CN110517450A - Wearable device and fall detection method based on NB-IoT - Google Patents

Abstract

The application discloses a wearable device and a fall detection method based on the narrowband Internet of Things. The set fall detection module can detect the user's fall behavior, and the alarm information corresponding to the fall behavior together with the location information obtained by the positioning module can be passed through NB‑ The IoT communication module is sent to the service platform or a designated communication terminal, which can achieve the purpose of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

Description

Wearable device and fall detection method based on NB-IoT

technical field

This application relates to the field of smart devices, in particular to a wearable device based on narrowband Internet of Things and a fall detection method.

Background technique

Countries all over the world are facing the severe challenge of a sharp increase in global senile chronic diseases caused by population aging. By 2050, the aging population in my country will reach 30% of the total population, which has brought certain pressure to families and society. With the intensification of the aging trend of the population, the home care of the elderly has become a concern of all walks of life. Coupled with the flow of modern young working population, the elderly and their children live separately, which weakens the function of family care for the elderly, and the phenomenon of "empty nest elderly" appears, which makes the aging problem "worse". According to statistics, "empty-nest elderly" account for half of the 167 million elderly people over 60 years old in my country. It has aroused the great attention of the government and all walks of life.

Due to the sharp increase in the number of elderly people, the demand for self-health monitoring and preventive medicine is increasing. Wearable devices based on Internet of Things (Internet of Things, referred to as IoT) technology have been attracting attention in recent years because of its potential to alleviate the problems caused by the aging population. Strain on healthcare systems due to globalization and increase in chronic diseases. At present, there are already examples of applications at home and abroad that integrate wearable medical health devices and mobile applications with telemedicine systems to build a medical Internet of Things system. Through wearable devices combined with advanced monitoring technologies, various health indicators, Vital signs and health status, etc., effectively reduce the overall cost of prevention and monitoring, and are considered to be reliable tools for long-term health monitoring.

However, most of the current wearable medical and health devices at home and abroad are based on communication methods such as Bluetooth and WIFI, which have many limitations, such as: they cannot work independently, complex configurations, potential safety hazards, and high power consumption. In addition, this type of wearable medical devices When health equipment monitors health indicators, vital signs, and health status, there are often problems such as inaccurate detection. For example, the behavior of falling cannot be accurately detected, thereby delaying or even missing the opportunity for medical treatment. These defects have reduced users' willingness to use to a certain extent and hindered the promotion and application of wearable medical and health devices.

Contents of the invention

In view of the shortcomings of the prior art described above, the purpose of this application is to disclose a wearable device and a fall detection method based on NB-IoT.

The present application discloses a wearable device based on the narrowband Internet of Things, including: a microprocessor and an NB-IoT communication module connected to the microprocessor, a positioning module, and a fall detection module; the positioning module is used to obtain position information ; The fall detection module is used to detect whether the user of the wearable device has a fall behavior and sends a fall detection signal after detecting that the user has a fall behavior; the microprocessor is used to send a fall detection signal according to the fall detection module. The fall detection signal generates an alarm message, and sends the alarm message together with the location information acquired by the positioning module to the service platform or a designated communication terminal through the NB-IoT communication module.

Optionally, the fall detection module is a three-axis acceleration sensor, and the three-axis acceleration sensor detects whether the user has fallen by detecting the acceleration signals of the wearable device in the three-axis directions in the space Cartesian coordinate system In the space Cartesian coordinate system, including X-axis, Y-axis, and Z-axis, wherein, the X-axis represents the user's front-back direction, the Y-axis represents the user's vertical direction, and the Z-axis represents the user's According to the relationship between the gravitational acceleration and its components in the X, Y, and Z axes of the triaxial acceleration, the included angles between the X, Y, and Z axes and the gravitational acceleration are respectively calculated.

Optionally, the three-axis acceleration sensor sends out a fall detection signal based on four judgment criteria when detecting whether the user has fallen.

Optionally, the manner in which the three-axis acceleration sensor sends a fall detection signal based on four judgment criteria includes:

Based on the first judgment basis, detect whether the wearable device has a weightlessness behavior;

Based on the second judgment basis, detect whether there is an impact behavior after the weightlessness behavior of the wearable device;

Based on the third judgment basis, detecting whether the wearable device is in a stable state after the impact behavior; and

Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold.

Optionally, the triaxial acceleration sensor can also detect the severity level of the user's fall behavior based on four judgment criteria and send out a fall detection signal corresponding to the severity level of the fall behavior.

Optionally, the wearable device further includes a temperature and humidity sensor and/or a heart rate sensor connected to the microprocessor.

Optionally, the wearable device further includes: a help button connected to the microprocessor, the help button is used to send a help request after being triggered; a voice communication module, the voice communication module is connected to the microprocessor The processor is connected, and the microprocessor controls the voice communication module to call the designated contact when receiving the help request from the help button.

The present application further discloses a fall detection method applied to a wearable device based on narrowband Internet of Things, wherein the wearable device includes: a microprocessor and an NB-IoT communication module connected to the microprocessor, Positioning module and fall detection module; Described fall detection method comprises the steps:

Use the fall detection module to detect the acceleration signals of the wearable device in the three-axis directions in the space Cartesian coordinate system, and calculate the acceleration signals according to the component relationship between the acceleration of gravity and the X, Y, and Z axes of the three-axis acceleration. The angle between the X, Y, and Z axes and the acceleration of gravity, so as to determine whether the user of the wearable device has fallen, and send a fall detection signal after detecting that the user has fallen; The space Cartesian coordinate system includes an X axis, a Y axis, and a Z axis, wherein the X axis represents the user's front and rear direction, the Y axis represents the user's vertical direction, and the Z axis represents the user's left and right direction; and

Utilize the microprocessor to generate an alarm message according to the fall detection signal sent by the fall detection module, and send the alarm message together with the location information acquired by the positioning module to the service platform or the specified location through the NB-IoT communication module communication terminal.

