In my Droidcon 2011 presentation I tried to highlight the battery cost of the continuous sensor sampling which is necessary for detecting motion patterns. While the general case still requires some sort of improvement over the current Android sensor architecture e.g. the use of the "wake on motion" feature of the acceleration sensors, it is possible to decrease the battery consumption if the motion to be detected is longer than 5-10 seconds. This is still not suitable for recognising very short events like fall, tap, shake, etc. but can be suitable to "wake up" a step counter when the motion starts. Some steps would be missed but this may be acceptable if the battery life is increased.
Click here to download the example program.
The essence of the workaround is to use the AlarmManager and to generate periodic wakeup events. When the wakeup event is received, sensor sampling is started and some samples are collected. Then we have to figure out using some heuristics whether there is some movement. This implementation calculates the average value of the acceleration sample absolute values and looks for values deviating from the average. If there is movement detected, the sampling continues. If there is no movement detected, the program gives up the wake lock and goes back sleeping.-
I have made some measurements using my Nexus 1. I switched the phone into airplane mode and ran the program during the night, for 7-8 hours. The battery percentage consumed during an hour can be seen below as a function of wakeup period (check out WAKEUP_PERIOD constant in AlarmSleep.java).
5 sec - 1.14%/hour
10 sec - 1.10%/hour
20 sec - 0.56%/hour
30 sec - 0.27%/hour
The battery consumption with the last timeout value - 30 sec - is very close to the standby consumption, 0.25%. If you can tolerate that long "dead period", then you can bring the battery consumption in "no motion" state very close to the phone's normal standby consumption. For a general "tap" or "shake" detector, however, this is not an adequate solution. I have received encouraging mails that a proper solution relying on the sensor's low-power mode may be deployed soon.
Saturday, October 22, 2011
Tuesday, October 11, 2011
Foreca Weather released
Let me advertise an application that has just appeared on Android Market.
Why? Because I was the Android programmer on this project working in cooperation with the designers of Foreca, a Finland-based weather forecast company. As you may remember, I am aesthetically challenged so the good looks of this application are all due to the thorough polishing of the Foreca guys. Foreca's vast weather network makes this application handy anywhere in the world. Weather stations can be selected by incrementally searching list or from a map, the application even finds the current location. Locations can be favourited and Foreca offers 10 days of detailed forecast. There is a widget too.
Click on the button and go to Android Market to fetch it!
Some screenshots.
Why? Because I was the Android programmer on this project working in cooperation with the designers of Foreca, a Finland-based weather forecast company. As you may remember, I am aesthetically challenged so the good looks of this application are all due to the thorough polishing of the Foreca guys. Foreca's vast weather network makes this application handy anywhere in the world. Weather stations can be selected by incrementally searching list or from a map, the application even finds the current location. Locations can be favourited and Foreca offers 10 days of detailed forecast. There is a widget too.
Click on the button and go to Android Market to fetch it!
Some screenshots.
Monday, October 10, 2011
Battery cost of sensor sampling
While at Droidcon UK 2011, I was asked to elaborate my claims about the battery cost of sensor sampling in a blog post. These claims can be found in my conference presentation but we thought it would help to describe them more in detail.
Continuous accelerometer sensor sampling introduces significant battery cost in Android devices. For example if you want to write an application that samples the sensor in the background and figures out, whether somebody double-tapped the body of the phone (not the active touch screen but anywhere on the phone's body), then the CPU of the phone can never sleep. You need to grab a partial wake lock to ensure continuous sampling otherwise the processing of the samples will stop when the device goes to sleep - typically some minutes after the keyguard activates. If you obtain partial wake lock, however, then you have to calculate with 1.5-4% battery consumption per hour (depending on sampling speed) which does not look like a lot but if you multiply it with 24 hours, you can see that you cannot sample the accelerometer continuously without spoiling the phone's usability.
Microsoft proposes a low-power co-processor for these background processing jobs with low computational complexity (accelerometer is typically sampled around 10-30 samples per second - you don't need a supercomputer to do that kind of processing). While this approach definitely solves the battery problem, there is the issue of an additional programming model (those low-power microcontrollers don't have full-blown programming environments) and it is very likely that application programmers will not be able to insert pieces of code to run on this microcontroller.
