SenseMe™ - Sensor Algorithm Library for Mobile Devices

 

SenseMe Graphics

 

SenseMe is available on both the ArcticLink 3 S2 ultra-low power sensor hub and as a software license for OEM implementation on 3rd party hardware platforms. SenseMe provides a comprehensive library of sensor fusion, activity, transport, gesture, and device location algorithms. Contact us for more detailed information about licensing.

alwayson

Smartphone and Wearable devices will make significant awareness leaps in every generation – SenseMe™ enables that through a variety of low power consumption, high-accuracy algorithms.

Devices are becoming more personally aware and meaningful to us as consumers and individuals. Accomplishing that at ultra low power consumption requires deep understanding in how to turn sensor data to meaningful information. This requires strong algorithmic expertise and a holistic system level view. 

senseme 1

Looking to the Future

While today’s smartphone and wearable devices offer basic user context and gestures such as pedometer and twist-to-wake, future generations will expand on activity, gesture, transport, and device location capabilities.  Tomorrow’s wearables will offer such capabilities as continuous HRM (Heart Rate Monitor), advanced health and wellness metrics, and smart power management depending on use cases.  Future generations of smartphones will further expand on that, allowing the smartphone to automatically recognize its location both on the user &  in the environment, then automatically make adjustments. This benefits the user, the OEM, the wireless carrier, and other parties.

QuickLogic’s SenseMe library of sensor algorithms is an ever-expanding set of activity,  gesture, transport, and device location algorithms designed to meet OEM and consumer requirements.  Through data provided by embedded sensor(s) within the device, SenseMe provides the host OS valuable information which can be used to enable new use cases, expand the user experience, monetize user data, and other things.

None of this is possible without paying specific attention to low power consumption. SenseMe is not only designed for low power algorithms, but also low power sensors. For instance, SenseMe relies on data from accelerometor sensors for many contexts and gestures, removing the requirement (and excessive power consumption) of a gyroscope.

SenseMe algorithms are designed for maximum accuracy in always-on, always-aware applications with a minimum of power consumption

OS Support

SenseMe is fully compatible with the latest Android OS and RTOS versions.

User Activity

Algorithm Description Sensor(s) Data Used Applications User Benefits Additional Benefits

senseme activity standing

Stationary

The stationary algorithm determines that while the device is ‘on person’ (in hand, in pocket, on wrist), the user is stationary (i.e., not walking, running, cycling, swimming, etc…)

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Accelerometer

  • Power management
  • Fitness
  • Health and wellness
  • Indoor navigation
  • Intelligent power management
  • Through knowing the amount of ‘stationary’ time, a more accurate view of the users activity level (and by extension health and wellness) can be had
  • By detecting that the device is stationary (but on person), the device can maintain a proper state of readiness
  • When not moving, power-intensive wireless components such as GPS can be powered down
  • Advertisers can garner user micro or macro data related to effectiveness of advertising through determination of how long users pause in the presence of material
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity walking

Walking

The walking algorithm determines that the user is walking.  Differentiated from running based on a combination of step cadence and step amplitude, the walking algorithm is part of QuickLogic’s “Enhanced Pedometer” solution.  The SenseMe Enhanced Pedometer has been OEM-tested to be >98% accurate in tests across genders, heights, weights, ages, and ethnicities.

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Accelerometer

  • Health and wellness
  • Fitness
  • Indoor navigation
  • A basic step counter is the ‘ground floor’ of determining the users fitness level
  • A differentiation of walking and running can provide a more accurate fitness level
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity running

Running

The running algorithm determines that the user is running.  Differentiated from walking based on a combination of step cadence and step amplitude, the running algorithm is part of QuickLogic’s "Enhanced Pedometer” solution.  The SenseMe Enhanced Pedometer has been OEM-tested to be >98% accurate in tests across genders, heights, weights, ages, and ethnicities.