Optionally, the triaxial acceleration sensor is based on four judgment criteria to detect whether the user has fallen, and the method includes:

Based on the first judgment basis, detect whether the wearable device has a weightlessness behavior;

Based on the second judgment basis, detect whether there is an impact behavior after the weightlessness behavior of the wearable device;

Based on the third judgment basis, detecting whether the wearable device is in a stable state after the impact behavior; and

Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold.

The application also discloses a safety monitoring service system, including: a wearable device, a service center, and a monitoring platform.

The wearable device and fall detection method based on the narrowband Internet of Things disclosed in this application can detect the user's fall behavior through the set fall detection module, and the alarm information corresponding to the fall behavior together with the location information obtained by the positioning module can be passed through NB-IoT The communication module sends it to the service platform or designated communication terminal, which can realize the purpose of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

Description of drawings

FIG. 1 is a structural block diagram of an implementation of a wearable device based on NB-IoT of the present application.

FIG. 2 shows the system architecture of the NB-IoT applied to the wearable device of the present application.

Fig. 3 is a schematic diagram showing the relationship between the three-axis acceleration component and the inclination angle in the embodiment in which the fall detection module in Fig. 1 uses a three-axis acceleration sensor.

Figure 4 shows the schematic diagram of the X-axis based measurement in Figure 3.

FIG. 5 is a structural block diagram of another implementation of a wearable device based on narrowband Internet of Things of the present application.

FIG. 6 is a schematic flowchart of an embodiment of the fall detection method of the present application.

FIG. 7 is a block diagram of the safety monitoring service system of the present application.

Detailed ways

The implementation mode of the present application is illustrated by specific specific examples below, and those who are familiar with this technology can easily understand other advantages and effects of the present application from the content disclosed in this specification.

In the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the application. It is to be understood that other embodiments may be utilized and compositional and operational changes may be made without departing from the spirit and scope of the present disclosure. The following detailed description should not be considered limiting, and the scope of the embodiments of the present application is only defined by the claims of the patent of the present application. The terminology used herein is for describing particular embodiments only and is not intended to limit the application.

Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well unless the context indicates otherwise. It should be further understood that the terms "comprising", "comprising" indicate the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not exclude one or more other features, steps, operations, The existence, occurrence or addition of an element, component, item, species, and/or group. The terms "or" and "and/or" as used herein are to be construed as inclusive, or to mean either one or any combination.

Please refer to FIG. 1 , which is a structural block diagram of a wearable device based on narrowband Internet of Things in an implementation of the present application.

Wearable devices are portable terminal devices that can be worn on people (including some animals) and have certain functions. Commonly, mainstream product forms include wrist-supported watches (including watches, bracelets, wristbands, etc.), feet-supported shoes (including shoes, socks or other products worn on the legs in the future), Glasses supported by the head (including glasses, helmets, headbands, etc.), as well as various non-mainstream product forms such as smart clothing, school bags, crutches, and accessories. This type of wearable device has widely appeared in people's daily life and is favored by more and more users.

In some examples, some wearable devices are based on communication methods such as Bluetooth and WIFI, which have many limitations, such as: not working independently, complex configuration, potential safety hazards, high power consumption, etc., which need to be established with smart terminals such as mobile phones Connect to use.

Generally, wearable devices can also detect the physiological characteristics or motion characteristics of the wearable user. When such wearable devices monitor health indicators, vital signs, and health status, there are often problems such as inaccurate detection. For example, the behavior of falling cannot be accurately detected, thereby delaying or even missing the opportunity for medical treatment. These defects reduce users' willingness to use to a certain extent and hinder the promotion and application of wearable devices.

To this end, the present application discloses a wearable device based on narrowband internet of things.

As shown in FIG. 1 , the wearable device includes: a microprocessor 11 , an NB-IoT communication module 12 , a positioning module 13 and a fall detection module 14 .

The microprocessor 11 can be, for example, a single-chip microcomputer as a control center of the wearable device.

Single-chip microcomputer is a kind of integrated circuit chip, which adopts the VLSI technology to integrate the central processing unit CPU with data processing capability, random access memory RAM, read-only memory ROM, various I/O ports and interrupt system, timer/counter and other functions Integrated together to form a small but perfect microcomputer system. Taking the STM32 series microcontroller as an example, it allows users to develop freely, and it has the characteristics of high performance, good real-time performance, digital signal processing, low power consumption, high integration and easy development.

Among the wireless transmission technologies, ZigBee technology, WIFI technology, Bluetooth (Blue Tooth) technology, etc. are relatively common.

ZigBee is a short-distance, low-speed wireless communication technology with an open global standard. The technology's low cost, low power consumption, low latency, and low duty cycle characteristics make it ideal for many industrial applications. The ZigBee protocol provides 128-bit AES encryption. In addition, the technology also supports Mesh networks, allowing network nodes to be connected together through multiple paths. The ZigBee Alliance recently standardized the technology, hoping to make connections more compatible and versatile. Currently, all ZigBee devices cannot communicate directly with other ZigBee devices from different manufacturers. At the same time, ZigBee has shortcomings such as network coverage, performance problems, and short communication distances.

WIFI is currently the most widely used wireless network transmission technology, and almost all smart phones, tablet computers and notebook computers support WIFI Internet access. The currently used WIFI is based on the IEEE802.11n wireless standard, the data transmission rate reaches 300Mbps, and the throughput is close to 100M. Although WIFI has fast transmission and wide penetration rate, it also has its own technical disadvantages. Its biggest problem is very low security and weak wireless stability; high power consumption is also one of its weaknesses. These weaknesses have caused its application to be restricted. limit.

The power consumption and cost of Bluetooth are between WIFI and ZigBee, but the transmission distance is the shortest. It belongs to a point-to-point, short-distance transmission communication method. Because it is transmitted between mobile devices or short distances, Bluetooth products will provide Some more personal experience, such as Bluetooth headsets, Bluetooth speakers, etc., but the shortcomings of its short transmission distance limit its application.

With the rapid development of science and technology, a narrowband Internet of Things (Narrow Band Internet of Things, which may be referred to as NB-IoT for short) based on a cellular structure is gradually established.

