WO2018107755A1 - 一种用户行为监测方法和可穿戴设备 - Google Patents

一种用户行为监测方法和可穿戴设备 Download PDF

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Publication number
WO2018107755A1
WO2018107755A1 PCT/CN2017/093882 CN2017093882W WO2018107755A1 WO 2018107755 A1 WO2018107755 A1 WO 2018107755A1 CN 2017093882 W CN2017093882 W CN 2017093882W WO 2018107755 A1 WO2018107755 A1 WO 2018107755A1
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Prior art keywords
user
real
motion data
time
axis
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English (en)
French (fr)
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李艳
赵大川
赵昕
刘莞尔
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Goertek Inc
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Goertek Inc
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/0423—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting deviation from an expected pattern of behaviour or schedule
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0407—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis
    • G08B21/043—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons based on behaviour analysis detecting an emergency event, e.g. a fall
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0446—Sensor means for detecting worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
    • G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
    • G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
    • G08B29/18—Prevention or correction of operating errors
    • G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B3/00—Audible signalling systems, e.g. audible personal calling systems
    • G08B3/10—Audible signalling systems, e.g. audible personal calling systems using electric transmission; using electromagnetic transmission
    • G08B3/1008—Personal calling arrangements or devices, i.e. paging systems
    • G08B3/1016—Personal calling arrangements or devices, i.e. paging systems using wireless transmission
    • G08B3/1025—Paging receivers with audible signalling details
    • G08B3/1033—Paging receivers with audible signalling details with voice message alert
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/38—Transceivers, i.e. devices in which transmitter and receiver form a structural unit and in which at least one part is used for functions of transmitting and receiving
    • H04B1/3827—Portable transceivers
    • H04B1/385—Transceivers carried on the body, e.g. in helmets

Definitions

  • the present invention relates to the field of wearable smart devices, and in particular, to a user behavior monitoring method and a wearable device.
  • existing wearable devices generally monitor user behavior by extracting features from big data information of normal behaviors (such as walking and running) of a similar group of people, and training a unified classification model.
  • the behavior of a classification model is judged as abnormal behavior, such as abnormal behavior such as falling and falling.
  • abnormal behavior such as falling and falling.
  • individual differences of each person are not considered, and since the instantaneous characteristic information of individual normal behaviors (such as running and going down the stairs) is similar to the characteristic information of abnormal behavior, it is easy to cause false positives.
  • the present invention has been made in view of the above problems, and provides a user behavior monitoring method and a wearable device to solve the above problems or at least partially solve the above problems.
  • a user behavior monitoring method including:
  • the inertial sensor monitors, collects the historical motion data of the user in the preset statistical period; and obtains the prediction index according to the trend of the historical exercise data;
  • real-time motion data of the user is collected, and the user predicts whether the user has an abnormal behavior according to the real-time motion data, the prediction index obtained in the data index acquisition stage, and the preset policy;
  • An alarm notification is sent when it is determined that the user has an abnormal behavior.
  • the preset statistical period is composed of multiple sub-cycles
  • Collecting historical motion data of the user in the preset statistical period includes: collecting motion data in each sub-period of the preset statistical period;
  • Obtaining the prediction index according to the trend of the historical motion data includes: obtaining the prediction index in the current sub-period according to the trend of the motion data in the plurality of sub-cycles;
  • Collecting real-time motion data of the user includes: collecting real-time motion data in the current sub-period.
  • each sub-period is composed of a plurality of time intervals
  • obtaining the prediction indicators in the current sub-period includes:
  • Collecting real-time motion data in the current sub-period includes collecting real-time motion data in a specified time interval in the current sub-period.
  • determining whether the user has abnormal behavior according to the real-time motion data, the prediction indicator obtained in the data indicator acquisition stage, and the preset policy include:
  • the real-time motion data is compared with the prediction index, and when the real-time motion data exceeds a predetermined range of the prediction index, and the related parameters of the real-time motion data and/or the real-time motion data meet a predetermined condition, it is determined that the user has an abnormal behavior.
  • the inertial sensor comprises: an accelerometer for collecting acceleration of the user in the x-axis, y-axis and/or z-axis directions;
  • Obtaining prediction indicators according to the trend of historical motion data includes: obtaining accelerations in the x-axis, y-axis, and/or z-axis directions according to the trend of acceleration in the x-axis, y-axis, and/or z-axis directions in a predetermined statistical period. Predicted maximum, predicted minimum, and/or predicted average;
  • the prediction indicators obtained in the data indicator acquisition stage, and the preset strategy, determining whether the user has abnormal behavior includes:
  • the real-time speed of the user is calculated according to the acceleration of the user in real time monitoring in the x-axis, y-axis and/or z-axis directions;
  • the direction of the acceleration in the z-axis direction monitored in real time changes from the positive direction in the z-axis direction to the negative direction in the z-axis direction, and the user
  • the real-time speed becomes 0 and maintains a predetermined time, it is determined that the user has a fall behavior
  • the direction of the gravity vector is the z-axis direction
  • the forward direction of the user is the x-axis direction
  • the y-axis and the x-axis and the z-axis constitute a right-hand coordinate system, and the right-hand coordinate system changes with the user's motion.
  • the inertial sensor further includes: a gyroscope for collecting a rotational angular velocity of the user about the x-axis direction, the y-axis direction, and/or the z-axis direction;
  • the prediction indicators obtained in the data indicator acquisition stage, and the preset strategy, determining whether the user has abnormal behavior includes:
  • the real-time speed of the user is calculated according to the acceleration of the user in real time monitoring in the x-axis, y-axis and/or z-axis directions;
  • the direction of the acceleration in the z-axis direction monitored in real time changes from the positive direction in the z-axis direction to the negative direction in the z-axis direction, the user's When the real-time speed becomes 0 and maintains the predetermined time, and the user's real-time tilt angle exceeds the predetermined angle, it is determined that the user has a fall behavior.
  • the method further includes: setting a barometer in the wearable device; and monitoring the height of the user in real time through the barometer after the user wears the wearable device;
  • determining whether the user has an abnormal behavior includes:
  • the preset statistical period is composed of consecutive N sub-cycles before the current sub-period; wherein N is a positive integer greater than 1.
  • the method further includes deleting historical motion data collected before consecutive N sub-cycles when the earliest acquisition time corresponding to the historical motion data is not within consecutive N sub-cycles.
  • a wearable device comprising: an inertial sensor and a microprocessor;
  • the inertial sensor is configured to collect historical motion data of the user in a preset statistical period after the user wears the wearable device, and collect real-time motion data of the user;
  • the microprocessor is connected to the inertial sensor, and is configured to obtain a prediction index according to a trend of the historical motion data; and to determine whether the user has an abnormal behavior according to the real-time motion data, the prediction index obtained in the data indicator acquisition stage, and the preset policy. When an abnormal behavior is determined by the user, an alarm notification is sent.
  • an alarm circuit is further disposed in the wearable device; the alarm circuit includes: an audio codec And speakers;
  • the microprocessor is connected to the alarm circuit for controlling the sound of the speaker through the audio codec.
  • an emergency call circuit is further disposed in the wearable device;
  • the emergency call circuit includes: a radio frequency transceiver, a radio frequency front end module, and an RF antenna;
  • the microprocessor is coupled to the emergency call circuit for receiving or transmitting radio frequency signals through the emergency call circuit.
  • the inertial sensor includes an accelerometer for acquiring acceleration of the user in the x-axis, y-axis, and/or z-axis directions, or the inertial sensor includes an accelerometer and is used to collect the user about the x-axis direction, the y-axis direction, and a gyroscope with a rotational angular velocity in the z-axis direction;
  • the microprocessor is coupled to the accelerometer for processing acceleration in the x-axis, y-axis, and/or z-axis directions acquired by the accelerometer; the microprocessor is coupled to the gyroscope and is also used to process the x-axis direction of the gyroscope acquisition, y Rotational angular velocity in the axial direction and/or the z-axis direction;
  • the wearable device is also provided with a barometer for monitoring the height of the user;
  • the microprocessor is connected with the barometer, and is also used for processing the height data collected by the barometer;
  • the direction of the gravity vector is the z-axis direction
  • the forward direction of the user is the x-axis direction
  • the y-axis and the x-axis and the z-axis constitute a right-hand coordinate system, and the right-hand coordinate system changes with the user's motion.
