CN217118429U - Wearable tumble prediction system - Google Patents
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- CN217118429U CN217118429U CN202123066179.5U CN202123066179U CN217118429U CN 217118429 U CN217118429 U CN 217118429U CN 202123066179 U CN202123066179 U CN 202123066179U CN 217118429 U CN217118429 U CN 217118429U
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Abstract
A wearable tumble prediction system relates to the tumble prediction field and comprises an upper computer, an Android system mobile phone, a single chip microcomputer, a tumble prediction sensor, two gait information acquisition sensors, a Bluetooth module, a power circuit and a PCB (printed circuit board); the single chip microcomputer, the falling prediction sensor, the Bluetooth module and the power circuit are integrated on the PCB; the power circuit, the falling prediction sensor and the two gait information acquisition sensors are all connected with the single chip microcomputer through cables; the singlechip is communicated with the Android system mobile phone through a Bluetooth module; the Android system mobile phone and the upper computer perform wireless data transmission; the power circuit is respectively connected with the falling prediction sensor, the two gait information acquisition sensors, the singlechip and the Bluetooth module; the PCB board is installed at human belly, and two gait information acquisition sensors are installed respectively in human left and right ankle department. The utility model discloses it is convenient to use, and the rate of accuracy of the prediction of tumbleing reaches more than 90%, and the prediction of tumbleing is effectual.
Description
Technical Field
The utility model relates to a prediction technical field tumbles, concretely relates to wearing formula prediction system tumbles.
Background
The elderly have gradually poor physical quality along with the rise of age, and the falling is a more prominent phenomenon among the elderly, so that the elderly need to be monitored daily to predict the falling in order to prevent the falling situation in daily activities.
At present, there are many apparatuses for fall detection at home and abroad, and fall detection products can be simply divided into three types of methods based on the use of different sensors: 1) based on video sensor detection; 2) detecting based on the ambient signal; 3) detecting based on the wearable instrument. However, these fall detection instruments mainly have the disadvantages of low fall prediction accuracy, poor prediction effect and the like.
SUMMERY OF THE UTILITY MODEL
In order to solve the problem of tumbleing because of the gait problem produces, especially the unexpected injury problem that old person tumbles and causes, the utility model provides a wearing formula prediction system that tumbles.
The utility model discloses a solve the technical scheme that technical problem adopted as follows:
the utility model discloses a wearing formula prediction system that tumbles, include: the system comprises an upper computer, an Android system mobile phone, a single chip microcomputer, a falling prediction sensor, a first step state information acquisition sensor, a second step state information acquisition sensor, a Bluetooth module, a power circuit and a PCB (printed circuit board); the single chip microcomputer, the falling prediction sensor, the Bluetooth module and the power circuit are integrated on the PCB; the power circuit, the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor are all connected with the single chip microcomputer through cables; the singlechip communicates with the android system mobile phone in a wireless mode through the Bluetooth module; wireless data transmission is carried out between the Android system mobile phone and the upper computer through a Bluetooth technology; the power circuit is respectively connected with the falling prediction sensor, the first step state information acquisition sensor, the second step state information acquisition sensor, the singlechip and the Bluetooth module; the PCB board is installed at human belly, and first attitude information acquisition sensor is installed in human left side ankle department, and second attitude information acquisition sensor is installed in human right side ankle department.
Further, the fall prediction sensor is used for acquiring human body acceleration data, and the first step state information acquisition sensor and the second step state information acquisition sensor are used for acquiring human body angular velocity data.
Further, the fall prediction sensor, the first step information acquisition sensor and the second step information acquisition sensor all specifically adopt MPU6050 sensors.