Narrowband IoT is an important branch of IoT, which is built on cellular networks. With the rapid development of science and technology, NB-IoT based on cellular structure is gradually built. NB-IoT only consumes about 180kHz bandwidth, and can be directly deployed on GSM network, UMTS network or LTE network to reduce deployment costs and achieve smooth upgrades. . NB-IoT has many advantages such as wide coverage, multiple connections, carrier-level QoS, operator support, and fast promotion, making it the communication technology that has attracted the most attention at present.

The NB-IoT is a type of the Internet of Things, and the system architecture of the NB-IoT is shown in FIG. 2 .

As shown in Figure 2, the NB-IoT system includes: NB-IoT terminal (UE), wireless network side, IoT core network, IoT platform, and application server.

The NB-IoT terminal can be connected to the base station through the air interface.

The wireless network side includes two networking methods: one is the integrated wireless access network, which includes 2G, 3G, 4G and NB-IOT wireless networks. The other is NB-IOT new construction, which is mainly responsible for air interface access processing, cell management and other related functions, and connects with the IOT core network through the interface, and forwards the non-access layer data to high-level network elements for processing.

The IoT core network is responsible for interacting with the non-access layer of NB-IoT terminals, and forwards IOT service-related data to the IOT platform for processing.

NB-IoT platform, the current platform mainly includes telecom platform, mobile platform and Huawei platform.

The application server is the final gathering point of IOT data, and performs data processing and other operations according to the needs of customers.

As shown in Figure 2, the NB-IOT terminal at the perception layer is connected to the E-node B base station at the network layer through the air interface. The NB-IOT base station is responsible for functions such as access processing and cell management, and is connected to the IOT controller through the MI interface. The IOT controller is responsible for interacting with the non-access layer of the NB-IOT terminal, and forwards IOT service-related data to the IOT platform for processing. The IOT platform aggregates the IOT data obtained from various access networks and forwards them to the corresponding application layer according to different types. Business applications are the final gathering point of IOT data, and perform data processing and other operations according to customer needs.

The NB-IOT security architecture can be divided into three layers: perception layer, transport layer and processing layer.

The first layer is the security system of the NB-IOT perception layer, the goal is to realize the safe collection of data from the physical world, as well as the safe exchange of data and transport layers. Security features include the following aspects: privacy protection and border protection of sensing nodes, identity authentication of base stations in sectors by sensing nodes, secure routing selection during handover of mobile nodes, and establishment and management of cryptographic systems.

The second layer is the security system of the NB-IOT transport layer. The goal is to realize the safe and reliable transmission of data between the perception layer and the processing layer. It specifically includes the following security features: identity authentication for massive node access, massive data Authentication during the transmission process, intrusion detection of the transmission system, and the establishment of a secure communication protocol with the perception layer and the processing layer.

The third layer is the security system of the NB-IOT processing layer. The goal is to achieve data security, effective management and application, including the following security features: disaster recovery backup for massive data, user access control for various applications, System protection intrusion detection, security audit of user behavior, and verification of mass data interaction.

NB-IOT technology has the following characteristics:

Wide coverage:

NB-IOT adopts a narrow-band design method, uploading control commands in the uplink, and issuing control commands in the downlink. The uplink uses 3.75KHz and 15KHz subcarrier spacing, and the downlink uses 15KHz subcarrier spacing. The minimum transmission bandwidth of NB-IOT terminals is 3.75KHz. The uplink is increased by 20dB, the communication capability is greatly enhanced, and the coverage area is increased by 100 times, which can meet the coverage requirements of remote areas. A small number of subcarriers and retransmission mechanisms are used between the base station and NB-IOT terminals to improve the successful reception rate of the receiving end, meet the deep coverage requirements of basements and underground garages, and achieve wide coverage.

Low power consumption:

NB-IOT is mainly aimed at low-frequency and low-frequency sub-services. By simplifying the wireless protocol and shortening the transmission/reception time and other measures, it can achieve ultra-long standby time, and the battery life can reach 10 years. Through PSM and eDRX technology, paging optimization technology, the valid time of system information is extended to 24 hours.

low cost:

NB-IOT supports three deployment methods: independent deployment, in-band protection deployment, and in-band deployment, making full use of spectrum resources. Operators can deploy NB-IOT networks based on existing base stations and covered frequency bands. Narrow bandwidth, low power consumption, and low baseband complexity make NB-IOT chips and modules low-cost. As the technology matures, the chip price will be as low as $1, and the module cost will be below $5.

Large scale connections:

NB-IOT supports multiple connections, and one sector can support 100,000 devices to access at the same time, which is 50 to 100 times higher than the existing wireless technology, and can meet the needs of interconnection and intercommunication. Compared with LoRa, NB-IOT is a licensed frequency band, and its time slot synchronization protocol is optimal for QoS, especially suitable for applications that require low latency and high data rates. Compared with GSM, NB-IOT has Advantages of lower power consumption, low cost, and wide coverage.

In this embodiment, the wearable device of the present application is based on the narrowband Internet of Things, and provides an NB-IoT communication module 12. The NB-IOT module 12 is a communication module based on NB-IOT technology. Compared with the aforementioned NB-IOT module 12 has the characteristics of low power consumption, low cost, wide-area transmission, massive access, support for a large number of nodes, support for retransmission mechanism, low complexity, fast, reliable, and safe, and can be applied to Various complex environments, with superior performance and battery life.

In practical applications, the NB-IoT communication module 12 has built-in data transmission protocols such as UDP/CoAP, and integrates the AT command package of the basic communication suite that supports access to the OneNET platform. The recommended power supply voltage is 3.8V, and it can automatically search for frequency and frequency band selection It can be set by AT command corresponding to the specific module version and operator type, supports built-in or external SIM card, and can upgrade the firmware version through the serial port.

The NB-IoT communication module 12 has three working modes: Active mode, Standby mode and Deep-Sleep mode.

When the NB-IoT communication module 12 works in Active mode, all functions of the module are available, all processors are running normally, and the radio can perform normal transmission and reception. In Active mode, the module can switch to Standby mode and Deep-Sleep working mode.