  • the technical solution provided by the present invention monitors the user's motion data through the wearable device.
  • the motion data collected by the wearable device in the previous preset statistical period is used as historical motion data.
  • the motion data collected by the wearable device in real time at the current time is used as real-time motion data, and the predicted index is obtained according to the change rule of the historical motion data, and the abnormal behavior of the user is determined according to the real-time motion data, the prediction index, and the preset strategy, and is determined to be Alarms are implemented to monitor the behavior of users wearing wearable devices.
  • the program can use the historical motion data of each user as the template data of self-learning for different users, and obtain the predictive index of the user's post-motion through continuous learning of the template data, combined with the theoretical predictive index and the current actual collected real-time motion.
  • the data can analyze and discover the abnormal behavior of the user, and realize customized and high-precision behavior monitoring.
  • FIG. 1 is a flow chart showing a method for monitoring user behavior according to an embodiment of the present invention
  • 2A is a diagram showing a trend of changes in acceleration in the x-axis direction in a preset statistical period according to an embodiment of the present invention
  • 2B is a graph showing a change trend of acceleration in the y-axis direction in a preset statistical period according to an embodiment of the present invention
  • 2C is a graph showing a change trend of acceleration in the z-axis direction in a preset statistical period according to an embodiment of the present invention
  • 2D is a diagram showing a trend of changes in the speed of a user within a preset statistical period in accordance with one implementation of the present invention
  • FIG. 3 shows a schematic diagram of a wearable device in accordance with one embodiment of the present invention
  • FIG. 4 shows a schematic diagram of a wearable device in accordance with another embodiment of the present invention.
  • FIG. 5 illustrates a flow chart of a wearable device monitoring user behavior in accordance with one embodiment of the present invention.
  • FIG. 1 shows a flow chart of a method for monitoring user behavior according to an embodiment of the present invention. As shown in Figure 1, the method includes:
  • Step S110 setting an inertial sensor in the wearable device.
  • Step S120 At the beginning of each data index acquisition phase, when the user wears the wearable device, the inertial sensor monitors, collects the historical motion data of the user in the preset statistical period; and obtains the prediction index according to the trend of the historical exercise data.
  • Step S130 During real-time monitoring, collect real-time motion data of the user, and determine whether the user has abnormal behavior according to the real-time motion data, the prediction index obtained in the data index acquisition stage, and the preset policy.
  • step S140 when it is determined that the user has an abnormal behavior, an alarm notification is sent.
  • the process of collecting historical motion data in step S120 and the process of acquiring real-time motion data in step S130 are all implemented based on the function of inertial sensor monitoring motion data in the wearable device, and the collection time and processing of historical motion data. There is a certain time difference between the time, and there is almost no time difference between the acquisition time of the real-time motion data and the processing time.
  • the historical motion data in step S120 refers to the motion data collected in the preset statistical period before the current time.
  • the real-time motion data in step S130 refers to the motion data collected at the current time, and the current real-time motion data may be used as the subsequent historical motion data. Therefore, the data index acquisition phase in step S120 and the real-time monitoring phase in step S130 are not divided according to the execution timing. However, according to the content of the implementation, the data indicator acquisition phase and the real-time monitoring phase can be performed simultaneously.
  • the method shown in FIG. 1 monitors the user's motion data through the wearable device, for the current time.
  • the motion data collected by the wearable device in the previous preset statistical period is used as the historical motion data
  • the motion data collected by the wearable device in real time at the current time is used as the real-time motion data, according to the change of the historical motion data.
  • the program can use the historical motion data of each user as the template data of self-learning for different users, and obtain the predictive index of the user's post-motion through continuous learning of the template data, combined with the theoretical predictive index and the current actual collected real-time motion.
  • the data can analyze and discover the abnormal behavior of the user, and realize customized and high-precision behavior monitoring.
  • the preset statistical period is composed of a plurality of sub-cycles;
  • the step S120 of collecting historical motion data of the user in the preset statistical period includes: collecting motion data in each sub-period of the preset statistical period;
  • the obtaining the prediction index according to the change trend of the historical motion data in the preset statistical period includes: obtaining the prediction index in the current sub-period according to the change trend of the motion data in the plurality of sub-cycles; and the step S130 is to collect the real-time of the user.
  • the motion data includes: collecting real-time motion data in the current sub-period.
  • the preset statistical period is one week
  • the preset statistical period is composed of 7 sub-cycles, and each sub-period is one day.
  • the scheme takes the user's motion data for the first 7 days as Historical sports data, based on the trend of the first 7 days of sports data to obtain the forecast indicators of today's sports, according to the real-time motion data collected today, the predicted indicators of today's sports acquired and the default strategy to determine whether the user has abnormal behavior today.
  • the preset statistical period is composed of a plurality of sub-cycles, each of which is composed of a plurality of time intervals; and the foregoing predicting indicators in the current sub-period according to the trend of the motion data in consecutive consecutive sub-cycles includes: Obtaining motion data in a specified time interval in each sub-period; predicting a prediction index in a specified time interval in the current sub-period according to a trend of the motion data in the specified time interval in the plurality of sub-periods; and collecting the current sub-period
  • the real-time motion data within the data includes: collecting real-time motion data within a specified time interval in the current sub-period.
  • the preset statistical period is 7 days
  • each sub-period is one day
  • each sub-period is from 7:00 to 8:00, 8:00 to 11:00, 11:00 to 13:00, and 13:00 to 16: 00, 16:00 ⁇ 21:00, 21:00 ⁇ 7:00, a total of 6 time intervals.
  • the user gets the time from 7:00 to 8:00 in the first 7 days.
  • the exercise data in the interval can predict the forecast index of the time interval from 7:00 to 8:00 today according to the trend of the exercise data from 7:00 to 8:00 in the previous 7 days; according to today's 7:00-8 :00 time zone
  • the real-time motion data collected in the interval and the predicted prediction index of the 7:00 to 8:00 time interval today can determine whether the behavior of the user in the time interval from 7:00 to 8:00 today is abnormal. For the other time intervals, the same reason will not be repeated here.
  • the preset statistical period is 5 weeks
  • each sub-period is one week
  • each sub-period includes: 5 working days from 7:00 to 8:00, 8:00 to 11:00, 11:00 ⁇ 13:00, 13:00 ⁇ 16:00, 16:00 ⁇ 21:00, 21:00 ⁇ 7:00 time interval and 8:00 ⁇ 11:00, 11:00 for two rest days ⁇ 13:00, 13:00 ⁇ 16:00, 16:00 ⁇ 21:00, 21:00 ⁇ 8:00 time interval.
  • the trend of the sports data from 7:00 to 8:00 in the Monday of each of the first 5 weeks can predict the 7:00 of this week's Monday.
  • the prediction index of the interval can determine whether the user's behavior in the time interval of 7:00 to 8:00 on Monday of this week is abnormal. For the other time intervals, the same reason will not be repeated here.
  • step S130 of the method shown in FIG. 1 determines whether the user has abnormal behavior according to the real-time motion data, the prediction index obtained in the data index acquisition phase, and the preset policy, including: calculating according to real-time motion data.
  • the relevant parameters of the real-time motion data including: comparing the real-time motion data with the prediction index, determining that the user is abnormal when the real-time motion data exceeds a predetermined range of the prediction index, and the relevant parameters of the real-time motion data and/or the real-time motion data meet predetermined conditions behavior.
  • the real-time motion data exceeds the predetermined range of the prediction index and the real-time motion data meets the predetermined condition, it is determined that the user has an abnormal behavior; or, when the real-time motion data exceeds the predetermined range of the prediction index and the relevant parameters of the real-time motion data meet the predetermined schedule
  • the condition it is determined that the user has an abnormal behavior; or, when the real-time motion data exceeds a predetermined range of the prediction index, and the related parameters of the real-time motion data and the real-time motion data meet a predetermined condition, it is determined that the user has an abnormal behavior.
  • the inertial sensor in the wearable device includes an accelerometer, and the accelerometer therein is used to collect the acceleration of the user in the x-axis, y-axis, and/or z-axis directions after the user wears the wearable device.
  • the step S120 acquires the prediction index according to the change trend of the historical motion data, including: obtaining the x-axis and the y-axis according to the change trend of the acceleration in the x-axis, the y-axis, and/or the z-axis direction in the preset statistical period.