Furthermore, the circuit design of the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor is the same; the fall prediction sensor comprises the following specific circuits: the LED driving circuit comprises an MPU6050 sensor, a first resistor, a second resistor, a third resistor, a fourth resistor, a fifth resistor, a sixth resistor, a seventh resistor, a first capacitor, a second capacitor, a third capacitor and a first LED; the first resistor and the second resistor are connected in series and then connected with a terminal 23 of an MPU6050 sensor, the third resistor and the fourth resistor are connected in series and then connected with a terminal of an MP U6050 sensor, and the other end of the first resistor and the second resistor after being connected in series and the other end of the third resistor and the fourth resistor after being connected in series are both connected with VCC 3.3; the fifth resistor is connected with the terminal 12 of the MPU6050 sensor; the sixth resistor is connected with a terminal 9 of the MPU6050 sensor; one end of the seventh resistor is connected with the first LED, and the other end of the seventh resistor is connected with VCC 3.3; one end of the first capacitor is connected with VCC3.3, and the other end of the first capacitor is connected with a terminal 18 of the MPU6050 sensor; one end of the second capacitor is connected with the terminal 10 of the MPU6050 sensor, and the other end of the second capacitor is grounded; one end of the third capacitor is connected to terminal 20 of the MP U6050 sensor and the other end of the third capacitor is grounded.
Further, the specific circuit of the bluetooth module includes: the device comprises an HC-08 Bluetooth serial port communication module, an eighth resistor, a ninth resistor, a tenth resistor, an eleventh resistor, a twelfth resistor, a thirteenth resistor and a fourth LED; one end of the eighth resistor and one end of the ninth resistor are both connected with a wiring terminal 34 of the HC-08 Bluetooth serial port communication module, one end of the tenth resistor is connected with a wiring terminal 32 of the HC-08 Bluetooth serial port communication module, and the other end of the tenth resistor is connected with the other end of the ninth resistor; one end of the eleventh resistor is connected with the wiring terminal 31 of the HC-08 Bluetooth serial port communication module, and the other end of the eleventh resistor is connected with the fourth LED; one end of the twelfth resistor is connected with the TXD, and the other end of the twelfth resistor is connected with VCC 3.3; one end of the thirteenth resistor is connected with RXD, and the other end of the thirteenth resistor is connected with VCC 3.3.
Further, the specific circuit of the power supply circuit includes: the LED driving circuit comprises a TP4056A chip, a CS662K chip, a fourteenth resistor, a fifteenth resistor, a sixteenth resistor, a seventeenth resistor, a fourth capacitor, a fifth capacitor, a sixth capacitor, a seventh capacitor, an eighth capacitor, a ninth capacitor, a second LED, a third LED, a first voltage stabilizing diode, a second voltage stabilizing diode, a one-bit fluctuation switch, a battery interface and a micro USB; one end of an eighth capacitor is connected with the terminal 1 of the micro USB, and the other end of the eighth capacitor is grounded; terminal 6 of the TP4056A chip, terminal 1 of the fourteenth resistor, the second LED and the micro USB are connected in series; the terminal of the TP4056A chip, the terminal 1 of the fifteenth resistor, the third LED and the micro USB are connected in series; one end of the seventeenth resistor and terminals 2 and 3 of the TP4056A chip are grounded; the other end of the seventeenth resistor is connected to terminal 1 of the TP4056A chip; one end of a sixteenth resistor is connected with the terminal 1 of the TP4056A chip, and the other end of the sixteenth resistor is connected with the terminals 4 and 8 of the TP4056A chip and the terminal 1 of the micro USB; one end of the ninth capacitor is grounded, and the other end of the ninth capacitor is connected with the terminal 5 of the TP4056A chip; the terminal 2 of the battery interface is grounded, and the terminal 1 of the battery interface is connected with the terminal 5 of the TP4056A chip and the terminal 1 of the one-position toggle switch; one end of the first voltage stabilizing diode, the fourth capacitor and the fifth capacitor after being connected in parallel is connected with a terminal 3 of an C S662K chip and a terminal 2 of a one-bit toggle switch; one end of the second voltage stabilizing diode, the sixth capacitor and the seventh capacitor which are connected in parallel is connected with a terminal 2 of the CS662K chip; the other end of the first voltage stabilizing diode, the fourth capacitor and the fifth capacitor which are connected in parallel is connected with the other end of the second voltage stabilizing diode, the sixth capacitor and the seventh capacitor which are connected in parallel.