When the NB-IoT communication module 12 works in the Standby mode, all processors are not running, but all peripherals can be activated, and the system clock is also valid, and the power consumption can be effectively reduced by controlling the clock and power. The Standby mode is entered when all processors are executing wait-for-interrupt instructions.

When the NB-IoT communication module 12 works in Deep-Sleep mode, only the 32KHz real-time clock (Real-TimeClock, RTC) works normally, which means that the module can switch to Active mode through the RTC interrupt or external interrupt of the peripheral device using the real-time clock . All processors are set to Deep-Sleep mode and execute waiting for interrupt instructions.

The above three working modes complement each other, providing guarantee for the low-power operation of the module and meeting the low-power communication requirements.

The wearable device based on NB-IoT disclosed in this application needs to implement functions such as module drive and control, network configuration, resource configuration, terminal keep-alive, data upload, and downlink command processing. Based on the successful access of the device to the NB-IoT.

The process of connecting wearable devices to NB-IoT may include but is not limited to: first power on the NB-IoT communication module, and then judge whether the microprocessor can communicate normally with the NB-IoT communication module through AT commands, and if so, then Continue to query the SIM card number and the current network signal value of the NB-IoT. If the signal is good, it can be attached to the NB-IoT through the NB-IoT communication module. After confirming that the SIM card dedicated to the NB-IoT has activated the relevant communication functions Query the network activation status. If it is confirmed that the network access is successful, at this time, the NB-IoT communication module can perform subsequent operations such as reporting data information and issuing instructions.

The positioning module 13 is used to obtain the location information of the wearable device (ie, the user).

In some embodiments, the positioning module 13 may include a satellite positioning module and/or a network positioning module.

The satellite positioning module can be, for example, any one or a combination of GPS (Global Positioning System, Global Positioning System) module, Beidou positioning module, Galileo positioning module, and Glonass positioning module. For example, the satellite positioning module can be a GPS+Beidou dual-mode positioning module, which can support both the GPS satellite positioning system and the Beidou satellite positioning system. In practical applications, it can support two sets of satellite positioning systems to run on wearable devices at the same time, achieving wider application range and more accurate positioning.

The network positioning module may be, for example, a base station positioning module, a WiFi positioning module, a Bluetooth positioning module, and the like.

Among them, the base station positioning service provided by the base station positioning module is also called the mobile location service (Location Based Service, referred to as LBS), which obtains the location information (latitude and longitude coordinates) of the mobile terminal user through the network of the telecom mobile operator, and displays it on the electronic map superior.

The WiFi positioning provided by the WiFi positioning module is based on a fixed WiFi MAC address, through the collected location of the WiFi hotspot, and then accesses the positioning service on the network to obtain the latitude and longitude coordinates.

The Bluetooth positioning provided by the Bluetooth positioning module is based on the RSSI (Received Signal Strength Indication, signal field strength indication) positioning principle, which uses several Bluetooth LAN access points installed indoors to maintain the network as a basic network connection mode based on multiple users , and ensure that the Bluetooth LAN access point is always the master device of this piconet, and then obtain the user's location information by measuring the signal strength.

The fall detection module 14 is used to detect whether the user of the wearable device has a fall behavior.

In this embodiment, the fall detection module 14 may use a three-axis acceleration sensor, which is used to obtain acceleration signals of the user in three directions to detect whether the user has fallen.

In practical applications, the three-axis acceleration sensor detects whether the human body has fallen or not by detecting the acceleration signals of the wearable device in the three-axis directions in the space Cartesian coordinate system. Specifically, as shown in FIG. 3 , in the space Cartesian coordinate system, it includes an X axis, a Y axis, and a Z axis, wherein, the X axis represents the front and back direction of the human body, the Y axis represents the vertical direction of the human body, and the Z axis represents the vertical direction of the human body. Indicates the left-right direction of the human body. The three-axis acceleration sensor is designed based on the principle of gravitational acceleration. It measures the acceleration of the X, Y, and Z axes when it is at rest, and according to the components of the acceleration of gravity and its components between the X, Y, and Z axes of the three-axis acceleration Respectively calculate the included angles between the X, Y, and Z axes and the acceleration of gravity, so as to obtain the included angle of the system.

Take the measurement of the X-axis inclination as an example, as shown in Figure 4, because the direction of the gravitational acceleration g is vertical and horizontal, so when the space plane is not horizontal, that is, when the gravitational acceleration g is not perpendicular to the space plane, the gravitational acceleration g is at this The projection of the space plane is not equal to 0. The gravitational acceleration g on the axis is sensed by the triaxial acceleration sensor, and the plane inclination angle can be obtained through conversion. The relationship between the triaxial acceleration sensor output and the acceleration of gravity g is:

sinα＝a _X /g Formula 1

Wherein, in Formula 1, a _X is the acceleration value output by the acceleration sensor in the X-axis.

By analogy, for the Y-axis inclination and the Z-axis inclination, the respective relationship between the output of the three-axis acceleration sensor and the acceleration of gravity g is:

sinβ=a _Y /g Formula 2

sinγ=a _Z /g formula 3

Among them, in formula 2, a _Y is the acceleration value output by the acceleration sensor in the Y axis, and in formula 3, a _Z is the acceleration value output by the acceleration sensor in the Z axis

From the above formula 1, formula 2 and formula 3, we can get:

α=arcsin(a _X /g) Formula 4

β=arcsin(a _Y /g) Formula 5

γ=arcsin(a _z /g) Formula 6

The three-axis acceleration sensor sends a fall detection signal based on four judgment criteria when detecting whether the user has fallen. In some implementations, the manner in which the three-axis acceleration sensor sends a fall detection signal based on four judgment criteria may specifically include:

Based on the first judgment criterion, it is detected whether the wearable device has a weightlessness behavior.

Based on the second judgment basis, it is detected whether an impact behavior occurs after the weightlessness behavior of the wearable device.

Based on the third judgment basis, it is detected whether the wearable device is in a steady state after the impact behavior.

Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold.

The inventors of the present application can obtain the acceleration change characteristics of the wearable device during the fall process of the human body through the study of the fall scene.