  • the preset statistical period is set to 7 days, and each day is divided into multiple time intervals. For example, the working day from Monday to Friday is divided into: 7:00 to 8:00 time interval.
  • the time interval from 8:00 to 11:00 is a small amount of exercise period, from 11:00 to 13:00 is the active period, from 13:00 to 16:00, a small amount of exercise period, from 16:00 to 21:00, activity Period, 21:00 ⁇ 8:00, rest period; Saturday, Sunday and workday are different, 19:00 ⁇ 20:30, fitness period; collect acceleration data in each time interval of each day, keep Effective data removes invalid data; acceleration data in the x-axis, y-axis, and/or z-axis directions for each time interval in the previous 7 days as historical motion data, based on the x-axis and y-axis of the same time interval in the previous 7 days
  • the trend of the acceleration of the and/or z-axis directions can predict the predicted maximum value, the predicted minimum value, and/or the predicted average value of the acceleration in the x-axis, y-axis, and/or z-axis directions of the same time interval on the 8th day.
  • FIG. 2A is a graph showing a trend of changes in acceleration in the x-axis direction in a predetermined statistical period according to an embodiment of the present invention, for each time interval of each day, based on accelerations of a plurality of x-axis directions acquired in the time interval.
  • the data can calculate the maximum value and the average value of the acceleration data in the x-axis direction in the time interval, and FIG. 2A shows the x in the 7-day data validity period and in the same time interval (eg, 11:00 to 13:00 active period).
  • the linear trend of the average acceleration, the maximum acceleration, and the maximum acceleration in the axial direction can be derived from the linear trend that the predicted maximum value of the acceleration in the x-axis direction in the same time interval on the eighth day is 1.475 m/s 2 .
  • 2B is a graph showing a change trend of acceleration in the y-axis direction in a preset statistical period according to an embodiment of the present invention, for each time interval of each day, based on accelerations of a plurality of y-axis directions acquired in the time interval.
  • the maximum and average values of the acceleration in the y-axis direction in the time interval can be calculated, and FIG.
  • 2B shows the y-axis in the same time interval (eg, 11:00 to 13:00 active period) within the 7-day data validity period.
  • the linear trend of the average acceleration, the maximum acceleration, and the maximum acceleration of the direction, according to the linear trend, can be obtained that the predicted maximum value of the acceleration in the y-axis direction in the same time interval on the eighth day is 1.6143 m/s 2 .
  • 2C is a graph showing a change trend of acceleration in the z-axis direction in a preset statistical period according to an embodiment of the present invention, for each time interval of each day, according to accelerations of a plurality of z-axis directions collected in the time interval.
  • FIG. 2C shows the z-axis in the same time interval (eg, 11:00 to 13:00 active period) during the 7-day data validity period.
  • the average acceleration of the direction, the maximum acceleration, and the linear trend of the maximum acceleration can be obtained.
  • the predicted maximum value of the acceleration in the z-axis direction in the same time interval on the eighth day is 9.6929 m/s 2 . Further, since the speed of the user can be obtained by integrating the acceleration data in the x-axis direction, the y-axis direction, and the z-axis direction, FIG.
  • FIG. 2D illustrates the speed of the user within a preset statistical period according to an embodiment of the present invention.
  • the change trend graph, for each time interval of the day, the maximum value and the average value of the speed in the time interval can be calculated according to the plurality of speeds in the time interval, and FIG. 2D shows the 7-day data validity period and the same time.
  • the average speed, the fastest speed, and the linear trend of the fastest speed in the interval (such as the 11:00 to 13:00 activity period), according to the linear trend, the prediction of the user's speed in the same time interval on the 8th day can be obtained.
  • the maximum value is 16.4427 km / h.
  • the motion data is collected by the inertial sensor in the wearable device, and the monitored motion data is calculated according to the historical motion data in the normal motion state in the past preset statistical period.
  • the motion characteristic curve corresponding to the change trend of the motion data forms a data template corresponding to the user, and the corresponding prediction data can be obtained by continuously learning the data template.
  • the step S130 determines whether the user has an abnormal behavior according to the real-time motion data, the prediction index obtained in the data index acquisition phase, and the preset policy, including: according to the real-time monitored user on the x-axis, the y-axis, and/or The acceleration in the z-axis direction is calculated by the user's real-time speed; when the magnitude of the acceleration in the z-axis direction monitored in real time exceeds the predicted maximum value of the acceleration in the z-axis direction, and the direction of the acceleration in the z-axis direction monitored in real time is positive from the z-axis direction.
  • the direction becomes the negative direction of the z-axis direction, and the real-time speed of the user becomes 0 and is maintained for a predetermined time it is determined that the user has a fall behavior. That is to say, in the process of monitoring the user behavior of the wearable device, when the magnitude of the acceleration of the user's vertical downward is monitored to exceed the predicted maximum value of the acceleration predicted in the direction according to the historical motion data, the user suddenly accelerates downward. When the direction of acceleration is changed from downward to upward, it indicates the situation of emergency stop motion. When the speed of the user is monitored to be maintained for a period of time, it means that there is no motion for a certain period of time after the emergency stop motion. When it occurs, it is determined that the user has a fall behavior.
  • the inertial sensor in the wearable device includes a gyroscope in addition to the accelerometer, and the accelerometer is used to collect the user on the x-axis, the y-axis, and/or the z after the user wears the wearable device.
  • the acceleration in the axial direction, the gyroscope is used to collect the angular velocity of rotation of the user around the x-axis direction, the y-axis direction, and/or the z-axis direction.
  • step S130 determines whether the user has an abnormal behavior according to the real-time motion data, the prediction index obtained in the data index acquisition phase, and the preset policy, including: the user according to the real-time monitoring on the x-axis, the y-axis, and/or The acceleration of the z-axis direction is calculated by the user's real-time speed; the real-time tilt angle of the user is calculated according to the real-time monitored user's rotational angular velocity around the x-axis direction, the y-axis direction, and/or the z-axis direction; when the z-axis direction is monitored in real time The magnitude of the acceleration exceeds the predicted maximum value of the acceleration in the z-axis direction, and the direction of the acceleration in the z-axis direction monitored in real time changes from the positive direction in the z-axis direction to the negative direction in the z-axis direction, and the real-time speed of the user becomes 0 and remains predetermined.
  • the discriminating rule of the discriminating rule ratio 1 of Example 2 has one more condition (ie, the user's tilt angle), which can more accurately determine the fall. behavior.
  • the wearable device of the present solution is further provided with a barometer.
  • the height of the user is monitored in real time through the barometer; then step S130 is based on real-time.
  • the motion data, the prediction index obtained in the data indicator acquisition stage, and the preset policy, and determining whether the user has an abnormal behavior further includes: after determining that the user has a fall behavior, further determining whether the real-time monitored user's height reduction exceeds a predetermined threshold. ; Yes, it is determined that the user has a high drop behavior.
  • the short message service SMS Short Message Service
  • the emergency number or emergency number
  • the emergency contact person can indicate the type of accident and the location of the accident in the text message, and play the SOS help voice at a certain frequency.
  • the fall behavior or the high drop behavior can be distinguished by different security levels of emergency calls or alarms.
  • the preset statistical period is composed of consecutive N sub-cycles before the current sub-period; wherein N is a positive integer greater than 1; the method shown in FIG. 1 further includes: when history When the earliest acquisition time corresponding to the motion data is not in consecutive N sub-cycles, the historical motion data collected before consecutive N sub-cycles is deleted. For example, the preset statistical period is 7 days. When the collection time corresponding to the motion data stored in the wearable device exceeds 7 days, some data needs to be deleted to maintain the resource of the wearable device. Therefore, the earliest stored in the wearable device is stored.
  • the motion data collected on the day is deleted, or at most only the statistical result values of the earliest collected motion data, such as the maximum value, the minimum value, and/or the average value. That is, all motion data is deleted in a first-in, first-out queue rule.
  • FIG. 3 shows a schematic diagram of a wearable device in accordance with one embodiment of the present invention.
  • the wearable device 300 includes a microprocessor 310 and an inertial sensor 320.
  • the inertial sensor 320 is configured to collect historical motion data of the user in a preset statistical period after the user wears the wearable device 300, and collect real-time motion data of the user.
