Patent Application
20190150827
This application claims the priority benefit of Taiwan application serial no. 106140054, filed on Nov. 20, 2017. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification.
The disclosure relates to a wearable device, and more particularly, to a wearable device capable of recognizing doze-off stage and a recognition method thereof
In the technical field for recognizing doze-off stage, the common sleep stage recognition for doze-off stage is usually conducted by utilizing a smart device with use of multiple physiological parameters such as brainwave, heartbeat, breathing or blood pressure so a doze-off stage recognition can be performed. Alternatively, a comparative recognition may be performed through an image processing to analyze variation of eye, head or mouth so a doze-off stage of a tester can be recognized. In other words, the common sleep stage recognition for the doze-off stage requires complicated accessories to be worn by the tester and requires analysis on a large amount of sense data. Consequently, the technique for recognizing the doze-off stage cannot be widely applied to various smart devices since the cost is overly high and the analysis procedure is complicated. In consideration of the above, providing a wearable device capable of effectively sensing a sleep stage of the tester in the doze-off stage having characteristics of convenience is one of important issues to be addressed in the field.
The disclosure provides a wearable device capable of recognizing doze-off stage and a method thereof, which can effectively recognize the doze-off stage of a wearer and provide characteristics of convenience.
A wearable device capable of recognizing doze-off stage of the disclosure includes a processor and an electrocardiogram sensor. The processor is configured to train a neural network module. The electrocardiogram sensor is coupled to the processor. The electrocardiogram sensor generates an electrocardiogram signal. The processor performs a heart rate variability analysis operation and a R-wave amplitude analysis operation to analyze a heart beat interval variation of the electrocardiogram signal, so as to generate a plurality of characteristic values. The processor utilizes the trained neural network module to perform a doze-off stage recognition operation according to the characteristic values, so as to obtain a doze-off stage recognition result.
A recognition method of doze-off stage is adapted to a wearable device. The wearable device includes a processor and an electrocardiogram sensor. The method includes: training a neural network module by the processor; generating an electrocardiogram signal by the electrocardiogram sensor; performing a heart rate variability analysis operation and a R-wave amplitude analysis operation by the processor to analyze a heart beat interval variation of the electrocardiogram signal, so as to generate a plurality of characteristic values; and utilizing the trained neural network module by the processor to perform a doze-off stage recognition operation according to the characteristic values, so as to obtain a doze-off stage recognition result.
Based on the above, the wearable device capable of recognizing doze-off stage and the method thereof according to the disclosure can sense a plurality of characteristic values by the electrocardiogram sensor. In this way, the processor can utilize the trained neural network module to perform the doze-off stage recognition operation according to the characteristic values. As a result, the wearable device of the disclosure can effectively obtain a recognition result of the doze-off stage and provide characteristics of convenience.
To make the above features and advantages of the disclosure more comprehensible, several embodiments accompanied with drawings are described in detail as follows.
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure.
In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details. In other instances, well-known structures and devices are schematically shown in order to simplify the drawing.
In order to make content of the disclosure more comprehensible, embodiments are provided below to describe the disclosure in detail, however, the disclosure is not limited to the provided embodiments, and the provided embodiments can be suitably combined. Moreover, elements/components/steps with same reference numerals represent same or similar parts in the drawings and embodiments.
Incidentally, the sleep stage established by polysomnography standard includes the wakefulness stage, the first non-rapid eye movement stage, a second non-rapid eye movement stage (Stage N2, a.k.a. slightly-deeper sleep stage), a third non-rapid eye movement stage (Stage N3, a.k.a. deep sleep stage) and a rapid eye movement (Stage R). Nonetheless, the wearable device 100 of the disclosure is adapted to recognize the doze-off stage when the wearer is dozing off As such, the doze-off stage to be recognized by the wearable device 100 of the disclosure with the trained neural network module only needs to include the wakefulness stage and the first non-rapid eye movement stage.