The utility model has the advantages that:
the utility model discloses a to the research of human motion action and risk prediction principle of tumbleing, designed a wearing formula prediction system that tumbles based on motion signal sensor, the prediction of tumbleing. The system mainly comprises an upper computer, an Android system mobile phone, a single chip microcomputer, a falling prediction sensor, a first step state information acquisition sensor, a second step state information acquisition sensor, a Bluetooth module, a power circuit and a PCB. Through studying and testing current human skeleton system model, the concrete mounted position of the prediction sensor of tumbleing, first step attitude information acquisition sensor, second step attitude information acquisition sensor has finally been confirmed, and the PCB board that has integrateed singlechip, the prediction sensor of tumbleing, bluetooth module and power supply circuit promptly is installed at human belly, and first step attitude information acquisition sensor is installed in human left side ankle department, and second step attitude information acquisition sensor is installed in human right side ankle department. The utility model discloses a human motion data and gait data are gathered to the sensor to transmit for the singlechip through bluetooth module and handle. The human body acceleration data is acquired through a falling prediction sensor arranged on the abdomen of a human body, and the human body angular velocity data is acquired through a first step state information acquisition sensor and a second step state information acquisition sensor which are respectively arranged on the left ankle and the right ankle of the human body.
The utility model discloses a human motion information and gait information are gathered to the sensor acquisition, and send the host computer through android d cell-phone and carry out support vector machine algorithm processing, the rate of accuracy of tumble prediction reaches more than 90%, and realize functions such as gait parameter and tumble prediction result demonstration at the cell-phone end, make things convenient for the old person to look over motion information at any time, can monitor the user anytime and anywhere, do not receive external influence, and can not invade user's privacy, medical personnel can also give relevant motion and diet suggestion etc. through the motion data of regularly gathering, bring bigger facility and health for the old person, tumble to the guardianship old person, tumble prediction and wisdom endowment development have the significance.
The utility model discloses in, the PCB board that will integrate singlechip, the prediction sensor of tumbleing, bluetooth module and power supply circuit is installed at human belly, first step attitude information acquisition sensor is installed in human left side ankle department, second step attitude information acquisition sensor is installed in human right side ankle department, for subsequent gait characteristic analysis and the analysis of tumble prediction factor correlation provide the motion information, and this kind of wearable equipment does not influence user's daily behavior activity, and it is convenient to use, and the prediction that tumbles is effectual.
Drawings
Fig. 1 is a block diagram of a wearable fall prediction system of the present invention.
Fig. 2 is a circuit diagram of a fall prediction sensor.
Fig. 3 is a circuit diagram of the bluetooth module.
Fig. 4 is a schematic diagram of a power supply circuit.
Fig. 5 is acceleration data collected by the system during normal walking.
Fig. 6 is angular velocity data collected by the system during normal walking.
Fig. 7 is acceleration data collected by the system while the experimenter is seated in the chair and is falling backwards.
Fig. 8 is angular velocity data collected by the system when the experimenter is seated in the chair and falls backward.
Fig. 9 is acceleration data collected by the system while the experimenter was standing, falling forward, and landing on the ground with the knee.
Fig. 10 is angular velocity data collected by the system while the experimenter was standing, falling forward, and landing on the knee.
Fig. 11 is acceleration data collected by the system when the experimenter was allowed to fall forward while standing and was restrained from falling with both hands.
Fig. 12 is angular velocity data collected by the system when the experimenter was allowed to fall forward while standing and was restrained from falling with both hands.
FIG. 13 is acceleration data collected by the system while the experimenter was standing with the bent legs tipped forward.
Fig. 14 is angular velocity data collected by the system while the experimenter was standing with the bent legs tipped forward.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the utility model discloses a wearing formula tumble prediction system mainly includes: the system comprises an upper computer, an Android system mobile phone, a single chip microcomputer, a falling prediction sensor, a first step state information acquisition sensor, a second step state information acquisition sensor, a Bluetooth module, a power circuit and a PCB.