  • the historical motion data and the real-time motion data are relatively speaking, the historical motion data refers to the motion data collected in the preset statistical period before the current time, and the real-time motion data refers to the motion data collected at the current time, the current Real-time motion data may be used as historical motion data.
  • the microprocessor 310 is connected to the inertial sensor 320, and is configured to obtain a prediction index according to a trend of the historical motion data; and to determine whether the user occurs according to the real-time motion data, the prediction index obtained in the data index acquisition phase, and the preset policy. Abnormal behavior; and send an alarm notification when it is determined that the user has an abnormal behavior.
  • the wearable device shown in FIG. 3 monitors the user's motion data.
  • the motion data collected by the wearable device in the previous preset statistical period is used as historical motion data, and the wearable device is currently
  • the motion data collected in real time is used as real-time motion data, and the prediction index is obtained according to the change rule of the historical motion data.
  • the prediction index, and the preset strategy it is determined whether the user has an abnormal behavior and alarms when the determination is yes. Monitoring the behavior of users wearing wearable devices.
  • the program can use the historical motion data of each user as the template data of self-learning for different users, and obtain the predictive index of the user's post-motion through continuous learning of the template data, combined with the theoretical predictive index and the current actual collected real-time motion.
  • the data can analyze and discover the abnormal behavior of the user, and realize customized and high-precision behavior monitoring.
  • inertial sensor 320 includes an accelerometer for acquiring acceleration of the user in the x-axis, y-axis, and/or z-axis directions
  • microprocessor 310 is coupled to the accelerometer for processing accelerometer acquisition Acceleration in the x-axis, y-axis, and/or z-axis directions.
  • the direction of the gravity vector is the z-axis direction
  • the forward direction of the user is the x-axis direction
  • the y-axis and the x-axis and the z-axis constitute a right-hand coordinate system, the right
  • the hand coordinate system changes with the user's movement, the same applies hereinafter.
  • the inertial sensor 320 includes not only an accelerometer, but also a gyroscope for acquiring a rotational angular velocity of the user about the x-axis direction, the y-axis direction, and/or the z-axis direction;
  • the device 310 is coupled to the accelerometer for processing accelerations in the x-axis, y-axis, and/or z-axis directions acquired by the accelerometer;
  • the microprocessor 310 is also coupled to the gyroscope and is also configured to process the x-axis direction of the gyroscope acquisition, Rotational angular velocity in the y-axis direction and/or the z-axis direction.
  • the wearable device 300 includes a microprocessor 310, an inertial sensor 320, a barometer 330, an alarm circuit 340, an emergency call circuit 350, and a heart rate sensor 360.
  • the inertial sensor 320 includes an accelerometer for acquiring acceleration of the user in the x-axis, y-axis, and/or z-axis directions and a gyroscope for collecting rotational angular velocities of the user about the x-axis direction, the y-axis direction, and/or the z-axis direction.
  • the microprocessor 310 is respectively connected with the accelerometer and the gyroscope to process the acceleration data collected by the accelerometer and the rotational angular velocity data collected by the gyroscope.
  • the barometer 330 is used to monitor the height of the user, and the microprocessor 310 is coupled to the barometer 330 for processing the height data monitored by the barometer 330.
  • the alarm circuit 340 includes an audio codec 341 and a speaker 342; the microprocessor 310 is coupled to the alarm circuit 340 for controlling the speaker 342 to sound through the audio codec 341.
  • the emergency call circuit 350 includes a radio frequency transceiver 351, a radio frequency front end module 352, and a radio frequency antenna 353; the microprocessor 310 is coupled to the emergency call circuit 350 for receiving or transmitting radio frequency signals through the emergency call circuit 350.
  • FIG. 5 is a flow chart showing the wearable device monitoring user behavior according to an embodiment of the present invention, from the perspective of a microprocessor in the wearable device. Starting from the specific operations of the components in the wearable device shown in FIG. 4, the working process of the wearable device includes:
  • Step S410 monitoring, by the heart rate sensor, that the user starts wearing the wearable device.
  • the microprocessor determines that the user starts wearing the wearable device.
  • step S420 the motion data is started to be recorded by the accelerometer and the gyroscope, and the height data is recorded by the barometer.
  • the recording of the motion data in this step is divided into two branches, one branch is from step S430 to step S450, and the retention and processing of the historical motion data is characterized, and the other branch is referred to as step S460, and the current real-time monitored motion data is represented. .
  • step S430 it is determined whether the collection time corresponding to the recorded data exceeds 7 days. If yes, step S440 is performed. In this embodiment, 7 days is used as a preset statistical period. Otherwise, step S420 is performed.
  • step S440 the first-in-first-out (FIFO) format is used to delete the data of the earliest day, and the entire data recording period is 7 days.
  • FIFO first-in-first-out
  • step S450 the user's own motion characteristic curve is calculated locally.
  • the trend of the 7-day exercise data is obtained.
  • the maximum and minimum values of the specific exercise data in the same time interval within 7 days are calculated. And/or an average value, thereby obtaining a variation curve of the maximum value of the specific motion data in the same time interval of 7 days, a variation curve of the minimum value, and/or an average value, and can predict the change according to the variation curves
  • the trend line of the maximum, minimum, and/or average values of the specific motion data is taken as the user's own motion characteristic curve.
  • step S460 real-time motion data and real-time height data of the user are monitored in real time.
  • step S470 it is determined whether the acceleration monitored in real time in step S460 exceeds the trend line corresponding to the maximum value of the acceleration calculated in step S450. If yes, step S480 is performed; otherwise, step S460 is continued.
  • step S480 it is determined by the heart rate sensor whether the user still wears the wearable device. If yes, step S490 is performed; otherwise, step S540 is performed.
  • step S490 it is determined whether it is stationary for 5 s to 10 s. If yes, step S500 is performed, otherwise step S460 is continued.
  • the real-time speed of the user is obtained by the acceleration integral monitored in real time, and the determination is established after the real-time speed of the user is 0 and is maintained for 5 s to 10 s.
  • step S500 it is determined that the user has a fall behavior.
  • step S510 it is determined whether the height of the user is reduced by 1 m or more. If yes, step S520 is performed; otherwise, step S500 is continued.
  • step S520 it is determined that the user has a high drop behavior.
  • Step S530 the emergency call message is sent through the emergency call circuit, and the SOS help sound is periodically played through the alarm circuit.
  • the step S530 can also be directly performed after the step 500.
  • step S540 the monitoring is stopped.
  • the wearable device shown in FIG. 4 can construct different template data (historical motion data) for different users, and can continuously learn in the subsequent process. More targeted, it can also improve monitoring accuracy.
  • the motion characteristics of the test subject are collected and the exercise trend curve is analyzed and calculated.
  • the data monitored in real time is compared with the analysis data of the test subject, which is more targeted and improves the accuracy.
  • the wearable device may be a smart watch, a smart wristband, or other types of wearable devices, which is not limited herein.
  • the technical solution provided by the present invention monitors the user's motion data through the wearable device.
  • the motion data collected by the wearable device in the previous preset statistical period is used as historical motion data.
  • the motion data collected by the wearable device in real time at the current time is used as real-time motion data, and the predicted index is obtained according to the change rule of the historical motion data, and the abnormal behavior of the user is determined according to the real-time motion data, the prediction index, and the preset strategy, and is determined to be It is an alarm that monitors the behavior of users wearing wearable devices.
  • the program can use the historical motion data of each user as the template data of self-learning for different users, and obtain the predictive index of the user's post-motion through continuous learning of the template data, combined with the theoretical predictive index and the current actual collected real-time motion.
  • the data can analyze and discover the abnormal behavior of the user, and realize customized and high-precision behavior monitoring.

