Gait monitoring and healthcare system

Abstract

To provide a gait monitoring and a healthcare system.SOLUTION: A gait monitoring and healthcare system 100 comprises: a first IMU 10; a second IMU 20; and a local host 30. The first IMU is attached to a thigh of a user, senses an active state of the thigh, and generates a first activity signal. The second IMU is attached to a calf of the user, senses an active state of the calf and generates a second activity signal. The local host includes a microprocessor and an alarm unit. The microprocessor: calculates a first elevation angle between a horizontal plane and the first IMU according to the first activity signal and the second activity signal, and a second elevation angle between the horizontal plane and the second IMU; calculates activity information of the knee joint of the user including a bending angle of the knee joint, an angular speed and a duration according to the first and second elevation angles; determines whether or not the bending angle of the knee joint in a gait state is in a dangerous range according to the activity information; and if in the danger range, controls the alarm unit to output an alarm signal.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a gait monitoring and health care system, and more particularly to a gait or knee joint activity monitoring and health care system for calculating and monitoring the bending angle, angular velocity and / or duration of a knee joint according to elevation angle information. [Background technology]

[0002] In Taiwan, the prevalence of knee osteoarthritis is about 15%, with approximately 3.5 million sufferers, and the trend is toward younger age groups. Knee osteoarthritis was initially thought to be untreatable. When knee osteoarthritis becomes severe, joint replacement becomes necessary. However, 75% of patients are reluctant to undergo joint replacement surgery, which causes them distress. Monitoring systems and devices for knee joint care are commercially available. However, these systems have many limitations, and the measured knee data is incomplete.

[0003] Taiwan Patent No. TWI615129B discloses a gait analysis system including a sole sensing unit, a knee sensing unit, and a portable device. The portable device performs gait analysis based on the information of the sole sensing unit and the knee sensing unit. However, TWI615129B does not disclose the details of the calculation. In addition, the pressure sensor installed on the sole of the foot causes discomfort during walking, increases the cost and calculation load, and requires an expensive microprocessor of the portable device. In addition, wireless information transmission may cause delays and does not monitor and alert the user in real time. Therefore, the prior art needs further improvement. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, the objective of the present invention is to provide a gait monitoring and health management system for calculating and monitoring the bending angle, angular velocity and / or duration of the knee joint according to the elevation angle information, and only two Inertial Measurement Units (IMUs) are needed to perform the sensing. [Means for solving the problem]

[0005] In order to achieve the above-mentioned object, the present invention provides a walking monitoring and health management system, which includes a first IMU, a second IMU, and a local host. The first IMU is attached to the user's thigh, and senses the activity state of the thigh to generate a first activity signal. The second IMU is attached to the user's lower leg, and senses the activity state of the lower leg to generate a second activity signal. The local host is electrically connected to the first IMU and the second IMU, and includes a microprocessor and an alarm. The microprocessor calculates a first elevation angle between the first IMU and a horizontal plane and a second elevation angle between the second IMU and a horizontal plane according to the first activity signal and the second activity signal, calculates activity information of the user's knee joint, including a bending angle, an angular velocity, and a duration of the knee joint according to the first elevation angle and the second elevation angle, and judges whether the bending angle of the knee joint in the walking state is within a danger range according to the activity information, and if it is within the danger range, controls the alarm to output a warning signal.

[0006] According to the above embodiment, two IMUs can be installed on the upper and lower legs, and the elevation angle and knee joint angle can be calculated, and related data analysis and warning can be performed.

[0007] In order to make the above contents of the present invention clearer and more understandable, detailed descriptions are given below with reference to preferred embodiments and drawings. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an ambulatory monitoring and health management system according to a preferred embodiment of the present invention. [Diagram 2]FIG. 2 illustrates the ambulatory monitoring and health management system of FIG. 1 in use. [Diagram 3] FIG. 3 is a simplified schematic diagram of the ambulatory monitoring and health management system of FIG. [Figure 4] FIG. 4 is a flow chart illustrating the operation of the ambulatory monitoring and health management system of FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present inventors have monitored, analyzed and studied the actual activity data of the knee joint, and found that knee osteoarthritis can be improved or even cured by using the knee joint appropriately. As long as a normal person uses the knee joint appropriately, the possibility of developing knee osteoarthritis can be greatly reduced. Therefore, the present invention provides a system and an auxiliary device for preventing and treating knee osteoarthritis by detecting and monitoring the activity of the knee joint. The system provides correction information on harmful activities to prevent and improve knee osteoarthritis.

