TECHNICAL FIELD

The present disclosure relates to the field of interactive mode of a smart device, in particular to an interactive method and an interactive system based on a smart watch.

BACKGROUND

At present, the market of wearable smart devices is booming, among which the smart watches are especially prevalent. However, the smart watch is worn on the wrist and cannot be assembled with a large enough screen. Thus, unlike on mobile phone, the user fails to input words on smart watches. The existing smart watches mainly include three types of input modes: single-touch screen, finger tracking and speech recognition. The methods of single-touch screen and finger tracking are limited by the screen, while speech recognition is more limited by information sensitivity. In order to overcome the difficulty in inputting, many scientific research teams have also conducted researches. Nonetheless, the additional equipment is generally required, which is not widely accepted due to the cost of the additional equipment and the cost of learning.

SUMMARY

In order to overcome the above-mentioned technical problems, the present disclosure provides an interactive method and an interactive system for a smart watch based on a vibration signal transmitted by a human body. In the premise of conforming to the usage habit of the user, the new interactive modes for smart watches are developed to solve the problem lacking in the interactive means of the smart watch. The technical solutions of the present disclosure are as follows:

An interactive method of a smart watch, including the following steps:

S1. acquiring a vibration signal of an accelerometer and a gyroscope of a smart watch based on a vibration signal transmitted by a human body;

S2. identifying the vibration signal by using an anomaly detection algorithm;

S3. pre-processing the vibration signal, and further classifying and identifying the vibration signal by using an improved algorithm of a k-nearest neighbor algorithm;

S4. analyzing the user's feedback on the classification and identification results and correcting the results in time to maintain a stable identification accuracy.

Further, the vibration signals of X, Y, and Z axes of the accelerometer and the gyroscope are separately collected.

Further, the step S2of identifying the signal by using the anomaly detection algorithm includes the following steps:

S21. collecting data of a Z axis of the accelerometer;

S22. filtering the data of the Z axis of the accelerometer by using a high-pass filter;

S23. setting a threshold of an effective tapping signal and a threshold of a noise signal;

S24. reading a signal having a smaller amplitude than the threshold of the noise signal as a first state;

S25. continue monitoring, waiting to read a signal having a larger amplitude than the threshold of the effective tapping signal, setting a position of the signal having the larger amplitude than the threshold of the effective tapping signal to be X, and setting a starting position of the signal to be X-L, namely, a position at a length L before the position X;

S26. continue monitoring, waiting to read a continuous signal having a smaller amplitude than the threshold of the noise signal, and setting an end position of the signal as a current position when the continuous amplitude is smaller than the threshold of the noise signal;

S27. obtaining signal through the starting position and the end position of the signal, judging whether a length of the signal is within the length range, and if not, returning to the step S25, otherwise proceeding to the step S28;

S28. performing a high-pass filtering on the signal, calculating energy of first m points and energy of points after a mthpoint of the signal after filtering respectively, judging whether the amplitude of the signal is greater than a threshold of a signal-to-noise ratio, if yes, determining the signal as a valid signal, otherwise determining the signal as a noise signal, and returning to the step S25.

Further, the step S3includes the following steps:

S31. pre-processing the signal by normalization, subtracting a mean value from the signal and dividing the signal by a variance;

S32. in the initialization training model phase, storing the signal processed in the step S31as a training sample in a database; in the actual use phase, classifying and identifying the signal by using the improved algorithm based on the k-nearest neighbor algorithm.

Further, the improved algorithm of the k-nearest neighbor algorithm is as follows: based on a dynamic time warping algorithm, matching an actual signal and a training signal in units of frame, and calculating a shortest Manhattan distance between the actual signal and the training signal, and using the shortest Manhattan distance as a basis for the k-nearest neighbor algorithm for classification and identification.

Further, the step S4includes the following steps:

S41. after collecting the classification result obtained in the step S32, correcting an input result of the user;

S42. after correction, correcting the training sample to a certain extent, so as to keep the accuracy stable.

Further, in the step S41, the classification and identification result in the step32is corrected by providing a candidate key or by a correlation result in an input method.

Further, the step S42specifically includes the following steps:

S421. when the correction result is identical to the classification result, performing no operation;

S422. when the correction result is not identical to the classification result, for the training sample having the same category as the classification result, deleting a sample having a largest distance calculated by the improved algorithm of the k-nearest neighbor algorithm, and replacing the deleted sample with a corrected training sample.

An interactive system of a smart watch, including:

a signal detection module, acquiring a vibration signal of an accelerometer and a gyroscope of a smart watch based on a vibration signal transmitted by a human body;

an identification and classification module, configured to identify the vibration signal by using the anomaly detection algorithm to pre-process the vibration signal, and further classify and identify the vibration signal by using the improved k-nearest neighbor algorithm to;

a real-time feedback module, analyzing the user's feedback on the results and correct the results in time to keep an identification accuracy stable.

A program, performing the interactive method of the smart watch of the present disclosure.

Compared with the prior art, the advantages of the present disclosure are as follows: in the present disclosure, the human body part such as the back of the hand is regarded as a virtual screen based on the vibration signal transmitted by the human body, and the improved machine learning algorithm is combined to actually expand the interactive means of the watch and to improve the user experience. The interactive means of the present disclosure is novel and interesting, can effectively meet the needs of users, and can be widely applied to the text input, the games in watches and the like.