In the present embodiment, the electrocardiogram sensor 130 is configured to sense an electrocardiogram signal ECG and provide the electrocardiogram signal to the processor 110. The processor 110 analyzes the electrocardiogram signal to generate a plurality of characteristic values. In other words, when the wearer is asleep, because the wearer is less likely to turn over or move his/her body, the wearable device 100 of the present embodiment can simply recognize the doze-off stage of the wearer by sensing only the electrocardiogram information of the wearer.
In the present embodiment, the processor 110 is, for example, a central processing unit (CPU), a system on chi (SOC) or other programmable devices for general purpose or special purpose, such as a microprocessor and a digital signal processor (DSP), a programmable controller, an application specific integrated circuit (ASIC), a programmable logic device (PLD) or other similar devices or a combination of above-mentioned devices.
In the present embodiment, the storage device 120 is, for example, a dynamic random access memory (DRAM), a flash memory or a non-volatile random access memory (NVRAM). In the present embodiment, the storage device 120 is configured to store data and program modules described in each embodiment of the disclosure, which can be read and executed by the processor 110 so the wearable device 100 can realize the recognition method of sleep stage described in each embodiment of the disclosure.
In the present embodiment, the storage device 120 further includes a neural network module 121. The processor 110 can sense a plurality of characteristic values from different wearers in advance by the electrocardiogram sensor 130, and use the characteristic values from the different wearers as sample data. In the present embodiment, the processor 110 can create a prediction model according to determination conditions, algorithms and parameters from the doze-off stage, and use a plurality of the sample data for training or correcting the prediction model. Accordingly, when the doze-off stage recognition operation is performed by the wearable device 100 for the wearer, the processor 110 can utilize the trained neural network module 121 to obtain a recognition result according to the characteristic values sensed by the electrocardiogram sensor 130. Nevertheless, enough teaching, suggestion, and implementation illustration regarding algorithms and calculation modes for the trained neural network module 121 of the present embodiment may be obtained with reference to common knowledge in the related art, which is not repeated hereinafter.
In the present embodiment, the heart rate variability analysis module 320 analyzes the R-wave signals, so as to obtain a low frequency signal LF, a high frequency signal HF, a detrended fluctuation analysis signal DFA, a first sample entropy signal SE1 and a second sample entropy signal SE2 as shown in
In the present embodiment, the R-wave amplitude analysis module 330 analyzes the R-wave signals, so as to obtain a result including a turning point ratio value EDR_TPR and a signal strength value EDR_BR as shown in
In this way, the recognition method of the present embodiment can recognize the doze-off stage of the wearer according to said 9 characteristic values sensed by the electrocardiogram sensor 130. In the present embodiment, the doze-off stage recognition result is one of the wakefulness stage and the first non-rapid eye movement stage. Also, the wakefulness stage and the first non-rapid eye movement stage are established by a polysomnography standard.
In addition, sufficient teaching, suggestion, and implementation regarding detailed features of each module and each sensor in the wearable device 100 of the present embodiment the disclosure may be obtained from the foregoing embodiments of
In summary, the wearable device capable of recognizing doze-off stage and the recognition method of doze-off stage according to the disclosure can provide an accurate sleep recognition function. The wearable device includes the electrocardiogram sensor so the wearable device can sense the electrocardiogram information of the wearer. The wearable device of the disclosure can use the trained neural network module to perform the doze-off stage recognition operation according to the characteristic values provided by the electrocardiogram sensor. Moreover, the wearable device of the disclosure can integrate each of the sensors into one wearable item instead of complicated accessories. As a result, the wearable device of the disclosure is suitable for the wearer to conveniently and effectively monitor the sleep state in a home environment during the doze-off stage so as to obtain the recognition result of the doze-off stage of the wearer.
Although the present disclosure has been described with reference to the above embodiments, it will be apparent to one of ordinary skill in the art that modifications to the described embodiments may be made without departing from the spirit of the disclosure. Accordingly, the scope of the disclosure will be defined by the attached claims and not by the above detailed descriptions.
| Number | Date | Country | Kind |
|---|---|---|---|
| 106140054 | Nov 2017 | TW | national |