My proposal is to exploit low-power features of the accelerometer sensors widely used in Android devices. For example the very popular Bosch Sensortec BMA150 accelerometer sensor which can be found in variety of HTC devices (and probably others) has a wake up on motion mode.
In its data sheet, this mode is described like the following.
In general BMA150 is attributed to low power applications and can contribute to the system power management.
The BMA150 provides the possibility to wake up a system master when specific acceleration values are detected. Therefore the BMA150 stays in an ultra low power mode and periodically evaluates the acceleration data with respect to interrupt criteria defined by the user. An interrupt output can be generated and trigger the system master. The wake-up mode is used for ultra-low power applications where inertial factors can be an indicator to change the activity mode of the system.
This would allow the main CPU to go into sleep mode and to be woken up by the sensor only if there are movements. So if the device is laying on the table, there would be basically no power consumption due to sensor sampling. This would enable production-quality implementation of a range of applications, for example the Activity Level Estimator which is being researched at the University of Geneva.
The attractive property of this approach is that even though implementing it in Android devices and in the framework is not trivial, it is not very complicated either. The hardware is already in the devices, maybe the sensor's interrupt pin has to be wired up with the main processor. SensorManager needs to be extended with some functions that allows applications to activate this wake up on motion feature. Application model would remain consistent with the current Android application model, no need to fiddle with low-level microcontroller code.
Now there just need to be a device manufacturer that carries this through.
Continuous accelerometer sensor sampling introduces significant battery cost in Android devices. For example if you want to write an application that samples the sensor in the background and figures out, whether somebody double-tapped the body of the phone (not the active touch screen but anywhere on the phone's body), then the CPU of the phone can never sleep. You need to grab a partial wake lock to ensure continuous sampling otherwise the processing of the samples will stop when the device goes to sleep - typically some minutes after the keyguard activates. If you obtain partial wake lock, however, then you have to calculate with 1.5-4% battery consumption per hour (depending on sampling speed) which does not look like a lot but if you multiply it with 24 hours, you can see that you cannot sample the accelerometer continuously without spoiling the phone's usability.
Microsoft proposes a low-power co-processor for these background processing jobs with low computational complexity (accelerometer is typically sampled around 10-30 samples per second - you don't need a supercomputer to do that kind of processing). While this approach definitely solves the battery problem, there is the issue of an additional programming model (those low-power microcontrollers don't have full-blown programming environments) and it is very likely that application programmers will not be able to insert pieces of code to run on this microcontroller.
My proposal is to exploit low-power features of the accelerometer sensors widely used in Android devices. For example the very popular Bosch Sensortec BMA150 accelerometer sensor which can be found in variety of HTC devices (and probably others) has a wake up on motion mode.
In its data sheet, this mode is described like the following.
In general BMA150 is attributed to low power applications and can contribute to the system power management.
- Current consumption 200μA operational
- Current consumption 1μA sleep mode
- Wake-up time 1ms
- Start-up time 3ms
- Data ready indicator to reduce unnecessary interface communication
- Wake-up mode to trigger a system wake-up (interrupt output when motion detected Low current consumption in wake-up mode to master)
The BMA150 provides the possibility to wake up a system master when specific acceleration values are detected. Therefore the BMA150 stays in an ultra low power mode and periodically evaluates the acceleration data with respect to interrupt criteria defined by the user. An interrupt output can be generated and trigger the system master. The wake-up mode is used for ultra-low power applications where inertial factors can be an indicator to change the activity mode of the system.
This would allow the main CPU to go into sleep mode and to be woken up by the sensor only if there are movements. So if the device is laying on the table, there would be basically no power consumption due to sensor sampling. This would enable production-quality implementation of a range of applications, for example the Activity Level Estimator which is being researched at the University of Geneva.
The attractive property of this approach is that even though implementing it in Android devices and in the framework is not trivial, it is not very complicated either. The hardware is already in the devices, maybe the sensor's interrupt pin has to be wired up with the main processor. SensorManager needs to be extended with some functions that allows applications to activate this wake up on motion feature. Application model would remain consistent with the current Android application model, no need to fiddle with low-level microcontroller code.
Now there just need to be a device manufacturer that carries this through.
Friday, October 7, 2011
My presentation about motion recognition at Droidcon 2011
I finished on time!
Per popular demand, here is the presentation.
Per popular demand, here is the presentation.
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