Wherein, singlechip, tumble prediction sensor, bluetooth module and power supply circuit all integrate on the PCB board.
The power circuit, the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor are all connected with the single chip microcomputer through cables. And the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor are all communicated with the singlechip through an IIC bus.
The single chip microcomputer is in wireless communication with the Android system mobile phone through the Bluetooth module.
The Android system mobile phone and the upper computer perform wireless data transmission through a Bluetooth technology.
The power circuit is respectively connected with the falling prediction sensor, the first step state information acquisition sensor, the second step state information acquisition sensor, the single chip microcomputer and the Bluetooth module, and the power circuit is used for supplying power to the falling prediction sensor, the first step state information acquisition sensor, the second step state information acquisition sensor, the single chip microcomputer and the Bluetooth module.
The utility model discloses a to current human skeleton system model study and experiment, the concrete mounted position of the prediction sensor of tumbleing, first step attitude information acquisition sensor, second step attitude information acquisition sensor has finally been confirmed, the PCB board that has integrateed singlechip, the prediction sensor of tumbleing, bluetooth module and power supply circuit promptly is installed at human belly, first step attitude information acquisition sensor is installed in human left side ankle department, second step attitude information acquisition sensor is installed in human right side ankle department. The utility model discloses a human motion data and gait data are gathered to the sensor to transmit for the singlechip through bluetooth module and handle. The human body acceleration data is acquired through a falling prediction sensor arranged on the abdomen of a human body, and the human body angular velocity data is acquired through a first step state information acquisition sensor and a second step state information acquisition sensor which are respectively arranged on the left ankle and the right ankle of the human body.
The utility model discloses in, the prediction sensor of tumbleing, first step attitude information acquisition sensor, second step attitude information acquisition sensor all specifically adopt MPU6050 sensor.
In the present invention, the specific circuit of the fall prediction sensor (the circuit design of the first step information collection sensor and the second step information collection sensor is the same as the circuit design of the fall prediction sensor) is as shown in fig. 2. The circuit mainly comprises an MPU6050 sensor, a first resistor R25, a second resistor R26, a third resistor R27, a fourth resistor R28, a fifth resistor R29, a sixth resistor R30, a seventh resistor R32, a first capacitor C30, a second capacitor C31, a third capacitor C32 and a first LED5, wherein the first resistor R25 and the second resistor R26 are connected in series and then connected with a terminal 23 of the MPU6050 sensor, the third resistor R27 and the fourth resistor R28 are connected in series and then connected with a terminal 24 of the MPU6050 sensor, and the other ends of the first resistor R25 and the second resistor R26 which are connected in series and the other ends of the third resistor R27 and the fourth resistor R28 which are connected in series are connected with VCC 3.3; fifth resistor R29 is connected to terminal 12 of the MPU6050 sensor; the sixth resistor R30 is connected to terminal 9 of the MPU6050 sensor; one end of a seventh resistor R32 is connected with the first LED5, and the other end of the seventh resistor R32 is connected with VCC 3.3; one end of a first capacitor C30 is connected with VCC3.3, and the other end of the first capacitor C30 is connected with the terminal 18 of the MPU6050 sensor; one end of a second capacitor C31 is connected with the terminal 10 of the MPU6050 sensor, and the other end of the second capacitor C31 is grounded; one terminal of a third capacitor C32 is connected to terminal 20 of MPU6050 sensor and the other terminal of the third capacitor C32 is connected to ground. The resistance of the first resistor R25 is 4.7K, the resistance of the second resistor R26 is 120 Ω, the resistance of the third resistor R27 is 4.7K, the resistance of the fourth resistor R28 is 120 Ω, the resistance of the fifth resistor R29 is 120 Ω, the resistance of the sixth resistor R30 is 10K, the resistance of the seventh resistor R32 is 510 Ω, the capacitance of the first capacitor C30 is 0.1 μ F, the capacitance of the second capacitor C31 is 0.1 μ F, and the capacitance of the third capacitor C32 is 10 μ F.