sensor icon acc 

Accelerometer

  • Health and wellness
  • Fitness
  • Indoor navigation
  • A basic step counter is the ‘ground floor’ of determining the users fitness level
  • A differentiation of walking and running can provide a more accurate fitness level when caloric burn is calculated
  • Addition of pressure sensor data would allow a more holistic view of fitness when altitude changes are considered
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

 

senseme activity hiking

Advanced Pedometer Parameters

The advanced pedometer parameters allow the ArcticLink 3 S2 sensor hub to track the users' speed, the distance traveled, and the estimated number of calories burned.    

sensor icon acc 

Accelerometer

  • Health and wellness
  • Fitness
  • Indoor navigation
  • A basic step counter is the ‘ground floor’ of determining the users fitness level
  • A differentiation of walking and running can provide a more accurate fitness level when caloric burn is calculated
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

 

senseme activity cycling

Cycling

The cycling algorithm determines that the user is riding a bicycle.  When a magnetometer is used, this algorithm will eliminate false positives of motor-powered bicycles such as scooters and motorcycles.

sensor icon acc

Accelerometer

  • Health and wellness
  • Fitness
  • Adding cycling caloric count into a health regiment provides a more accurate view of fitness
  • Virtual coaching can be done based on average cycling time, speed, cadence, location, etc…
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity swimming

Swimming

The swimming algorithm determines that the user is swimming.

sensor icon acc

Accelerometer

  • Health and wellness
  • Fitness
  • Adding cycling caloric count into a health regiment provides a more accurate view of fitness
  • Virtual coaching can be done based on average lap time, speed, stroke cadence, etc…
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity sleeping

Sleeping

The sleeping algorithm detects that the user is asleep.

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Accelerometer

  • Health and wellness
  • Fitness
  • Provide data for duration and quality of sleep
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity stairs

Elevation Change

This algorithm detects that a user is actively changing elevations.

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Accelerometer

sensor icon pressure

Pressure

  • Health and Wellness
  • Fitness
  • Indoor Navigation
  • Caloric impact of elevation changes can be calculated in fitness levels
  • Indoor navigation requires building floor data – on stairs enables applications to understand the user's movement between floors
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme activity hrm

Heart Rate Monitor

The PPG (Photoplethysmogram-based) Heart Rate Monitor (HRM) algorithm detects the heart rate of the user using data provided by a visible and/or infrared light-sourced based HRM sensor.

sensor icon heartrate

Heart Rate

  • Health and wellness
  • Fitness
  • Accurate view of HRM can provide essential data for fitness and wellness applications
  • Tracking heart rate multiple times per day can provide information on possible health concerns
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

User Gesture

Algorithm Description Sensor(s) Data Used Applications User Benefits Additional Benefits

senseme raise hand

Raise Hand

Designed for either wrist-worn wearables or smartphones, the raise hand algorithm detects that the device has been lifted from waist level to a viewing level, allowing the device display to automatically turn on (or other OEM-enabled functions)

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Accelerometer

  • User experience
  • Power management
  • Wrist-worn wearable or smartphone display automatically turns on with user gesture
  • Device display is only powered on at the desired time, conserving battery

rotate to wake

Rotate Hand

Designed for either wrist-worn wearables, the rotate hand algorithm detects that the wrist wearing the device is twisting (similar to the movement of checking the time on a watch face) allowing the device display to automatically turn on (or other OEM-enabled functions)

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Accelerometer

  • User experience
  • Power management
  • Wrist-worn wearable or smartphone display automatically turns on with user gesture
  • Device display is only powered on at the desired time, conserving battery

tap to wake

Tap-to-wake

The Tap-to-wake algorithm (also called double tap) recognizes the unique signature of the user double tapping the face of the device 

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Accelerometer

  • User experience
  • Power Management
  • Wrist-worn wearable or smartphone display automatically turns on with user gesture
  • Device display is only powered on at the desired time, conserving battery

double tap front

Double Tap Front

The double tap front algorithm detects the user double-tapping the front of a smartphone (vs double tapping the back).  This algorithm is calibrated for each industrial design.