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Abstract

一种用户行为监测方法和可穿戴设备，包括：在可穿戴设备中设置惯性传感器（步骤S110）；在每个数据指标获取阶段开始时，通过惯性传感器监测，采集用户在预设统计周期的历史运动数据（步骤S120）；根据历史运动数据的变化趋势获取预测指标；实时监测时，采集用户的实时运动数据，根据实时运动数据、获取到的预测指标以及预设策略，判断用户是否发生异常行为（步骤S130）；当判定用户发生异常行为时，发送报警通知（步骤S140）。该设备能够针对不同的用户，实现了定制化、高精度的行为监测。

Description

一种用户行为监测方法和可穿戴设备 技术领域
本发明涉及可穿戴智能设备领域，尤其涉及一种用户行为监测方法和可穿戴设备。
背景技术
随着移动互联网技术的快速发展，可穿戴设备日新月异，在互联网领域不断掀起新高潮，其关注度、需求度都在不断提升。其中，为符合现代人对健康运动方面日益关注的趋势，各种可穿戴设备都相继推出了用户行为监测功能。
然而，现有可穿戴设备监测用户行为的方法一般是从一类相似人群的正常行为(如走路，跑步)的大数据信息中，提取特征，训练出一个统一的一分类模型，凡不符合该一分类模型的行为会被判断为异常行为，如跌倒、摔落等异常行为。在这种现有的技术方案中，每个人的个体差异未被考虑，而且由于个别正常行为(如跑步、下楼梯)的瞬间特征信息也与异常行为的特征信息相似，容易造成误报。
发明内容
本发明鉴于上述问题，本发明提供了一种用户行为监测方法和可穿戴设备，以解决上述问题或者至少部分地解决上述问题。
依据本发明的一个方面，提供了一种用户行为监测方法，包括：
在可穿戴设备中设置惯性传感器；
在每个数据指标获取阶段开始时，当用户佩戴可穿戴设备后，通过惯性传感器监测，采集用户在预设统计周期的历史运动数据；根据历史运动数据的变化趋势获取预测指标；
实时监测时，采集用户的实时运动数据，根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为；
当判定用户发生异常行为时，发送报警通知。
可选地，预设统计周期由多个子周期构成；
采集用户在预设统计周期的历史运动数据包括：采集预设统计周期中的每个子周期内的运动数据；
根据历史运动数据的变化趋势获取预测指标包括：根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标；
采集用户的实时运动数据包括：采集当前子周期内的实时运动数据。
可选地，每个子周期由多个时间区间构成；
根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标包括：
获取每个子周期中的指定时间区间内的运动数据；
根据多个子周期中的指定时间区间内的运动数据的变化趋势，预测当前子周期中的指定时间区间内的预测指标；
采集当前子周期内的实时运动数据包括：采集当前子周期中的指定时间区间内的实时运动数据。
可选地，根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
根据实时运动数据计算实时运动数据的相关参数；
将实时运动数据与预测指标进行比较，当实时运动数据超出预测指标的预定范围、且实时运动数据和/或实时运动数据的相关参数符合预定条件时，判定用户发生异常行为。
可选地，惯性传感器包括：用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计；
根据历史运动数据的变化趋势获取预测指标包括：根据预设统计周期内的x轴、y轴和/或z轴方向的加速度的变化趋势，获得x轴、y轴和/或z轴方向的加速度的预测最大值、预测最小值和/或预测平均值；
根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；
当实时监测的z轴方向的加速度的大小超过z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、并且用户的实时速度变为0并维持预定时间时，判定用户发生跌倒行为；
其中，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右手坐标系随用户运动而变化。
可选地，惯性传感器还包括：用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；
根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；
根据实时监测的用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度计算得到用户的实时倾斜角度；
当实时监测的z轴方向的加速度的大小超过z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、用户的实时速度变为0并维持预定时间、并且用户的实时倾斜角度超过预定角度时，判定用户发生跌倒行为。
可选地，该方法进一步包括：在可穿戴设备中设置气压计；当用户佩戴可穿戴设备后，通过气压计实时监测用户的高度；
根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为还包括：
在判定用户发生跌倒行为后，进一步判断实时监测到的用户的高度的降低是否超过预定阈值；是则，判定用户发生高处摔落行为。
可选地，预设统计周期由当前子周期之前的连续N个子周期构成；其中，N为大于1的正整数；
该方法进一步包括：当历史运动数据对应的最早采集时间不在连续N个子周期内时，将在连续N个子周期之前采集的历史运动数据删除。
依据本发明的另一个方面，提供了一种可穿戴设备，包括：惯性传感器和微处理器；
惯性传感器用于在用户佩戴可穿戴设备后，采集用户在预设统计周期的历史运动数据，以及采集用户的实时运动数据；
微处理器与惯性传感器连接，用于根据历史运动数据的变化趋势获取预测指标；以及用于根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为；当判定用户发生异常行为时，发送报警通知。
可选地，可穿戴设备中还设置有报警电路；报警电路包括：音频编解码器 和扬声器；
微处理器与报警电路连接，用于通过音频编解码器控制扬声器发声。
可选地，可穿戴设备中还设置有紧急呼叫电路；紧急呼叫电路包括：射频收发器、射频前端模块和射频天线；
微处理器与紧急呼叫电路连接，用于通过紧急呼叫电路接收或发送射频信号。
可选地，惯性传感器包括用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计，或者，惯性传感器包括加速度计和用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；
微处理器与加速度计连接，用于处理加速度计采集的x轴、y轴和/或z轴方向的加速度；微处理器与陀螺仪连接，还用于处理陀螺仪采集的x轴方向、y轴方向和/或z轴方向的旋转角速度；
可穿戴设备中还设置有用于监测用户高度的气压计；微处理器与气压计连接，还用于处理气压计采集的高度数据；
其中，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右手坐标系随用户运动而变化。
由上述可知，本发明提供的技术方案通过可穿戴设备监测用户的运动数据，对于当前时间来说，将可穿戴设备在之前的预设统计周期内采集到的运动数据作为历史运动数据，将可穿戴设备在当前时间实时采集到的运动数据作为实时运动数据，根据历史运动数据的变化规律获取预测指标，根据实时运动数据、预测指标以及预设策略来判断用户是否发生异常行为并在判定为是时报警，实现了对佩戴可穿戴设备的用户行为的监测。本方案能够针对不同的用户，将每个用户个人的历史运动数据作为自学习的模板数据，通过对模板数据的不断学习得到用户之后运动的预测指标，结合理论预测指标以及当前实际采集的实时运动数据能够分析发现用户的非正常行为，实现了定制化、高精度的行为监测。
附图简要说明
图1示出了根据本发明一个实施例的一种用户行为监测方法的流程图；
图2A示出了根据本发明一个实施的预设统计周期内的x轴方向的加速度的变化趋势图；
图2B示出了根据本发明一个实施的预设统计周期内的y轴方向的加速度的变化趋势图；
图2C示出了根据本发明一个实施的预设统计周期内的z轴方向的加速度的变化趋势图；
图2D示出了根据本发明一个实施的预设统计周期内的用户的速度的变化趋势图；
图3示出了根据本发明一个实施例的一种可穿戴设备的示意图；
图4示出了根据本发明另一个实施例的一种可穿戴设备的示意图；
图5示出了根据本发明一个实施例的可穿戴设备监测用户行为的流程图。
具体实施方式
下面为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明实施方式作进一步地详细描述。
图1示出了根据本发明一个实施例的一种用户行为监测方法的流程图。如图1所示，该方法包括：
步骤S110，在可穿戴设备中设置惯性传感器。
步骤S120，在每个数据指标获取阶段开始时，当用户佩戴可穿戴设备后，通过惯性传感器监测，采集用户在预设统计周期的历史运动数据；根据历史运动数据的变化趋势获取预测指标。