The utility model discloses in, the singlechip specifically adopts STM32F407 series singlechip as system CPU, and the model is STM32F407ZGT6, and this model singlechip interface is abundant and low-power consumption, high performance, and complicated functional requirement can be realized to cool small and exquisite hardware design, can also guarantee that data has very high stability and reliability simultaneously.
The utility model discloses in, bluetooth module specifically adopts HC-08 bluetooth serial ports communication module, and it has advantages such as 80 meters overlength communication distance and ultralow power and small-size, satisfies the design needs completely, is applicable to all Android system mobile communication. The specific circuit of the bluetooth module is shown in fig. 3. The circuit mainly comprises an HC-08 Bluetooth serial port communication module, an eighth resistor R35, a ninth resistor R36, a tenth resistor R37, an eleventh resistor R38, a twelfth resistor R39, a thirteenth resistor R40 and a fourth LED2, wherein one end of the eighth resistor R35 and one end of the ninth resistor R36 are both connected with a terminal 34 of the HC-08 Bluetooth serial port communication module, one end of the tenth resistor R37 is connected with the terminal 32 of the HC-08 Bluetooth serial port communication module, and the other end of the tenth resistor R37 is connected with the other end of the ninth resistor R36; one end of an eleventh resistor R38 is connected with the terminal 31 of the HC-08 Bluetooth serial port communication module, and the other end of the eleventh resistor R38 is connected with a fourth LED 2; one end of a twelfth resistor R39 is connected with TXD, and the other end of the twelfth resistor R39 is connected with VCC 3.3; one end of a thirteenth resistor R40 is connected with RXD, and the other end of the thirteenth resistor R40 is connected with VCC 3.3. The resistance value of the eighth resistor R35 is 470 Ω, the resistance value of the ninth resistor R36 is 10K, the resistance value of the tenth resistor R37 is 10K, the resistance value of the eleventh resistor R38 is 1K, the resistance value of the twelfth resistor R39 is 10K, and the resistance value of the thirteenth resistor R40 is 4.7K.
The utility model discloses when designing power supply circuit, should rationally supply power according to the required voltage of each module, must ensure that the voltage that power supply circuit produced is within the power supply range of every module. Firstly, the working voltage of a single chip microcomputer (STM 32F407ZGT 6) is considered, and the range is 1.8-3.6V; and secondly, the power supply voltage of the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor, namely the MPU6050 sensor can be 2.5V, 3.0V or 3.3V, the power supply voltage required by the Bluetooth module (HC-08 Bluetooth serial port communication module) is 2.0-3.6V, and the voltage required by other devices is about 3.3V. Considering the volume of the system and the electromagnetic interference generated by the power supply, the 3.3V voltage is uniformly used for supplying power to all devices, so that the volume of a system circuit board is reduced, and the electromagnetic interference is reduced.