In practical application scenarios, a certain weightlessness phenomenon will occur at the beginning of the human fall. During the free fall process, this weightlessness phenomenon will be more obvious, and the vector sum of the acceleration will be reduced to close to 0g, and the duration is related to the height of the free fall. . For a general fall, although the weightlessness phenomenon will not be as obvious as that of a free fall, the vector sum of the acceleration will also be less than 1g (usually the vector sum of the acceleration should be greater than 1g). Therefore, detecting whether the wearable device has weightlessness behavior can be used as the first basis for judging the fall state. In practical applications, it can be detected by the Free_Fall interrupt in the three-axis acceleration sensor.

After weightlessness, when the human body falls, it may collide with the ground or other objects. This kind of collision is reflected in the acceleration, that is, it will produce a large impact on the acceleration curve. This shock can be detected by the Activity interruption in the triaxial acceleration sensor. Therefore, after the Free_Fall interrupt, the subsequent Activity interrupt is the second basis for judging the fall state.

In addition, after the human body falls and hits, it usually does not get up immediately, and there will be a short-term static state (if the person is unconscious due to the fall, it may even be a long-term static state). This kind of static state is reflected in the acceleration, that is, there will be a period of stability on the acceleration curve. This static state can be detected by the Inactivity interrupt in the triaxial acceleration sensor. Therefore, the Inactivity interruption after the Activity interruption is the third basis for judging the falling state.

In addition, after the actual fall, the human body may also turn over, so the direction of the human body will be different from the original static standing posture (initial state). This makes the triaxial acceleration data in the static state after the fall different from the triaxial acceleration data in the initial state. Assuming that the wearable device configured with the triaxial acceleration sensor is fixed on a certain part of the user (ie, the measured human body), the triaxial acceleration data in the initial state can be considered known (for example, the initial The state is: X axis 0g, Y axis -1g, Z axis 0g). The triaxial acceleration sensor reads the triaxial acceleration data after Inactivity is interrupted, and compares it with the triaxial acceleration data in the initial state. For example, the direction of the gravitational acceleration changes from -1g on the Y axis to 1g on the Z axis, which indicates that a fall has occurred. Therefore, the fourth basis for fall detection is that the three-axis acceleration data in the static state after the fall has changed from the three-axis acceleration data in the initial state, and the vector change exceeds a certain threshold. In practical application, the range of the threshold value may be 0.4g to 0.7g. In some embodiments, the threshold value may be, for example, 0.5g. Certainly, the threshold value set above is only an exemplary illustration, but it is not limited thereto. In other embodiments, the threshold value can still be changed in other ways, for example: 0.4g, 0.45g, 0.55g, 0.6g, etc.

The above four judgment bases are combined to form the entire fall detection algorithm, which can give an alarm for the fall state. Of course, it should also be noted that the time interval between each interruption should be within a reasonable range. For example, unless you fall from a high place with a large drop, the time interval between Free_Fall interruption (weightlessness) and Activity interruption (impact) will not be very long. Also, under normal circumstances, the time interval between Activity interruption (impact) and Inactivity interruption (rest) is not very long. Of course, the detection threshold and time parameters of related interrupts can also be flexibly set according to needs. In addition, if the fall caused serious consequences, for example, causing a person's coma, then the human body will remain still for a longer period of time, and this state can still be detected through Inactivity interruption. That is to say, if it is found that the Inactivity state is maintained for a long time after the fall, a serious alarm can be given again.

When the three-axis acceleration sensor detects that the user of the wearable device has fallen based on the aforementioned four judgment criteria, a fall detection signal is generated and sent to the microprocessor 11.

Based on the above four judgments, in order to obtain the effectiveness of detecting whether the user has fallen, taking the first judgment as an example, using the Free_Fall interrupt in the three-axis acceleration sensor to detect when the human body just begins to fall and lose weight duration, in order to avoid misjudgment, delayed judgment, non-judgment, etc. In the lower parts of the user (for example, ankles, running shoes, etc.), when based on the first judgment basis, when using the Free_Fall interrupt in the three-axis acceleration sensor to detect, or directly cannot detect the human body The weightlessness state at the beginning of the fall, or the detected duration of the weightlessness at the beginning of the fall of the human body is so short that it is difficult to make an effective judgment based on the duration. Therefore, in general, the wearable device of the present application is suggested to be configured on the user's upper torso (above the waist), such as the wrist, waist, chest, neck, head, etc., so as to obtain a better fall detection effect.

Of course, as mentioned above, in this application, the three-axis acceleration sensor can not only detect whether the user of the wearable device has fallen based on the four judgment criteria, but also based on the parameters of each of the four judgment criteria. Set and establish severity levels for falls. Taking the third judgment basis as an example, different settings can be made to the duration of the Inactivity interruption in the three-axis acceleration sensor. For example, it can be set that when the duration of the Inactivity interruption in the three-axis acceleration sensor is within a certain When it is within a threshold, it can be determined that the severity level of the fall behavior is level one, and when the duration of the Inactivity interruption in the three-axis acceleration sensor exceeds the threshold value, the severity level of the fall behavior can be determined It is a second grade that is more serious than the previous grade one. The above is only an example, and in other examples, similar settings can be made for other judgment criteria, so as to take appropriate measures by setting the severity level of the falling behavior.

The triaxial acceleration sensor can also detect the severity level of the user's fall behavior based on four judgment criteria and send out a fall detection signal corresponding to the severity level of the fall behavior. For example, when it is detected that the severity level of the falling behavior of the user is one level, a first fall detection signal corresponding to the severity level of the falling behavior is sent. When it is detected that the severity level of the falling behavior of the user is two levels, a second fall detection signal corresponding to the severity level of the falling behavior is sent.

The triaxial acceleration sensor provided in this application has the advantages of high resolution, wide measurement range, and direct access through the interface, etc., and its small size is more suitable for application in wearable devices.

