction and signal classification operating within a power budget of a few 100 µW or less.

To achieve this, most works do not use any specific software platform. Instead they achieve very low-power consumption by using only dedicated and highly customized hardware circuits. While there are many different approaches to realizing low-power fully custom electronics, for the hardware, the design trends are reasonably established: very low supply voltages, typically in the 0.5 to 1 V range; highly simplified circuit architectures, where a small reduction in processing accuracy leads to substantial power savings; and the use of extensive analogue processing in the very lowest power consumption circuits.[3]

Less well established are the signal processing functions for ultra-low power. Focusing on feature extractions, our 2015 review highlighted that the majority (more than half) of wearable algorithms created to date are based upon frequency information, with wavelet transforms being particularly popular.[4] This indicates a potential over-reliance on time–frequency decompositions as the best algorithmic starting points. It seems unlikely that time–frequency decompositions would provide the best, or even suitable, feature extraction across all signal types and all potential applications. There is a clear opportunity for creating wearable algorithms that are based on other feature extraction methods, such as the fractal dimension or Empirical Mode Decomposition.

Investigating this requires studying the three-way trade-off between algorithm performance (e.g., correct detections), algorithm cost (e.g., false detections), and power consumption. We know how to design signal processing algorithms, and we know how to design ultra-low power circuitry. However, combining the two opens many new degrees of freedom in the design space, and there are many opportunities and work to do in mapping feature extractions and classifiers into sub-1-V power supply dedicated hardware.

[1] G. Frantz, et al, “Ultra-low power signal processing,” IEEE Signal Processing Magazine, vol. 27, no. 2, 2010.
[2] S. A. Imtiaz, A. J. Casson, and E. Rodriguez-Villegas, “Compression in Wearable Sensor Nodes,” IEEE Transactions on Biomedical Engineering, vol. 61, no. 4, 2014.
[3] A. J. Casson, et al, “Wearable Algorithms,” in E. Sazonov and M. R. Neuman (eds.), Wearable Sensors, Elsevier, 2014.
[4] A. J. Casson, “Opportunities and Challenges for Ultra Low Power Signal Processing in Wearable Healthcare,” 23rd European Signal Processing Conference, Nice, 2015.

Alex Casson is a lecturer in the Sensing, Imaging, and Signal Processing Department at the University of Manchester. His research focuses on creating next-generation human body sensors, developing both the required hardware and software. Dr. Casson earned an undergraduate degree at the University of Oxford and a PhD from Imperial College London.

Compact NFC Security Module for Smart Wearables

Infineon Technologies is collaborating with Beijing-based Mobile Payment Solutions Co. Ltd. (MPS) on a new plug-and-play NFC security module. The smallest module in the series measures only 4 mm × 4 mm, making it a good fit for wearable electronics.Infineon_NFC

The MPS Boosted NFC security module series is well suited for wearable applications. At the core of the module is Infineon’s Boosted NFC Secure Element, which eliminates the need for the separate NFC controller that’s typically required with conventional solutions to utilize card emulation functionality in a device. The NFC antenna and antenna-matching components are included in the package, which reduces the PCB footprint by more than 75% percent (when you are using the smallest module of the series).

Running on a standard Java security card operating system, the Boosted NFC security module allows for the flexible loading of multiple Java-based applications (applets) on smart devices. While the Boosted NFC security module is an excellent option for new product designs, you could easily integrate it into existing designs to extend functionality to include secure payment.

The NFC security module’s main component is Infineon’s SLE78 security chip, which combines highest security performance with a storage capacity of more than 1 MB. This provides sufficient memory to securely store user credentials and run multiple applications, enabling a single device to replace a variety of cards (e.g., payment cards and public transportation tickets).