Because the utility model discloses a wearable equipment, the system needs long-term the use, and the battery should possess characteristics small, capacious, and lithium ion battery is amasss for a short time, longe-lived, little to human harm, consequently, the utility model discloses specifically use the chargeable lithium ion battery of 3.7V for the system power supply, battery capacity is 800 mAh. The specific circuit of the power supply circuit is shown in fig. 4. The circuit mainly comprises a TP4056A chip, a CS662K chip, a fourteenth resistor R42, a fifteenth resistor R43, a sixteenth resistor R44, a seventeenth resistor R45, a fourth capacitor C35, a fifth capacitor C36, a sixth capacitor C37, a seventh capacitor C38, an eighth capacitor C39, a ninth capacitor C40, a second LED3, a third LED4, a first voltage stabilizing diode D2, a second voltage stabilizing diode D3, a one-bit fluctuation switch P7, a battery interface P8 and a micro USB P9. One end of an eighth capacitor C39 is connected with the terminal 1 of the micro USB P9, and the other end of the eighth capacitor C39 is grounded; terminal 6 of the TP4056A chip, terminal 1 of the fourteenth resistor R42, the second LED3 and the micro USB P9 are connected in series; the terminal 7 of the TP4056A chip, the fifteenth resistor R43, the third LED4 and the terminal 1 of the micro USB P9 are connected in series; one end of a seventeenth resistor R45 and terminals 2 and 3 of the TP4056A chip are grounded; the other end of the seventeenth resistor R45 is connected to terminal 1 of the TP4056A chip; one end of a sixteenth resistor R44 is connected with terminal 1 of the TP4056A chip, and the other end of the sixteenth resistor R44 is connected with terminals 4 and 8 of the TP4056A chip and terminal 1 of the mi cro USB P9; one end of a ninth capacitor C40 is grounded, and the other end of the ninth capacitor C40 is connected with the terminal 5 of the TP4056A chip; terminal 2 of battery interface P8 is grounded, and terminal 1 of battery interface P8 is connected with terminal 5 of TP4056A chip and terminal 1 of one-position toggle switch P7; one end of the first voltage-stabilizing diode D2, the fourth capacitor C35 and the fifth capacitor C36 which are connected in parallel is connected with a terminal 3 of the CS662K chip and a terminal 2 of the one-bit toggle switch P7; one end of the second zener diode D3, the sixth capacitor C37 and the seventh capacitor C38 after being connected in parallel is connected with the terminal 2 of the CS662K chip; the other end of the first voltage-stabilizing diode D2, the fourth capacitor C35 and the fifth capacitor C36 which are connected in parallel is connected with the other end of the second voltage-stabilizing diode D3, the sixth capacitor C37 and the seventh capacitor C38 which are connected in parallel. The resistance of the fourteenth resistor R42 is 1K, the resistance of the fifteenth resistor R43 is 1K, the resistance of the sixteenth resistor R44 is 1K, the resistance of the seventeenth resistor R45 is 1K, the capacitance of the fourth capacitor C35 is 10 μ F, the capacitance of the fifth capacitor C36 is 0.1 μ F, the capacitance of the sixth capacitor C37 is 0.1 μ F, the capacitance of the seventh capacitor C38 is 10 μ F, the capacitance of the eighth capacitor C39 is 0.1 μ F, and the capacitance of the ninth capacitor C40 is 0.1 μ F.
The utility model discloses a wearing formula prediction system that tumbles, its theory of operation as follows:
according to the change rule of the acceleration and the angular velocity when the human body moves, the sampling frequency of each sensor is set to be 100Hz, the full-scale range of the acceleration is +/-2 g, the full-scale range of the angular velocity is +/-2000 dps, and the system can acquire data after initialization is completed: firstly, acquiring acceleration data through a falling prediction sensor, and acquiring angular velocity data through a first gait information acquisition sensor and a second step information acquisition sensor; the signals collected by the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor are all transmitted to the singlechip, the signal is processed by the singlechip and is transmitted to the Android system mobile phone in a wireless transmission way through the Bluetooth module, the received data is transmitted to an upper computer in a wireless transmission mode through an Android system mobile phone, the upper computer processes the received data by adopting the existing support vector machine algorithm to obtain a falling prediction result, meanwhile, the tumble prediction result can be sent to the Android system mobile phone in real time, the tumble prediction result and abnormal parameter alarm and the like are displayed through the Android system mobile phone, so that the old can conveniently check the motion data and the tumble prediction result at any time, therefore, medical care personnel can give out relevant exercise and diet suggestions and the like through the exercise data acquired regularly, and great convenience is brought to the old.
The utility model discloses according to the various actions of the prediction principle of tumbleing and among the old person's daily life of analysis, fall into two types with old person's action: one is the Daily activity adl (activities of Daily life); another class is fall behavior fall (fall down). The falling behavior is further specifically divided into: backward when sitting (bsc), forward knee-down when standing (FKL), forward hand-support-down when standing (FOL), and backward bending of the legs when Standing (SDL).
