from Bosch Sensortec is a case in point. The BHI160BP
sensor employs an algorithm for pedestrian dead reckoning
(PDR) to calculate the user’s relative location based on data
collected from the inertial sensor. Then it re-calibrates itself
every few minutes to obtain the absolute position provided
by the GNSS/GPS module.
In other words, the GNSS/GPS module, which can rapid-
ly drain a device’s battery capacity, is kept in sleep mode for
most of the time. That allows users to navigate reliably and
extends GPS tracking in wearable devices from several hours
up to several days.
Another MEMS sensor from Bosch, the BMA400, claims
to draw 10 times less current than existing accelerometers
due to intelligent features like built-in activity recognition.
It wakes up automatically only when it detects motion
and goes back to sleep mode when the motion stops. The
BMA400 accelerometer consumes 14 μA at the highest per-
formance and falls to 1 μA and below in the ultra-low-power
self-wake-up mode.
This acceleration sensor for wearable devices handles
continuous measurement by using precisely defined cut-off
frequencies. That, in turn, makes motion sensors resistant
to vibrations, so wearable devices can distinguish between
real alarm situations such as broken glass and false signals
coming from random noise and vibrations.Wearable sensors meet AI
What really makes wearables smart is artificial intelligence
(AI), and that’s partly enabled by MEMS sensors featuring
built-in local intelligence, which comes in the form of algo-
rithms and software frameworks. These software solutions
combined with hardware accelerators incorporate deep
learning and other AI features to contextualize individual
behaviors and surroundings. They independently process
sensor data, and that also reduces power consumption, low-
ers cost, and boosts overall efficiency.For example, the BMA400 accelerometer from Bosch
mentioned in the above section integrates the motion classi-
fication functions. Bosch also offers smart sensor hubs, such
as the BHI260 and BHA260 devices, which integrate MEMS
sensors with a microcontroller for low-power sensor data
processing and data buffering alongside a software frame-
work that includes sensor fusion.
These low-power solutions incorporate data from
multiple sensors and enable pre-processing for always-on
execution. They facilitate features such as 3D orientation,
step counting, position tracking, activity recognition, pose-
and-head tracking, and context awareness in wrist-mounted
products, hearables, eyewear, and other wearable devices.
Not surprisingly, therefore, software assets are becoming vi-
tal in the arsenal of MEMS sensor suppliers. It’s the algorithms
and software frameworks that allow MEMS sensors to offer an
accurate live classification of AI data and enable features like
activity tracking without utilizing precious processor resources.
It’s also a practical manifestation of the edge computing that
has mostly been discussed in the context of IoT designs.Summary
Wearable designers are building a whole new industry one
gadget at a time, and MEMS sensors are an intrinsic part of
this design movement. Wearable designs have come a long
way from counting steps in fitness trackers, and they are
already applying machine-learning algorithms to classify
and analyze data.
New MEMS sensors are enabling this without requiring
an external microcontroller, which cuts cost, lowers power
usage, and reduces design complexity. Next, the symbiotic
relationship between MEMS sensors and AI is expected to
take wearable devices into a whole new space. ☐Sensor makers are now employing innovative
new techniques to further lower the power draw
from always-on sensors without performance and
accuracy trade-offs. A new crop of solutions combines
MEMS sensors with algorithms and firmware that
intelligently process, synthesize, and calibrate the
output of sensors.Established in 1990
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10 FEATURE MEMS & Sensors for Wearables
JANUARY 2019 • electronicproducts.com • ELECTRONIC PRODUCTS