In this embodiment, the fall detection module 14 is illustrated by taking a three-axis acceleration sensor as an example, but it is not limited thereto. In other embodiments, the fall detection module 14 can also use other sensors, for example, in In some embodiments, the fall detection module 14 may be implemented by using an inclination sensor. Take the common gyroscope as an example. The principle of the gyroscope is that the direction pointed by the rotation axis of a rotating object will not change when it is not affected by external force. According to this characteristic, the direction of the object can be obtained. The gyroscope measures the angular velocity of the object's motion. By integrating the angular velocity, the deflection angle value of the gyroscope can be obtained, that is, the inclination angle information of the object can be obtained. Gyroscopes provide accurate signals such as level, position, velocity, and acceleration.

The microprocessor 11 can be connected with the NB-IoT communication module, the positioning module and the fall detection module, and is used as an information processing device of the wearable device.

Microprocessor 11 may include a microcontroller, one or more general purpose microprocessors, one or more application specific processors (ASICs), one or more field programmable logic arrays (FPGAs), or any combination thereof.

Taking the single-chip microcomputer as an example, the single-chip microcomputer is a kind of integrated circuit chip. The central processing unit CPU with data processing capability, random access memory RAM, read-only memory ROM, various I/O ports, interrupt system, timing Functions such as counter/counter are integrated together to form a small but complete microcomputer system. Taking the STM32 series microcontroller as an example, it allows users to develop freely, and it has the characteristics of high performance, good real-time performance, digital signal processing, low power consumption, high integration and easy development.

The wearable device of the present application may also include a memory 15, which is used to store at least one program that can execute any one or more of the above methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.

The microprocessor 11 is operatively coupled with the memory 15 . More specifically, the processor may execute programs stored in memory and/or non-volatile storage to perform operations in the data processing platform.

In this embodiment, the three-axis acceleration sensor can detect whether the user has fallen on the basis of four judgment criteria and send a fall detection signal to the microprocessor 11 after detecting that the user has fallen. 11 An alarm message can be generated according to the fall detection signal sent by the fall detection module 14, and the alarm message together with the location information acquired by the positioning module 13 can be sent to the service platform or a designated communication terminal through the NB-IoT communication module 12. It can be seen from this that using the wearable device of this application can achieve the goals of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

In addition, please refer to FIG. 5 , which shows a structural block diagram of another embodiment of the wearable device of the present application. As shown in FIG. 5 , the wearable device of the present application may also have other changes according to actual application scenarios.

The wearable device of the present application may also include a temperature and humidity sensor 16, which is connected to the microprocessor 11 for obtaining temperature and humidity data of the environment where the wearable device is located. In practical applications, the temperature and humidity sensor 16 can sense the temperature and humidity changes in the surrounding environment in real time, and display the obtained temperature and humidity data in real time or notify the user in other ways by the microprocessor 11 accordingly. Moreover, if there is a large change in the ambient temperature and humidity within a relatively short period of time, the microprocessor 11 can send an early warning to the user accordingly.

In some examples, the temperature and humidity sensor 16 can be, for example, the STH20 digital temperature and humidity sensor launched by Sensirion, with full-scale calibration and two-wire digital interface, which can be directly connected with the microprocessor 11, greatly shortening the development time and simplifying the peripheral circuit. And reduce research and development costs, with high reliability and stability.

The wearable device of the present application may also include a heart rate sensor 17, and the heart rate sensor 17 is connected to the microprocessor 11 for obtaining the heart rate data of the user of the wearable device. In practical applications, the heart rate sensor 17 can detect the user's heart rate data in real time, and the obtained heart rate data can be displayed in real time or the microprocessor 11 can notify the user accordingly in other ways. Moreover, if it is detected that the heart rate of the user exceeds the preset threshold, the microprocessor 11 can send an early warning prompt to the user accordingly.

In some examples, the heart rate sensor 17 can use a MAX30102 module, which is a heart rate oximetry module integrated with pulse oximetry detection and heart rate detection. The MAX30102 module integrates a red LED and an infrared LED, a photodetector, an optical device, and low-noise electronics with ambient light suppression. MAX30102 uses a 1.8V power supply and an independent 5.0V power supply for internal LEDs. It is used in wearable devices for heart rate and blood oxygen collection and detection, and can be worn on fingers, earlobes and wrists. The standard I ² C compatible communication interface can transmit the collected values to the single chip microcomputer for calculation of heart rate and blood oxygen. also. The MAX30102 module can also be turned off by software, and the standby current is close to zero, so that the power supply can always maintain the power supply state. In addition, the MAX30102 module integrates a glass cover, which can effectively eliminate external and internal light interference, and has optimal reliability.

MAX30102 itself integrates a complete light-emitting LED and driving part, light sensing and AD conversion part, environmental interference elimination and digital filtering part, and only needs to configure the digital interface, which greatly reduces the design burden. You only need to use the microcontroller to read the FIFO of MAX30102 itself through the hardware I ² C interface, and you can get the converted light intensity value, and you can get the heart rate data and blood oxygen saturation by writing the corresponding algorithm.

In this embodiment, the temperature and humidity sensor 16 and the heart rate sensor 17 can be used alone, and can also be used in conjunction with the aforementioned positioning module 13 and fall detection module 14. Taking the mutual cooperation of each module as an example, the three-axis acceleration sensor as the fall detection module 14 detects that the user has fallen based on four judgment criteria and then sends a fall detection signal to the microprocessor 11, and the microprocessor 11 can detect according to the fall. The fall detection signal sent by the module 14 generates an alarm message, and the alarm message together with the location information acquired by the positioning module 13, the temperature and humidity data acquired by the temperature and humidity sensor 16, and the heart rate data acquired by the heart rate sensor 17 are passed through NB-IoT The communication module 12 sends to the service platform or a designated communication terminal.

Of course, the wearable device of the present application may also include other components. For example, the wearable device may also include an illumination sensor, a lighting device with a flashlight function, and a camera module.

In addition, as mentioned above, in addition to using the microprocessor 11 to send the alarm information and other related information of the user's fall behavior to the service platform or designated communication terminal, the wearable device itself of the present application can also include corresponding communication module. For example, in some embodiments, the wearable device of the present application may include a help button and a voice communication module.