Source: Infineon Technologies

Heart Rate Monitoring Sensor Solution

Silicon Labs recently announced an optical heart rate-sensing solution for wrist-based heart rate monitoring (HRM) applications. The new Si1144 HRM solution includes a low-power optical sensor module paired with an EFM32 Gecko microcontroller running Silicon Labs’s advanced HRM algorithm. The compact Si1144 sensor module integrates an optical sensor, green LED, an ADC, LED drivers, control logic, and an I2C digital interface, making it a good option for power-sensitive wearables.Silicon Labs Si1144



The Si1144-AAGX HRM module’s features:

  • Accurately senses weak blood flow signals on the wrist
  • Choice of two algorithms to support static HRM and optional dynamic motion-compensated HRM using data from an external accelerometer
  • HRM solution pairs optical module with a Pearl Gecko microcontroller
  • Fully integrated HRM IC with green LED lens, high-sensitivity photodiode, low-noise ADC, LED drivers, optical blocking, and host communications/interrupts
  • Two LED drivers
  • Ultra-low-power consumption for long battery life (less than 500 nA standby current with 1.71-to-3.6-V supply voltage)
  • I2C serial communications, up to 3.4-Mbps data rate
  • 10-lead 4.9 × 2.85 × 1.2 mm LGA module package

Samples and production quantities of the Si1144-AAGX HRM module currently available. The Si1144 module along with Silicon Labs’s HRM algorithm costs $2.82 in 10,000-unit quantities. The HRM44-GGG-PS development board costs $57.60.

Source: Silicon Labs

Bluetooth Smart SoCs Links Wearables to Apps for WeChat

Dialog Semiconductor recently announced its support for WeChat’s communications protocol with the launch of its WeChat SDK. With the kit, you can quickly add Bluetooth connectivity between WeChat apps and wearables and other IoT devices. Dialog DA14580 Dialog’s development kit is available now and includes a protocol stack for the WeChat communication layer. The SDK—which is based on the DA1458x family of SmartBond SoCs—enables you to reduce the overall development time for connecting their products wirelessly to WeChat apps. Your users can control wearable devices via the app and share information via the platform.

DA1458x SoCs combine a Bluetooth low-energy radio with an ARM Cortex-M0 application processor. With intelligent power management circuitry and accessible processor resources via 32 GPIOs,you can build fully hosted applications.

The SmartBond WeChat SDK enables efficient coding and comes with SmartSnippets software development environment, which is based on Keil µVision tools.

Source: Dialog Semiconductor

Consumer Interest in Wearables Increases

New consumer research from Futuresource Consulting highlights a significant increase in consumers’ intentions to purchase wearable devices. Interviewing more than 8,000 people in May and and October in the US, the UK, France, and Germany, the study saw interest in fitness trackers and smart watches rise by 50% and 125%, respectively. However, interest in smart glasses and heart rate monitors has stalled.

Source: Futuresource

Source: Futuresource

The overall wearables market has seen significant growth so far in 2014, with Futuresource forecasting full-year sales of over 51 million units worldwide. However, it’s only just warming up, and wearables sales are expected to accelerate from 2015 as new brands enter the space.

The most marked change since May is the strong growth in the number of iPhone owners intending to purchase wearable devices. iPhone owners now lead the way in all categories – particularly in smartwatches, which 17% of iPhone owners expressed an intent to purchase in the next 12 months, up from only 6% in May 2014. This increase coincides with September’s announcement of the Apple Watch. As Apple customers are typically some of the earliest adopters of new technologies, their increasing engagement with the smartwatch category is a strong positive for the Apple Watch release in early 2015.

Source: Futuresource Consulting

Embedded Bluetooth Modules for the Internet of Things

ASIX Electronics Corp. has launched five AXB series embedded Bluetooth modules, the AXB031/AXB033 for Internet of Things applications and the AXB051/AXB052/AXB081 for wireless audio applications. You can connect the AXB modules to any MCU with UART interface, or you can operate it as a standalone unit without an MCU.ASIX-BT_Modules

The Bluetooth 4.0 AXB031 and AXB033 modules are well suited for wearable applications, such as medical sensors and activity monitors, as well as commercial/industrial automation and smart home applications.

According to the company’s release, ASIX offers “developers a full-featured Bluetooth Smart stacks and application development environment to make it easy to add Bluetooth Smart to embedded system. In addition, ASIX also provides a dual-mode Bluetooth 4.0 audio module, AXB081, and two Bluetooth 3.0 audio modules, AXB051/AXB052, for the fast-growing wireless audio applications, such as wireless stereo speakers, headphones, home theater, automotive hands-free, and MirrorLink car player applications.”

Source: ASIX