In order to verify the reliability and accuracy of the wearable tumble prediction system of the utility model, 5 experiments are carried out, (1) normal walking is carried out; (2) when sitting on the chair, the chair falls backwards; (3) when standing, the knee falls forwards and the knee touches the ground; (4) when standing, the user falls forwards and is restrained by hands to land; (5) when standing, the legs bend and fall backwards.
(1) The experimenter walks normally or does other normal actions including squatting, jumping, etc. Acceleration and angular velocity data collected by the system under normal walking are shown in fig. 5 and 6.
(2) Acceleration and angular velocity data collected by the system while the experimenter was seated in the chair and fallen backward are shown in fig. 7 and 8.
(3) The acceleration and angular velocity data collected by the system when the experimenter stood down forward and landed with the knees is shown in fig. 9 and 10.
(4) Acceleration and angular velocity data collected by the apparatus when the experimenter stood down forward and restrained the fall with both hands are shown in fig. 11 and 12.
(5) Acceleration and angular velocity data collected by the instrument while bending the legs forward while standing are shown in fig. 13 and 14.
The experimental test results are shown in table 1, and it can be seen from the results that the system can predict daily activities and falling behaviors and effectively distinguish different falling behaviors, the falling prediction accuracy of the system is over 90%, and the accuracy is high.
TABLE 1 test results
| Event(s) | Number of experiments | Predicting accurate number | Prediction accuracy |
| ADL | 50 | 50 | 100％ |
| BSC | 50 | 46 | 92％ |
| FKL | 50 | 45 | 90％ |
| FOL | 50 | 47 | 94％ |
| SDL | 50 | 46 | 92％ |
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the scope of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: it is to be understood that modifications may be made to the above-described arrangements in the embodiments or equivalents may be substituted for some of the features of the embodiments, but such modifications or substitutions do not depart from the spirit and scope of the present invention.
Claims (6)
1. Wearable fall prediction system, characterized in that includes: the system comprises an upper computer, an Android system mobile phone, a single chip microcomputer, a falling prediction sensor, a first step state information acquisition sensor, a second step state information acquisition sensor, a Bluetooth module, a power circuit and a PCB (printed circuit board); the single chip microcomputer, the falling prediction sensor, the Bluetooth module and the power circuit are integrated on the PCB; the power circuit, the falling prediction sensor, the first step state information acquisition sensor and the second step state information acquisition sensor are all connected with the single chip microcomputer through cables; the singlechip communicates with the Android system mobile phone in a wireless mode through the Bluetooth module; wireless data transmission is carried out between the Android system mobile phone and the upper computer through a Bluetooth technology; the power circuit is respectively connected with the falling prediction sensor, the first step state information acquisition sensor, the second step state information acquisition sensor, the singlechip and the Bluetooth module; the PCB board is installed at human belly, and first attitude information acquisition sensor is installed in human left side ankle department, and second attitude information acquisition sensor is installed in human right side ankle department.
2. The wearable fall prediction system of claim 1, wherein the fall prediction sensor is configured to collect human body acceleration data, and the first and second step-wise information collection sensors are configured to collect human body angular velocity data.
3. The wearable fall prediction system of claim 1, wherein the fall prediction sensor, the first step information acquisition sensor, and the second step information acquisition sensor are all MP U6050 sensors.
4. The wearable fall prediction system of claim 1, wherein the fall prediction sensor, the first step-state information acquisition sensor, and the second step-state information acquisition sensor have the same circuit design; the specific circuit of the fall prediction sensor comprises: the LED driving circuit comprises an MPU6050 sensor, a first resistor, a second resistor, a third resistor, a fourth resistor, a fifth resistor, a sixth resistor, a seventh resistor, a first capacitor, a second capacitor, a third capacitor and a first LED; the first resistor and the second resistor are connected in series and then connected with a terminal 23 of the MPU6050 sensor, the third resistor and the fourth resistor are connected in series and then connected with a terminal of the MPU6050 sensor, and the other end of the first resistor, the other end of the second resistor, the other end of the third resistor and the other end of the fourth resistor are connected in series and are connected with VCC 3.3; the fifth resistor is connected with the terminal 12 of the MPU6050 sensor; the sixth resistor is connected with a terminal 9 of the MPU6050 sensor; one end of the seventh resistor is connected with the first LED, and the other end of the seventh resistor is connected with VCC 3.3; one end of the first capacitor is connected with VCC3.3, and the other end of the first capacitor is connected with a terminal 18 of the MPU6050 sensor; one end of the second capacitor is connected with the terminal 10 of the MPU6050 sensor, and the other end of the second capacitor is grounded; one end of the third capacitor is connected to terminal 20 of the MPU6050 sensor and the other end of the third capacitor is grounded.