步骤S130，实时监测时，采集用户的实时运动数据，根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为。
步骤S140，当判定用户发生异常行为时，发送报警通知。
其中需要说明的是，步骤S120采集历史运动数据的过程与步骤S130采集实时运动数据的过程均是基于可穿戴设备中的惯性传感器监测运动数据的功能而实现的，历史运动数据的采集时间和处理时间之间具有一定的时间差，而实时运动数据的采集时间与处理时间之间几乎没有时间差，具体地，步骤S120中的历史运动数据是指在当前时间之前预设统计周期内采集的运动数据，而步骤S130中的实时运动数据是指在当前时间采集的运动数据，当前的实时运动数据可能会作为之后的历史运动数据，因此步骤S120数据指标获取阶段与步骤S130实时监测阶段不是按照执行时序划分的，而是按照执行内容划分的，数据指标获取阶段与实时监测阶段可以同时进行。
可见，图1所示的方法通过可穿戴设备监测用户的运动数据，对于当前时 间来说，将可穿戴设备在之前的预设统计周期内采集到的运动数据作为历史运动数据，将可穿戴设备在当前时间实时采集到的运动数据作为实时运动数据，根据历史运动数据的变化规律获取预测指标，根据实时运动数据、预测指标以及预设策略来判断用户是否发生异常行为并在判定为是时报警，实现了对佩戴可穿戴设备的用户行为的监测。本方案能够针对不同的用户，将每个用户个人的历史运动数据作为自学习的模板数据，通过对模板数据的不断学习得到用户之后运动的预测指标，结合理论预测指标以及当前实际采集的实时运动数据能够分析发现用户的非正常行为，实现了定制化、高精度的行为监测。
在本发明的一个实施例中，预设统计周期由多个子周期构成；上述步骤S120采集用户在预设统计周期的历史运动数据包括：采集预设统计周期中的每个子周期内的运动数据；上述步骤S120根据预设统计周期中的历史运动数据的变化趋势获取预测指标包括：根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标；上述步骤S130采集用户的实时运动数据包括：采集当前子周期内的实时运动数据。
例如，预设统计周期为一周，则该预设统计周期由7个子周期构成，每个子周期是一天，则对于当前子周期(今天)来说，本方案将用户在前7天的运动数据作为历史运动数据，根据前7天的运动数据的变化趋势获取今天运动的预测指标，根据今天采集的实时运动数据、所获取的今天运动的预测指标以及预设策略来判断用户今天是否发生异常行为。
更为优化地，预设统计周期由多个子周期构成，每个子周期由多个时间区间构成；则上述根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标包括：获取每个子周期中的指定时间区间内的运动数据；根据多个子周期中的指定时间区间内的运动数据的变化趋势，预测当前子周期中的指定时间区间内的预测指标；上述采集当前子周期内的实时运动数据包括：采集当前子周期中的指定时间区间内的实时运动数据。
例如，预设统计周期为7天，每个子周期是一天，每个子周期由7:00～8:00、8:00～11:00、11:00～13:00、13:00～16:00、16:00～21:00、21:00～7:00共6个时间区间构成，对于当前子周期(今天)来说，获取用户在前7天每天的7:00～8:00时间区间内的运动数据，根据前7天每天7:00～8:00时间区间的运动数据的变化趋势可以预测今天7:00～8:00时间区间的预测指标；根据今天在7:00～8:00时间区 间内采集的实时运动数据、所预测的今天7:00～8:00时间区间的预测指标可以判断用户在今天7:00～8:00时间区间内的行为是否异常。对于其他时间区间来说同理，在此不再赘述。
或者，在另一个例子中，预设统计周期为5个星期，每个子周期是一个星期，每个子周期包括：五个工作日的7:00～8:00、8:00～11:00、11:00～13:00、13:00～16:00、16:00～21:00、21:00～7:00时间区间以及两个休息日的8:00～11:00、11:00～13:00、13:00～16:00、16:00～21:00、21:00～8:00时间区间。则对于本星期的星期一来说，根据前5个星期中的每个星期的星期一的7:00～8:00时间区间的运动数据的变化趋势可以预测本星期的星期一的7:00～8:00时间区间的预测指标；根据本星期的星期一在7:00～8:00时间区间内采集的实时运动数据、所预测的本星期的星期一的7:00～8:00时间区间的预测指标可以判断用户在本星期的星期一的7:00～8:00时间区间内的行为是否异常。对于其他时间区间来说同理，在此不再赘述。
可以看出，预设统计周期的时间越长、子周期和时间区间被划分地越细致，越能够更小粒度地通过历史运动数据预测一个用户之后的运动规律，但是预设统计周期的延长和子周期或时间区间的细化势必要带来对可穿戴设备的存储资源的占用、增加计算负荷，因此需要选取一个平衡点来中和这两方面，以实现最有效的监控方案。
在本发明的一个实施例中，图1所示方法的步骤S130根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：根据实时运动数据计算实时运动数据的相关参数；将实时运动数据与预测指标进行比较，当实时运动数据超出预测指标的预定范围、且实时运动数据和/或实时运动数据的相关参数符合预定条件时，判定用户发生异常行为。也就是说，当实时运动数据超出预测指标的预定范围且实时运动数据符合预定条件时，判定用户发生异常行为；或者，当实时运动数据超出预测指标的预定范围且实时运动数据的相关参数符合预定条件时，判定用户发生异常行为；或者，当实时运动数据超出预测指标的预定范围、且实时运动数据和实时运动数据的相关参数符合预定条件时，判定用户发生异常行为。
通过具体的例子来进行说明，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右手坐标系随用户运动而 变化，在例1中，可穿戴设备中的惯性传感器包括加速度计，用户佩戴可穿戴设备后，其中的加速度计用于采集用户在x轴、y轴和/或z轴方向的加速度。
在本例1中，步骤S120根据历史运动数据的变化趋势获取预测指标包括：根据预设统计周期内的x轴、y轴和/或z轴方向的加速度的变化趋势，获得x轴、y轴和/或z轴方向的加速度的预测最大值、预测最小值和/或预测平均值。具体地，设定预设统计周期为7天，每天作为一个子周期，每个子周期划分为多个时间区间，如周一到周五的工作日每天划分为：7：00～8：00时间区间为活动期，8:00～11:00时间区间为少量运动期，11:00～13:00为活动期，13:00～16:00，少量运动期，16:00～21:00，活动期，21:00～8:00，休息期；周六、周日与工作日的区别是，19:00～20:30，健身期；采集每一天的每个时间区间内的加速度数据，保留有效数据去除无效数据；以前7天中每天每个时间区间内的x轴、y轴和/或z轴方向的加速度数据作为历史运动数据，根据前7天每天同一时间区间的x轴、y轴和/或z轴方向的加速度的变化趋势可以预测第8天同一时间区间的x轴、y轴和/或z轴方向的加速度的预测最大值、预测最小值和/或预测平均值。
图2A示出了根据本发明一个实施的预设统计周期内的x轴方向的加速度的变化趋势图，对于每天的每个时间区间，根据该时间区间内采集到的多个x轴方向的加速度数据可以计算得到该时间区间内的x轴方向的加速度数据的最大值和平均值，图2A示出7天数据有效期内，同一时间区间内(如11:00～13:00活动期)的x轴方向的平均加速度、最大加速度以及最大加速度的线性趋势，根据该线性趋势能够得出第8天的同一时间区间内的x轴方向的加速度的预测最大值为1.475m/s2。图2B示出了根据本发明一个实施的预设统计周期内的y轴方向的加速度的变化趋势图，对于每天的每个时间区间，根据该时间区间内采集到的多个y轴方向的加速度可以计算得到该时间区间内的y轴方向的加速度的最大值和平均值，图2B示出了7天数据有效期内，同一时间区间内(如11:00～13:00活动期)的y轴方向的平均加速度、最大加速度以及最大加速度的线性趋势，根据该线性趋势能够得出第8天的同一时间区间内的y轴方向的加速度的预测最大值为1.6143m/s2。图2C示出了根据本发明一个实施的预设统计周期内的z轴方向的加速度的变化趋势图，对于每天的每个时间区间，根据该时间区间内采集到的多个z轴方向的加速度可以计算得到该时间区间内的z轴方向的加速度的最大值和平均值，图2C示出了7天数据有效期内，同一时间区间 内(如11:00～13:00活动期)的z轴方向的平均加速度、最大加速度以及最大加速度的线性趋势，根据该线性趋势能够得出第8天的同一时间区间内的z轴方向的加速度的预测最大值为9.6929m/s2。进一步地，由于通过对x轴方向、y轴方向和z轴方向的加速度数据进行积分能够得到用户的速度，则图2D示出了根据本发明一个实施的预设统计周期内的用户的速度的变化趋势图，对于每天的每个时间区间，根据该时间区间内的多个速度可以计算得到该时间区间内的速度的最大值和平均值，图2D示出了7天数据有效期内，同一时间区间内(如11:00～13:00活动期)的平均速度、最快速度以及最快速度的线性趋势，根据该线性趋势能够得出第8天的同一时间区间内的用户的速度的预测最大值为16.4427km/h。
