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Sensor combinations enable smarter mobile devices

28 Apr 2015  | Ernst Haselsteiner

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The last hardware piece required in the smart notification system is an ambient temperature sensor. A temperature-sensitive component such as a PT100 resistor can, with signal processing, enable the mobile device to determine the temperature in the surrounding air. Its position on the circuit board should be chosen to minimise the influence of heat generated by the mobile phone itself. The absolute accuracy of the measurement is not important: its function is simply to indicate whether the device is close to the user's body.

Now these various inputs can be combined to produce a unified view of the environment that the mobile phone is in at the time when a notification must be provided to the user (figure 1). In other words, the various measurements must all feed into one algorithm which makes a choice from a number of possible environments, each with its own set of characteristics.

Figure 1: The current environment can be built up from various types of sensor input.

How to combine information from different sensors
Referring to the "ringing phone left on the desk" scenario at the start of this article, the volume sensor alone would have indicated that the device was in a quiet environment; a smart algorithm could then lower the volume of the audible notification. In addition, the information from the front and back proximity sensors in combination would indicate that the phone was almost certainly lying on a desk or other flat surface. The phone could use this information to make the decision to disable vibration, which is not necessary when the device is not on the user's body.

As this example shows, a combination of sensor inputs enables the device to sense its environment accurately and to make an intelligent decision about the notification style appropriate to that environment.

Extending this principle of operation, a number of typical scenarios could be defined, and for each scenario a profile could be configured. This profile would provide an instruction to the mobile device on the way it should notify the user. For example:

 • The profile "in the user's pocket" would prompt the device to vibrate
 • "In a bag, in motion" would prompt the use of a high-volume alert
 • "Lying on a desk in a quiet room" would reduce the volume of the alert
The flow chart in figure 2 outlines a decision tree which would use the information from multiple sensors to distinguish between seven profiles. A series of accelerometer measurements may be used to decide whether the mobile device is in motion or not. The temperature sensor is used to judge whether the device is close to the user's body. The ambient light sensor can distinguish between indoors and outdoors (a value of ≥2,000 lux indicates that the device is outdoors), or determine whether the device is in a dark environment such as a bag or pocket.

Figure 2: This decision tree is for determining the most appropriate notification profile.

User configuration and self-learning capabilities
A smartphone's capability is such that the decision tree can be made configurable by the user. In practice, of course, this could create some complexity, and it might be that few users would in practice modify the default decision tree.

But a self-learning algorithm could certainly enhance the user experience. The default state of the algorithm contains a simple decision tree and a pre-defined set of notification profiles. But these rules may be automatically modified in response to user inputs and behaviour.

For instance, if the user tends to switch the phone to vibrate-only in particular scenarios, the device can automatically add this modification to the standard profile. By doing so, over time the user will get the impression that the device is anticipating and understanding the behaviour. Of course, this adaptation must be implemented cautiously to avoid misinterpreting a small number of random events.

A smartphone is already equipped with a rich array of sensors that are aware of the environment around the device. But they operate in an isolated fashion today. By combining their measurements, the phone can be made to sense complete scenarios that a single sensor on its own cannot perceive. Combined with a decision flow implemented in soft-ware, the sensor combination in the phone can actually mimic human behaviour, which uses multiple sensor inputs to make smart responses to the environment we live in.

The result is a more natural, comfortable and enjoyable experience for the user – and for all those people who share a living or working environment with the user.

About the author
Ernst Haselsteiner is with ams AG.

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