[0010] As shown in Fig. 1 to Fig. 3, the walking monitoring and health management system 100 of this embodiment includes a first IMU 10, a second IMU 20, and a local host 30. The first IMU 10 is attached to a user's thigh 1, senses an activity state of the thigh 1, and generates a first activity signal E1. The second IMU 20 is attached to a user's lower leg 2, senses an activity state of the lower leg 2, and generates a second activity signal E2. The local host 30 is electrically connected to the first IMU 10 and the second IMU 20, and includes a microprocessor 31 and an alarm 32. The microprocessor 31 executes the following operations (a) to (c).

[0011] First, in operation (a), the microprocessor 31 calculates a first pitch angle A1 between the horizontal plane HP and the first IMU 10 (or thigh 1) and a second pitch angle A2 between the horizontal plane HP and the second IMU 20 (or lower leg 2) according to the first activity signal E1 and the second activity signal E2. There is an advantage that walking judgment can be made according to the pitch angle. For example, when a user goes up and down stairs, the thigh is lifted higher. The activity of the knee joint is similar when the user is walking uphill, downhill, and horizontally. However, the independent activity ranges of the thigh and lower leg are not the same, and these characteristics cannot be judged according to the angle of the knee joint. Therefore, with the help of the activity angle of the thigh (first pitch angle) and the activity angle of the lower leg (second pitch angle), walking judgment can be simplified.

[0012] Next, in operation (b), the microprocessor 31 calculates the activity information of the user's knee joint 3 according to the first elevation angle A1 and the second elevation angle A2. The activity information includes the bending angle (or knee joint angle) A3, the angular velocity and the duration of the knee joint 3, where A3=180-A1-A2 degrees.

[0013] Then, in operation (c), the microprocessor 31 judges according to the activity information whether the bending angle A3 (optional angular velocity and optional duration) of the knee joint 3 in the activity state (e.g., walking state or running state) is within the danger range. If it is within the danger range, the microprocessor 31 controls the alarm 32 to output a warning signal. The alarm 32 includes, but is not limited to, a buzzer, a vibrator, and the like. According to the above devices, the activity state data can be calculated, the user's current activity can be predicted, and the knee joint activity monitoring and health management settlement function can be realized. Here, since the local host 30 is disposed at the knee joint 3, the knee joint 3 can directly feel the vibration and achieve a better reminder effect. The calculation based on the elevation angle can provide useful data to the microprocessor 31. For example, when the user is moving on a horizontal ground, the working angle of the ankle joint 4 can be provided, so that the microprocessor 31 obtains other judgment criteria. In addition, the microprocessor 31 can conveniently calculate the first elevation angle A1 and the second elevation angle A2 by using the quaternion method. The IMU contains a three-axis gyroscope. 0 ,q 1 ,q 2 ,q 3 An initial value of q (e.g., (1,0,0,0)) is set according to equation (1). The quaternion is updated to q' using the angular velocity change of the gyroscope according to equation (2), q' is normalized to the new q, and the elevation angle PA is determined according to equation (3). JPEG2025035962000002.jpg72155Here, i, j, and k are imaginary numbers, and ΔT is the sampling update time of the IMU.

[0014] In one example, the danger range is from 60 degrees to 120 degrees, and the bending angle A3 is the included angle between the extension line of the thigh 1 and the lower leg 2. The inventors have found that destructive pressure on the knee cartilage occurs when the bending angle A3 in the walking state is in the range of 60 degrees to 120 degrees. Therefore, there is no need to use a pressure sensor on the sole of the foot. The longer the knee joint is exposed to the destructive pressure, the greater the degree of damage to the knee joint, so the duration must be considered. Therefore, if the bending angle A3 is within the danger range, the microprocessor 31 further counts the duration of the bending angle A3 to obtain the staying time. If the staying time is longer than the threshold time, the microprocessor 31 further controls the alarm 32 to output another warning signal to prevent damage to the knee joint. To meet different conditions, the microprocessor 31 determines the threshold time according to the user's age (previously inputted into the local host 30), activity information, and activity state (e.g., medium speed, high speed, and low speed walking state or running state). In another example, the microprocessor 31 executes the warning according to the danger range of other activities or the resting bending angle of the knee joint and the threshold time value.