可见，针对不同的用户，在用户佩戴可穿戴设备后，通过可穿戴设备中的惯性传感器进行运动数据的收集，根据历史运动数据计算得到被监测者在过去的预设统计周期内在正常运动状态下运动数据的变化趋势对应的运动特性曲线，形成该用户对应的数据模板，通过对该数据模板的不断学习能够获得相应的预测数据。
在本例1中，步骤S130根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；当实时监测的z轴方向的加速度的大小超过z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、并且用户的实时速度变为0并维持预定时间时，判定用户发生跌倒行为。也就是说，在可穿戴设备监测用户行为的过程中，当监测到用户竖直向下的加速度的大小超过依据历史运动数据预测的该方向的加速度的预测最大值，说明用户突然向下加速，当监测到加速度的方向从向下变成向上，说明发生紧急停止运动的情况，当监测到用户的速度保持一段时间为0，说明在紧急停止运动之后一定时间段内无运动，在上述情况均发生时，确定用户发生跌倒行为。
进一步地，在例2中，可穿戴设备中的惯性传感器除了包括加速度计，还包括陀螺仪，用户佩戴可穿戴设备后，其中的加速度计用于采集用户在x轴、y轴和/或z轴方向的加速度，陀螺仪用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度。
在本例2中，步骤S130根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；根据实时监测的用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度计算得到用户的实时倾斜角度；当实时监测的z轴方向的加速度的大小超过z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、用户的实时速度变为0并维持预定时间、并且用户的实时倾斜角度超过预定角度时，判定用户发生跌倒行为。也就是说，在可穿戴设备监测用户行为的过程中，当监测到用户突然向下加速、发生紧急停止运动的情况、在紧急停止运动之后一定时间段内无运动，并且在发生这一系列情况的时间内用户的倾斜角度也超过正常范围，则确定用户发生跌倒行为，例2的判别规则比例1的判别规则多了一个条件(即，用户的倾斜角度)，能够更为精确地判断出跌倒行为。
在上述例1或例2的基础上，再进一步地，本方案的可穿戴设备中还设置有气压计，当用户佩戴可穿戴设备后，通过气压计实时监测用户的高度；则步骤S130根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为还包括：在判定用户发生跌倒行为后，进一步判断实时监测到的用户的高度的降低是否超过预定阈值；是则，判定用户发生高处摔落行为。也就是说，在通过上述例1或例2的判断规则判定用户发生跌倒行为后，进一步需要判断该跌倒行为的严重程度，则通过气压计监测到的高度数据来判断用户是否下落了超过安全范围的高度，如果是则确定用户的跌倒行为具体是高处摔落行为，需要更紧急的应对机制。
在确定用户发生跌倒行为或高处摔落行为时，通过可穿戴设备的GSM(Global System for Mobile Communication，全球移动通信系统)网络发送紧急呼救SMS(Short Message Service，短信息服务)短信至120(急救电话号码)或紧急联络人，可以在短信中表明事故类型以及出事地点，并以一定的频率播放SOS求救声音。其中，可以通过紧急呼叫或报警的不同安全级别来区分跌倒行为或高处摔落行为。
在本发明的一个实施例中，预设统计周期由当前子周期之前的连续N个子周期构成；其中，N为大于1的正整数；图1所示的方法进一步包括：当历史 运动数据对应的最早采集时间不在连续N个子周期内时，将在连续N个子周期之前采集的历史运动数据删除。例如，预设统计周期为7天，当可穿戴设备中存储的运动数据对应的采集时间超出7天时，需要删除一些数据来保持可穿戴设备的资源余地，因此，将可穿戴设备中存储的最早的一天采集的运动数据均删除，或者最多只保留最早采集的运动数据的统计结果值，如最大值、最小值和/或平均值等。即所有的运动数据都以先入先出的队列规则进行删除。
图3示出了根据本发明一个实施例的一种可穿戴设备的示意图。如图3所示，该可穿戴设备300包括：微处理器310和惯性传感器320。
惯性传感器320用于在用户佩戴可穿戴设备300后，采集用户在预设统计周期的历史运动数据，以及采集用户的实时运动数据。其中，历史运动数据和实时运动数据是相对而言的，历史运动数据是指在当前时间之前预设统计周期内采集的运动数据，而实时运动数据是指在当前时间采集的运动数据，当前的实时运动数据可能会作为之后的历史运动数据。
微处理器310与惯性传感器320连接，用于根据历史运动数据的变化趋势获取预测指标；以及用于根据实时运动数据、数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为；并在判定用户发生异常行为时，发送报警通知。
可见，图3所示的可穿戴设备监测用户的运动数据，对于当前时间来说，将可穿戴设备在之前的预设统计周期内采集到的运动数据作为历史运动数据，将可穿戴设备在当前时间实时采集到的运动数据作为实时运动数据，根据历史运动数据的变化规律获取预测指标，根据实时运动数据、预测指标以及预设策略来判断用户是否发生异常行为并在判定为是时报警，实现了对佩戴可穿戴设备的用户行为的监测。本方案能够针对不同的用户，将每个用户个人的历史运动数据作为自学习的模板数据，通过对模板数据的不断学习得到用户之后运动的预测指标，结合理论预测指标以及当前实际采集的实时运动数据能够分析发现用户的非正常行为，实现了定制化、高精度的行为监测。
在本发明的一个实施例中，惯性传感器320包括用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计，微处理器310与加速度计连接，用于处理加速度计采集的x轴、y轴和/或z轴方向的加速度。其中，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右 手坐标系随用户运动而变化，下文同理。
进一步地，在本发明的另一个实施例中，惯性传感器320不仅包括加速度计，还包括用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；微处理器310与加速度计连接，用于处理加速度计采集的x轴、y轴和/或z轴方向的加速度；微处理器310还与陀螺仪连接，还用于处理陀螺仪采集的x轴方向、y轴方向和/或z轴方向的旋转角速度。
图4示出了根据本发明另一个实施例的一种可穿戴设备的示意图。如图4所示，该可穿戴设备300包括：微处理器310、惯性传感器320、气压计330、报警电路340、紧急呼叫电路350和心率传感器360。
惯性传感器320中包括用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计和用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；微处理器310分别与加速度计和陀螺仪连接，对加速度计采集的加速度数据和陀螺仪采集的旋转角速度数据进行处理。
气压计330用于监测用户的高度，微处理器310与气压计330连接，用于对气压计330监测到的高度数据进行处理。
报警电路340包括：音频编解码器341和扬声器342；微处理器310与报警电路340连接，用于通过音频编解码器341控制扬声器342发声。
紧急呼叫电路350包括：射频收发器351、射频前端模块352和射频天线353；微处理器310与紧急呼叫电路350连接，用于通过紧急呼叫电路350接收或发送射频信号。
通过图5来说明图4所示的可穿戴设备的工作原理，图5示出了根据本发明一个实施例的可穿戴设备监测用户行为的流程图，从可穿戴设备中的微处理器的角度出发，具体说明了图4所示的可穿戴设备中的各个部件所执行的工作，则上述可穿戴设备的工作过程包括：
步骤S410，通过心率传感器监测用户开始佩戴可穿戴设备。
即当心率传感器监测到用户的心率数据时，微处理器确定用户开始佩戴可穿戴设备。
步骤S420，通过加速度计和陀螺仪开始记录运动数据，通过气压计开始记录高度数据。
其中，本步骤对运动数据的记录分为两个分支，一个分支是从步骤S430-步骤S450，表征对于历史运动数据的保留和处理，另一个分支是指步骤S460，表征当前实时监测的运动数据。
步骤S430，判断所记录的数据对应的采集时间是否超过7天，是则执行步骤S440，本实施例中以7天为预设统计周期，否则，执行步骤S420。
步骤S440，采用先入先出(FIFO)的形式，删除最早一天的数据，保证整个数据记录周期是7天。
步骤S450，本地计算出用户自身的运动特性曲线。
即根据所记录的7天的运动数据，得到7天的运动数据的变化趋势，对于具体的运动数据来说，计算出该具体的运动数据在7天内每天同一时间区间内的最大值、最小值和/或平均值，进而得到该具体的运动数据在7天的同一时间区间的最大值的变化曲线、最小值的变化曲线和/或平均值的变化曲线，且根据这些变化曲线能够预测出该具体的运动数据的最大值、最小值和/或平均值的趋势线，作为用户自身的运动特性曲线。
步骤S460，实时监测用户的实时运动数据和实时高度数据。