Wherein, the help key is connected with the microprocessor 11, and is used for sending a help request after being triggered. In some examples, the help key may be a physical key provided on the housing of the wearable device, and the physical key may be triggered by pressing up and down, flipping left and right, rotating forward and backward, and the like. In some examples, the help key can be a virtual key, which can be implemented by invoking related controls or applications displayed on the display screen of the wearable device.

The voice communication module is connected to the microprocessor 11. When the microprocessor 11 receives the help request from the help button, it controls the voice communication module to call the designated contact, and the user performs voice communication with the designated contact. Wherein, the specified contact person can be preset in advance or can be specified from the prestored contacts when calling the voice communication module. In some examples, the voice communication may be a common telecommunications call based on a mobile communication network. In some examples, the voice communication may also be a voice message.

It can be seen from the above that the wearable device based on the NB-IoT disclosed in this application can detect the user's fall behavior through the set fall detection module, and the alarm information corresponding to the fall behavior together with the location information obtained by the positioning module can be passed through NB-IoT The communication module sends it to the service platform or designated communication terminal, which can realize the purpose of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

The present application also discloses a fall detection method applied to a wearable device based on narrowband Internet of Things. Please refer to FIG. 6 , which is a schematic flow chart of an embodiment of the fall detection method of the present application. As shown in Figure 6, the application fall detection method may include the following steps:

Step S1, use the fall detection module to detect the fall of the user to whom the wearable device belongs.

In this embodiment, the fall detection module may use a three-axis acceleration sensor for acquiring acceleration signals of the user in three directions to detect whether the user has fallen.

In practical application, in step S1, using the three-axis acceleration sensor to detect whether the human body has fallen behavior is realized by detecting the acceleration signals of the wearable device in the three-axis directions in the space Cartesian coordinate system. Specifically, as shown in FIG. 3 , the space rectangular coordinate system includes an X-axis, a Y-axis, and a Z-axis, wherein the X-axis represents the front-to-back direction of the human body, the Y-axis represents the vertical direction of the human body, and the Z-axis Indicates the left-right direction of the human body. The three-axis acceleration sensor is designed based on the principle of gravitational acceleration. It measures the acceleration of the X, Y, and Z axes when it is at rest, and according to the components of the acceleration of gravity and its components between the X, Y, and Z axes of the three-axis acceleration Respectively calculate the angles between the X, Y, and Z axes and the acceleration of gravity, so as to obtain the angle of the system.

In some implementations, the manner in which the three-axis acceleration sensor sends a fall detection signal based on four judgment criteria may specifically include:

Based on the first judgment criterion, it is detected whether the wearable device has a weightlessness behavior.

Based on the second judgment basis, it is detected whether an impact behavior occurs after the weightlessness behavior of the wearable device.

Based on the third judgment basis, it is detected whether the wearable device is in a steady state after the impact behavior.

Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold.

Step S2, judging whether it is detected that the wearable device belongs to the user who has fallen. If it is detected that the user of the wearable device has a fall behavior, then proceed to step S3; if it is not detected that the user of the wearable device has a fall behavior, then return to step S1 and continue to detect.

In step S3, the fall detection module detects and sends a fall detection signal to the microprocessor.

Step S4, the microprocessor generates an alarm message according to the fall detection signal and sends the alarm message together with the location information acquired by the positioning module to the service platform or designated communication terminal through the NB-IoT communication module.

It can be seen from the above that the fall detection method of the wearable device based on the narrowband Internet of Things disclosed in this application can detect the user's fall behavior through the set fall detection module, and the alarm information corresponding to the fall behavior together with the location information obtained by the positioning module Through the NB-IoT communication module, it is sent to the service platform or designated communication terminal, which can achieve the purpose of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

The present application also discloses a safety monitoring service system, please refer to FIG. 7, which is a block diagram of the safety monitoring service system of the present application. As shown in Figure 7, the security monitoring service system of the present application includes: a wearable device 1, a service center 3, and a monitoring platform 5.

The wearable device 1 can be worn on the user's body, and the wearable device 1 can work independently or be used after being connected with other corresponding terminals (such as mobile phones, pad computers or notebook computers, etc.) through necessary connection methods.

Generally, the wearable device 1 can detect the physiological characteristics or motion characteristics of the wearable user, as the user's personal health assistant and safety monitoring protector.

Specifically, the wearable device 1 may include a microprocessor, an NB-IoT communication module, a positioning module, a fall detection module, and several other sensors, etc., wherein the positioning module is used to obtain position information, and the fall detection module is used to To detect whether the user of the wearable device has a fall behavior, other sensors may include but not limited to: temperature and humidity sensors, heart rate sensors, etc.

For the wearable device 1 and its components, reference may be made to FIGS. 1 to 5 and related descriptions, which will not be repeated here.

The service center 3 may include a cloud server, a data center, a monitoring server, and an emergency center.

The service center 3 can receive the data sent by the wearable device 1 through the NB-IoT communication module.

The cloud server is used to store massive physical sign data and historical past data.

The data center is used to store the user's physiological data and the identity information of the user, guardian and medical staff, and the guardian and medical staff remotely view the data in the data center through the monitoring platform 5.

The monitoring server is used for monitoring physiological parameter indicators, and an abnormal real-time alarm occurs to notify medical staff and guardians of emergency assistance.

The emergency center is connected to the monitoring server, and when there is an emergency request, the monitoring server will immediately notify the emergency center after receiving the emergency request, and the emergency center will immediately take measures according to the information fed back by the appealing monitoring server.

Further, the monitoring platform 5 monitors from three aspects: the user and his wearable device 1, the guardian and his mobile client, the medical staff and the monitoring platform. Users and their wearable devices 1 obtain information and suggestions provided by the service center 3 through the NB-IoT communication module, understand their physiological conditions through voice broadcasts, and promptly remind them to seek medical treatment when abnormalities occur. Guardians and their mobile clients obtain data through mobile communication base stations, and the data are displayed intuitively in the form of text, graphics, and tables, and the physiological indicators of the ward are checked in real time. The mobile communication base station sends it to the guardian's mobile client, and the guardian can take immediate action after confirmation to ensure the health and safety of the guardian. Medical personnel and the monitoring platform, through the establishment of a database of physiological parameters, medical personnel can remotely view various data and control various indicators in a timely manner, which can prevent the occurrence of potential diseases in advance, and give reasonable and timely treatment suggestions when abnormalities occur. Monitoring of vital signs and defense against chronic diseases.