[0015] In this example, a 9-axis IMU is used. The IMU includes a 3-axis gyroscope, a 3-axis accelerometer, and a 3-axis magnetometer, which respectively measure the sensing values ​​of the angular velocity, acceleration, and magnetic field strength of an object in a 3D space. The microprocessor 31 performs knee joint activity analysis according to these sensing values. To improve accuracy, the microprocessor 31 calibrates the sensing value of the gyroscope according to one or two sensing values ​​of the accelerometer and the magnetometer, calculates the first elevation angle and the second elevation angle using the quaternion method, and then calculates activity information such as the bending angle, angular velocity, duration, and altitude change of the knee joint according to the first elevation angle and the second elevation angle.

[0016] For example, use the Mahony algorithm. 0 ,q 1 ,q 2 ,q 3 The initial value (1,0,0,0) of gyroscope (g x,g y ,g z ) is the accelerometer value (a x ,a y ,a z ) or magnetometer value (m x ,m y ,m z ) and calibrated according to (q 0 ,q 1 ,q 2 ,q 3 ) is updated. In the example where the calibration is performed using the accelerometer value, the value of q (q 0 ,q 1 ,q 2 ,q 3 ) and the theoretical gravitational acceleration vector (v x ,v y ,v z ) to determine JPEG2025035962000003.jpg2894

[0017] Next, (a x ,a y ,a z ) is calculated according to (Equation 5) as (b x ,b y ,b z ) and the error between the actual gravitational acceleration and the theoretical acceleration (e x ,e y ,e z ) is determined according to (Equation 6). JPEG2025035962000004.jpg7588

[0018] Next, we need to integrate, so we use parameter i=i x +i y +i z is the initial value i=0 x +0 y +0 z is set to (g x ,g y ,g z ) is the coefficient k i , k p and ΔT, the calibrated gyroscope value (g' x ,g' y,g' z ) is obtained according to (Eq. 7). JPEG2025035962000005.jpg5983

[0019] In one example, ki=30 and kp=0 are taken.

[0020] Next, (q 0 ,q 1 ,q 2 ,q 3 ) is the real number of the final quaternion (q' 0 ,q' 1 ,q' 2 ,q' 3 ) will be updated. JPEG2025035962000006.jpg69130

[0021] Finally, (q' 0 ,q' 1 ,q' 2 ,q' 3 ) is the new (q 0 ,q 1 ,q 2 ,q 3 ), and the elevation angle is obtained according to equation (3). In another example, the calibration method using a magnetometer is similar to the calibration method using an accelerometer, so detailed description is omitted. The advantage is that the elevation angle can be directly obtained by using a quaternion. It is no longer necessary to convert the sensing data of the IMU into the rotation coordinate of the IMU and then obtain the included angle between the two IMUs, making the calculation easier. JPEG2025035962000007.jpg4898

[0022] The activity information is also input to a machine learning model in the microprocessor 31, which classifies the user's walking status (or overall activity state). The walking status includes climbing stairs, descending stairs, climbing a slope, descending a slope, and walking on flat ground. The machine learning model may be a support vector machine (SVM), a multi-layer perceptron (MLP), etc.

[0023] The gait monitoring and health management system 100 further includes an auxiliary device 40. The first IMU 10 is disposed on the thigh 1 via a first mounting part 41 of the auxiliary device 40, and the second IMU 20 is disposed on the lower leg 2 via a second mounting part 42 of the auxiliary device 40. The auxiliary device 40 may be a wearable having a fitting part 43 for fitting to the knee joint 3. A first distance from a center point of the fitting part 43 to the first IMU 10 and a second distance from a center point of the fitting part 43 to the second IMU 20 are 15 cm or more. This makes it possible to solve calculation errors during actual sensing. A user can wear the auxiliary device and perform a test. Alternatively, the auxiliary device 40 further includes a first connection part 44 and a second connection part 45. The first connection part 44 is disposed between the first mounting part 41 and the fitting part 43, and is connected to the first mounting part 41 and the fitting part 43. The second connecting portion 45 is disposed between the second mounting portion 42 and the fitting portion 43, and is connected to the second mounting portion 42 and the fitting portion 43. Therefore, the user can satisfy the above-mentioned condition in which the first mounting portion 41 and the second connecting portion 45 are in flat contact with the skin surfaces of the thigh and lower leg.

[0024] The gait monitoring and health management system 100 may further include a remote host 90. The remote host 90 communicates with or is connected to the communication interface 33 of the local host 30. The remote host may be a mobile phone, a tablet computer, a notebook computer, or a server. The microprocessor 31 outputs the activity information of the knee joint to the remote host 90 through the communication interface 33. The remote host 90 performs statistical analysis of the activity information to provide the user with an activity modification solution according to the activity information. Thus, information can be provided for modifying the harmful activity of the user. The system can perform historical integration and analysis of the user's gait information, obtain the number of errors, the time spent at the dangerous angle, etc. every day, and further provide a suitable activity modification solution. The user can also determine some parameters of the modification solution and track the progress curve. In addition, based on factors such as age, activity status, etc. that affect the knee joint, a personalized gait improvement program can be created according to different ethnic groups, and personalized gait or activity suggestions can be customized to extend the life of the knee joint.