步骤S470，判断步骤S460实时监测的加速度是否超出步骤S450计算得到的加速度的最大值对应的趋势线，是则，执行步骤S480，否则，继续步骤S460。
步骤S480，通过心率传感器判断用户是否还佩戴可穿戴设备，是则，执行步骤S490，否则，执行步骤S540。
步骤S490，判断是否静止5s～10s，是则执行步骤S500，否则继续步骤S460。
即通过实时监测的加速度积分得到用户的实时速度，当用户的实时速度为0且维持5s～10s之后，此判定成立。
步骤S500，判定用户发生跌倒行为。
步骤S510，判断用户的高度是否降低1m以上，是则执行步骤S520，否则继续步骤S500。
步骤S520，判定用户发生高处摔落行为。
步骤S530，通过紧急呼叫电路发送紧急报警短信，通过报警电路周期性地播放SOS求救声音。
其中，在步骤500之后也可直接执行步骤S530。
步骤S540，停止监测。
可见，图4所示的可穿戴设备能针对各个不同的用户，构建不同的模板数据(历史运动数据)，且能在后续过程中不断学习。更具针对性，也能提高监测精度。采集被测者本身的运动特性并分析计算出运动趋势曲线，实时监控的数据与被测者本身的分析数据对比，更具有针对性，提高了准确性。
上述各实施例中，可穿戴设备可以是智能手表，也可以是智能手环，还可以是其他类型的可穿戴设备，在此不做限制。
需要说明的是，图3-图4所示的可穿戴设备的工作原理的各实施例与上文中图1-图2所示的各实施例对应相同，相同的部分不再赘述。
综上所述，本发明提供的技术方案通过可穿戴设备监测用户的运动数据，对于当前时间来说，将可穿戴设备在之前的预设统计周期内采集到的运动数据作为历史运动数据，将可穿戴设备在当前时间实时采集到的运动数据作为实时运动数据，根据历史运动数据的变化规律获取预测指标，根据实时运动数据、预测指标以及预设策略来判断用户是否发生异常行为并在判定为是时报警，实现了对佩戴可穿戴设备的用户行为的监测。本方案能够针对不同的用户，将每个用户个人的历史运动数据作为自学习的模板数据，通过对模板数据的不断学习得到用户之后运动的预测指标，结合理论预测指标以及当前实际采集的实时运动数据能够分析发现用户的非正常行为，实现了定制化、高精度的行为监测。
以上所述仅为本发明的较佳实施例而已，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内所作的任何修改、等同替换、改进等，均包含在本发明的保护范围内。

Claims (13)

  1. 一种用户行为监测方法，其中，包括：
    在可穿戴设备中设置惯性传感器；
    在每个数据指标获取阶段开始时，当用户佩戴可穿戴设备后，通过惯性传感器监测，采集用户在预设统计周期的历史运动数据；根据所述历史运动数据的变化趋势获取预测指标；
    实时监测时，采集用户的实时运动数据，根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为；
    当判定用户发生异常行为时，发送报警通知。
  2. 如权利要求1所述的方法，其中，所述预设统计周期由多个子周期构成；
    所述采集用户在预设统计周期的历史运动数据包括：采集预设统计周期中的每个子周期内的运动数据；
    所述根据所述历史运动数据的变化趋势获取预测指标包括：根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标；
    所述采集用户的实时运动数据包括：采集当前子周期内的实时运动数据。
  3. 如权利要求2所述的方法，其中，每个子周期由多个时间区间构成；
    所述根据连续多个子周期内的运动数据的变化趋势，获取当前子周期内的预测指标包括：
    获取每个子周期中的指定时间区间内的运动数据；
    根据多个子周期中的指定时间区间内的运动数据的变化趋势，预测当前子周期中的指定时间区间内的预测指标；
    所述采集当前子周期内的实时运动数据包括：采集当前子周期中的指定时间区间内的实时运动数据。
  4. 如权利要求1所述的方法，其中，根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
    根据所述实时运动数据计算所述实时运动数据的相关参数；
    将所述实时运动数据与所述预测指标进行比较，当所述实时运动数据超出所述预测指标的预定范围、且满足下述之一时，判定用户发生异常行为：
    所述实时运动数据符合预定条件；
    所述实时运动数据的相关参数符合预定条件；
    所述实时运动数据和实时运动数据的相关参数符合预定条件。
  5. 如权利要求4所述的方法，其中，惯性传感器包括：用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计；
    所述根据所述历史运动数据的变化趋势获取预测指标包括：根据预设统计周期内的x轴、y轴和/或z轴方向的加速度的变化趋势，获得x轴、y轴和/或z轴方向的加速度的预测最大值、预测最小值和/或预测平均值；
    所述根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
    根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；
    当实时监测的z轴方向的加速度的大小超过所述z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、并且用户的实时速度变为0并维持预定时间时，判定用户发生跌倒行为；
    其中，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右手坐标系随用户运动而变化。
  6. 如权利要求5所述的方法，其中，惯性传感器还包括：用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；
    所述根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为包括：
    根据实时监测的用户在x轴、y轴和/或z轴方向的加速度计算得到用户的实时速度；
    根据实时监测的用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度计 算得到用户的实时倾斜角度；
    当实时监测的z轴方向的加速度的大小超过所述z轴方向的加速度的预测最大值、实时监测的z轴方向的加速度的方向从z轴方向的正方向变为z轴方向的负方向、用户的实时速度变为0并维持预定时间、并且用户的实时倾斜角度超过预定角度时，判定用户发生跌倒行为。
  7. 如权利要求5所述的方法，其中，该方法进一步包括：在可穿戴设备中设置气压计；当用户佩戴可穿戴设备后，通过气压计实时监测用户的高度；
    所述根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为还包括：
    在判定用户发生跌倒行为后，进一步判断实时监测到的用户的高度的降低是否超过预定阈值；是则，判定用户发生高处摔落行为。
  8. 如权利要求2所述的方法，其中，所述预设统计周期由当前子周期之前的连续N个子周期构成；其中，N为大于1的正整数；
    该方法进一步包括：当历史运动数据对应的最早采集时间不在所述连续N个子周期内时，将在所述连续N个子周期之前采集的历史运动数据删除。
  9. 一种可穿戴设备，其中，包括：惯性传感器和微处理器；
    惯性传感器用于在用户佩戴可穿戴设备后，采集用户在预设统计周期的历史运动数据，以及采集用户的实时运动数据；
    微处理器与惯性传感器连接，用于根据所述历史运动数据的变化趋势获取预测指标；以及用于根据所述实时运动数据、所述数据指标获取阶段中获取到的预测指标以及预设策略，判断用户是否发生异常行为；当判定用户发生异常行为时，发送报警通知。
  10. 如权利要求9所述的可穿戴设备，其中，可穿戴设备中还设置有报警电路；报警电路包括：音频编解码器和扬声器；
    微处理器与报警电路连接，用于通过音频编解码器控制扬声器发声。
  11. 如权利要求9或10所述的可穿戴设备，其中，可穿戴设备中还设置有紧急呼叫电路；紧急呼叫电路包括：射频收发器、射频前端模块和射频天线；
    微处理器与紧急呼叫电路连接，用于通过紧急呼叫电路接收或发送射频信 号。
  12. 如权利要求9所述的可穿戴设备，其中，惯性传感器包括用于采集用户在x轴、y轴和/或z轴方向的加速度的加速度计，或者，惯性传感器包括加速度计和用于采集用户绕x轴方向、y轴方向和/或z轴方向的旋转角速度的陀螺仪；
    微处理器与加速度计连接，用于处理加速度计采集的x轴、y轴和/或z轴方向的加速度；微处理器与陀螺仪连接，还用于处理陀螺仪采集的x轴方向、y轴方向和/或z轴方向的旋转角速度；
    可穿戴设备中还设置有用于监测用户高度的气压计；微处理器与气压计连接，还用于处理气压计采集的高度数据；
    其中，以重力矢量方向为z轴方向，以用户正前方向为x轴方向，y轴与x轴、z轴构成右手坐标系，该右手坐标系随用户运动而变化。
  13. 如权利要求9所述的可穿戴设备，其中，可穿戴设备中还设置有用于监测用户是否佩戴可穿戴设备的心率传感器；微处理器与心率传感器连接。
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