Therefore, in the safety monitoring service system of this application, after the wearable device 1 is powered on, it first performs initialization work, and the built-in positioning module obtains the user's current location information, and each sensor collects physiological signals, which are transmitted to the The background service center 3, the service center 3 judges whether the data exceeds the warning value, and when the physiological index exceeds the warning value, an alarm is activated and corresponding rescue is carried out.

Taking fall detection as an example, the fall detection module (three-axis acceleration sensor) on the wearable device detects the user in real time, and when it detects that the user has fallen based on four judgment criteria, the wearable device passes the NB -IoT module sends the alarm information corresponding to the fall behavior or the severity level of the fall to the background service center 3. On the one hand, the service center 3 can send the alarm information to the designated communication terminal; on the other hand, the service center 3 can send a help request to the emergency center information, the emergency center takes rescue measures, for example, calling 120 for emergency treatment, etc.

The wearable device and fall detection method based on the narrowband Internet of Things disclosed in this application can detect the user's fall behavior through the set fall detection module, and the alarm information corresponding to the fall behavior together with the location information obtained by the positioning module can be passed through NB-IoT The communication module sends it to the service platform or designated communication terminal, which can realize the purpose of accurate detection of fall behavior, rapid transmission of alarm information, and effective monitoring and protection of users.

The above-mentioned embodiments are only illustrative to illustrate the principles and effects of the present application, but are not intended to limit the present application. Any person familiar with this technology can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in the application should still be covered by the claims of the application.

Claims (10)

1. A wearable device based on narrowband Internet of Things, characterized in that, comprising: A microprocessor and a NB-IoT communication module, a positioning module and a fall detection module connected to the microprocessor; in: The positioning module is used to obtain location information; The fall detection module is used to detect whether the user of the wearable device has a fall behavior and sends a fall detection signal after detecting that the user has a fall behavior; and The microprocessor is used to generate an alarm message according to the fall detection signal sent by the fall detection module, and send the alarm message together with the location information acquired by the positioning module to the service platform or designated location through the NB-IoT communication module. communication terminal. 2. The wearable device according to claim 1, wherein the fall detection module is a three-axis acceleration sensor, and the three-axis acceleration sensor detects whether the user has fallen by detecting whether the wearable device is Acceleration signals on the three axes in the spatial rectangular coordinate system; In the spatial rectangular coordinate system, it includes the X axis, the Y axis, and the Z axis, wherein the X axis represents the user's front and rear directions, and the Y axis Indicates the vertical direction of the user, and the Z axis indicates the left and right direction of the user; according to the component relationship between the gravitational acceleration and the X, Y, and Z axes of the three-axis acceleration, the X, Y, and Z axes and the three axes are calculated respectively. Angle of acceleration due to gravity. 3 . The wearable device according to claim 2 , wherein the triaxial acceleration sensor sends out a fall detection signal based on four judgment criteria when detecting whether the user falls. 4. The wearable device according to claim 3, wherein the manner in which the three-axis acceleration sensor sends a fall detection signal based on four judgment criteria includes: Based on the first judgment basis, detect whether the wearable device has a weightlessness behavior; Based on the second judgment basis, detect whether there is an impact behavior after the weightlessness behavior of the wearable device; Based on the third judgment basis, detecting whether the wearable device is in a stable state after the impact behavior; and Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold. 5. The wearable device according to claim 4, characterized in that, the triaxial acceleration sensor can also detect the severity level of the user's fall behavior based on four judgment criteria and send out the severity level corresponding to the fall behavior The corresponding fall detection signal. 6. The wearable device according to claim 1, further comprising a temperature and humidity sensor and/or a heart rate sensor connected to the microprocessor. 7. The fall detection watch according to claim 1, further comprising: A help key, connected to the microprocessor, the help key is used to send a help request after being triggered; and A voice communication module, the voice communication module is connected to the microprocessor, and the microprocessor controls the voice communication module to call a designated contact when receiving the help request from the help key. 8. A fall detection method applied to a wearable device based on the narrowband Internet of Things, wherein the wearable device includes: a microprocessor and a NB-IoT communication module and a positioning module connected to the microprocessor And fall detection module; Described fall detection method comprises the steps: Use the fall detection module to detect the acceleration signals of the wearable device in the three-axis directions in the space Cartesian coordinate system, and calculate the acceleration signals according to the component relationship between the acceleration of gravity and the X, Y, and Z axes of the three-axis acceleration. The angle between the X, Y, and Z axes and the acceleration of gravity, so as to determine whether the user of the wearable device has fallen, and send a fall detection signal after detecting that the user has fallen; The spatial Cartesian coordinate system includes an X axis, a Y axis, and a Z axis, wherein the X axis represents the user's front-back direction, the Y axis represents the user's vertical direction, and the Z axis represents the user's left-right direction; and Utilize the microprocessor to generate an alarm message according to the fall detection signal sent by the fall detection module, and send the alarm message together with the location information acquired by the positioning module to the service platform or the specified location through the NB-IoT communication module communication terminal. 9. The fall detection method according to claim 8, wherein the triaxial acceleration sensor detects whether the user has a fall behavior based on four judgment criteria, the method comprising: Based on the first judgment basis, detect whether the wearable device has a weightlessness behavior; Based on the second judgment basis, detect whether there is an impact behavior after the weightlessness behavior of the wearable device; Based on the third judgment basis, detecting whether the wearable device is in a stable state after the impact behavior; and Based on the fourth judgment criterion, it is detected whether the acceleration information of the wearable device changes from the initial state when the wearable device is in a stable state, and whether the magnitude of the change exceeds a threshold. 10. A safety monitoring service system, characterized in that it comprises: The wearable device according to any one of claims 1 to 7; service centers; and monitoring platform.