摘要:
A toothbrushing monitoring device for a user includes a sensor coupled with the toothbrush for measuring toothbrushing patterns, a recording device coupled with the sensor, and a comparator. The recording device records data relating to the user's preferred toothbrushing patterns in a first mode, which is used to establish toothbrushing pattern reference data. The comparator compares toothbrushing patterns measured by the sensor and the toothbrushing pattern reference data, which may be output to an output device. In one example, the sensor consists of only an accelerometer.
摘要:
A toothbrushing monitoring device for a user includes a sensor coupled with the toothbrush for measuring toothbrushing patterns, a recording device coupled with the sensor, and a comparator. The recording device records data relating to the user's preferred toothbrushing patterns in a first mode, which is used to establish toothbrushing pattern reference data. The comparator compares toothbrushing patterns measured by the sensor and the toothbrushing pattern reference data, which may be output to an output device. In one example, the sensor consists of only an accelerometer.
摘要:
The present invention describes a method to calibrate an angular measurement device using acceleration measurement device. The angular rate measurement device includes at least one angular measurement sensor, wherein the acceleration measurement device is able to distinguish the direction and strength of gravity. By performing the calibration method the scale factor(s) of the angular measurement device and the strength and direction of gravity can be obtained and/or corrected. An embodiment according to the method of the present invention describes optional use of a communication network to perform necessary evaluation and calculation steps at a remote device.
摘要:
Various embodiments relate to a context recognition. Classification of a context is performed by using features received from at least one sensor of a client device, and model parameters being defined by a training data to output a result and a likelihood of the context. The result is shown to the user, who provides feedback regarding the result. The features, result, likelihood, and the feedback are stored, whereby the model parameters are adapted using the features, result, likelihood and the feedback to obtain adapted model parameters. The result, likelihood and the feedback can also be used for performing confidence estimation to obtain a confidence value. The confidence value can then be used for performing an action, e.g. adding a new sensor, adding a new feature, changing a device profile, launching an application.