5. The wearable fall prediction system of claim 1, wherein the specific circuitry of the bluetooth module comprises: the device comprises an HC-08 Bluetooth serial port communication module, an eighth resistor, a ninth resistor, a tenth resistor, an eleventh resistor, a twelfth resistor, a thirteenth resistor and a fourth LED; one end of the eighth resistor and one end of the ninth resistor are both connected with a wiring terminal 34 of the HC-08 Bluetooth serial port communication module, one end of the tenth resistor is connected with a wiring terminal 32 of the HC-08 Bluetooth serial port communication module, and the other end of the tenth resistor is connected with the other end of the ninth resistor; one end of an eleventh resistor is connected with a wiring terminal 31 of the HC-08 Bluetooth serial port communication module, and the other end of the eleventh resistor is connected with a fourth LED; one end of the twelfth resistor is connected with the TXD, and the other end of the twelfth resistor is connected with VCC 3.3; one end of the thirteenth resistor is connected with RXD, and the other end of the thirteenth resistor is connected with VCC 3.3.
6. The wearable fall prediction system of claim 1, wherein the specific circuitry of the power circuit comprises: the LED driving circuit comprises a TP4056A chip, a CS662K chip, a fourteenth resistor, a fifteenth resistor, a sixteenth resistor, a seventeenth resistor, a fourth capacitor, a fifth capacitor, a sixth capacitor, a seventh capacitor, an eighth capacitor, a ninth capacitor, a second LED, a third LED, a first voltage stabilizing diode, a second voltage stabilizing diode, a one-bit fluctuation switch, a battery interface and a micro USB; one end of an eighth capacitor is connected with the terminal 1 of the micro USB, and the other end of the eighth capacitor is grounded; terminal 6 of the TP4056A chip, terminal 1 of the fourteenth resistor, the second LED and the micro USB are connected in series; the terminal of the TP4056A chip, the terminal 1 of the fifteenth resistor, the third LED and the micro USB are connected in series; one end of the seventeenth resistor and terminals 2 and 3 of the TP4056A chip are grounded; the other end of the seventeenth resistor is connected to terminal 1 of the TP4056A chip; one end of a sixteenth resistor is connected with the terminal 1 of the TP4056A chip, and the other end of the sixteenth resistor is connected with the terminals 4 and 8 of the TP4056A chip and the terminal 1 of the micro USB; one end of the ninth capacitor is grounded, and the other end of the ninth capacitor is connected with the terminal 5 of the TP4056A chip; the terminal 2 of the battery interface is grounded, and the terminal 1 of the battery interface is connected with the terminal 5 of the TP4056A chip and the terminal 1 of the one-position toggle switch; one end of the first voltage stabilizing diode, the fourth capacitor and the fifth capacitor which are connected in parallel is connected with a terminal 3 of the CS662K chip and a terminal 2 of the one-bit toggle switch; one end of the second voltage stabilizing diode, the sixth capacitor and the seventh capacitor which are connected in parallel is connected with a terminal 2 of the CS662K chip; the other end of the first voltage stabilizing diode, the fourth capacitor and the fifth capacitor which are connected in parallel is connected with the other end of the second voltage stabilizing diode, the sixth capacitor and the seventh capacitor which are connected in parallel.
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| CN202123066179.5U CN217118429U (en) | 2021-12-08 | 2021-12-08 | Wearable tumble prediction system |
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