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Accelerometer

  • User experience
  • Double tap front can enable specific OEM- or user-set applications, features, or programs
  • System-level use cases for double tap front can be set by OEM

double tap rear

Raise and Double Tap Back

The double tap back algorithm detects the user double-tapping the back of a smartphone (vs double tapping the front).  This algorithm is calibrated for each industrial design.

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Accelerometer

  • User experience
  • Double tap front can enable specific OEM- or user-set applications, features, or programs, such as taking a ‘selfie’
  • System-level use cases for double tap front can be set by OEM

wave

Optical Gesture

Non-contact hand gestures by the user (wave left, wave right, up, down, etc…) are detected through optical means

sensor icon gesture

Gesture

  • User experience
  • Pictures can be scrolled without touching the display and leaving fingerprints
  • Remote control functions can be enabled through optical gestures, saving power by not having to turn the display 
  • Optical gestures can be used to scroll photographs, control audio/video systems remotely, or other OEM-desired functions

User Transport

Algorithm Description Sensor(s) Data Used Applications User Benefits Additional Benefits

senseme car

In Car

The In Car algorithm allows the device to determine that the user has entered a car. 

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Accelerometer

sensor icon magnet

Magnetometer

  • User experience
  • Navigation
  • Intelligent power management
  • Power down of wireless components such as Wi-Fi to save power
  • Can enable car applications on phone, such as GPS navigation and music player
  • Car context can be used to temporarily disable programs such as fitness to save power
Certain items can be enabled (Bluetooth, GPS) and others disabled (texting, browsing, hand-held phone calls) to increase driving safety

senseme bike

On Bike

The cycling algorithm determines that the user is riding a bicycle. When a magnetometer is used, this algorithm will eliminate false positives of motor-powered bicycles such as scooters and motorcycles

sensor icon acc

Accelerometer

  • Health and wellness
  • Fitness
  • Navigation
  • Adding cycling caloric count into a health regiment provides a more accurate view of fitness
  • Virtual coaching can be done based on average cycling time, speed, cadence, location, etc…
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

senseme elevator

In Elevator

This algorithm detects that a user is actively traveling in an elevator

sensor icon acc

Accelerometer

sensor icon magnet

Magnetometer

sensor icon pressure

Pressure

  • Health and Wellness
  • Fitness
  • Indoor Navigation
  • Caloric impact of taking the elevator rather than stairs can be calculated in fitness levels
  • Virtual coaching can be done to advise users on taking the stairs
  • Indoor navigation requires building floor data – on stairs enables applications to understand the movement of the user between floors
  • Health applications (and add-on’s), workout gear, and lifestyle activities can be marketed towards frequent users of these applications
  • Medical staff could view data as a supplement to regular visits and monitoring

Device Location

Algorithm Description Sensor(s) Data Used Applications User Benefits Additional Benefits

senseme noperson

Device Not on Person

This algorithm detects that the device is not ‘on person’ (i.e., on a table, not in a person's hand, pocket, on wrist, etc…)

sensor icon acc

Accelerometer

  • Intelligent power management
  • Password Protection
  • All/some wireless components and/or programs can be powered down to save power
  • Device can auto-lock when not on person for security purposes
  • Could be used to trigger data send to carrier or tethered device regarding device location

senseme accidental

Accidental Drop

This algorithm detects a free fall condition (zero G-force) of a device

sensor icon acc

Accelerometer

  • Lost device notification
  • If device falls (out of pocket, off table, etc…), the device can emit a loud noise or flashing light to alert the user that the device has fallen
  • Accidental drop could trigger an email or text with a timestamp and/or GPS coordinates 
  • Accidental drop could also trigger a device lock to ensure security

senseme angle

Horizontal Angle Detection

Detects the angle a device is being held at

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Accelerometer

sensor icon magnet

Magnetometer

sensor icon gyro

Gyroscope

  • User applications
  • Can be used to activate/deactivate programs, or interact with applications
  • Specific use cases determined by OEM

Documentation

Have a Question?

If you have questions for our solution experts, please contact us!