[0025] In summary, the present invention also provides a walking monitoring and health management method, including steps S1 to S10.

[0026] After the user wears the assistive device, a walking test is performed (S1). We found that if the first distance and the second distance are less than 15 cm, the calculated knee joint angle is less than 60 degrees, which causes subsequent errors. Therefore, it is necessary to determine whether the knee joint angle (or bending angle) is 60 degrees or more (S2). If the knee joint angle is less than 60 degrees, the system notifies the user to adjust the assistive device (S3). If the knee joint angle is 60 degrees or more, the system performs actual monitoring (S4), performs optional accuracy calibration (S5), determines the elevation angle (S6), and calculates activity information (S7). Next, the system determines whether the knee joint angle is within the danger range (S8). If the angle is outside the danger range, return to step S4. If the angle is within the danger range, output a warning signal and count the stay time (S9). Next, the system determines whether the stay time is longer than the threshold time (S10). If the stay time does not exceed the threshold time, return to step S4. If the stay time exceeds the threshold time, the process returns to step S9.

[0027] Compared with the prior art, which requires additional pressure determination on the sole of the foot and the knee, the present invention only requires two IMUs attached to the lower leg and the thigh, and can perform data analysis and warning. The system can also provide corresponding warnings or suggest activity methods according to the gait analysis results and personal physiological information (e.g., age). Users can also use the system to set personalized training content and track progress curves.

[0028] The specific examples presented in the detailed description of the preferred embodiments are intended to illustrate the technical content of the present invention, and are not intended to narrowly limit the present invention to the above-mentioned embodiments. Various modifications that do not depart from the spirit of the present invention and the scope of the following claims are included in the scope of the present invention. [Explanation of symbols]

[0029] A1: 1st elevation angle A2: 2nd elevation angle A3: Bending angle E1: 1st activity signal E2: 2nd activity signal HP: horizontal plane S1~S10: Step 1: Thigh 2: Lower leg (calf) 3: Knee joint 4: Ankle joint 10: First IMU 20: Second IMU 30: localhost 31: Microprocessor 32:Alarm 33: Communication interface 40: Auxiliary equipment 41: First mounting part 42: Second mounting part 43: Fitting part 44: First connection part 45: Second connection part 90:Remote host 100: Ambulatory monitoring and health management system

Claims

1. 1. An ambulatory monitoring and health management system, comprising: a first IMU, a second IMU, and a local host, the first IMU is attached to a user's thigh and senses an activity state of the thigh to generate a first activity signal; the second IMU is attached to the user's lower leg and senses an activity state of the lower leg to generate a second activity signal; the local host is electrically connected to the first IMU and the second IMU and includes a microprocessor and an alarm; The microprocessor performs a plurality of operations, the plurality of operations including: (a) calculating a first elevation angle between the first IMU and a horizontal plane and a second elevation angle between the second IMU and the horizontal plane according to the first activity signal and the second activity signal; (b) calculating activity information of the knee joint of the user according to the first elevation angle and the second elevation angle, the activity information including a bending angle, an angular velocity and a duration of the knee joint; (c) determining whether the bending angle of the knee joint in a walking state is within a danger range according to the activity information, and, if the bending angle is within the danger range, controlling the alarm to output a warning signal; 1. A walking monitoring and health management system comprising:

2. The ambulatory monitoring and health management system of claim 1 , wherein the microprocessor performs the operation (a) using a quaternion method.

3. The gait monitoring and health management system of claim 1, wherein the danger range is from 60 degrees to 120 degrees, and the bending angle is an included angle between the extension line of the thigh and the lower leg.

4. Further comprising an auxiliary device, 2. The gait monitoring and health management system of claim 1, wherein the first IMU is placed on the thigh via a first mounting part of the auxiliary device, the second IMU is placed on the lower leg via a second mounting part of the auxiliary device, a fitting part of the auxiliary device fits the knee joint, and a first distance from a center point of the fitting part to the first IMU and a second distance from the center point of the fitting part to the second IMU are 15 cm or more.

5. 5. The walking monitoring and health management system of claim 4, wherein the local host is attached to the knee joint via the fitting part, and the auxiliary device further comprises a first connection part between the first mounting part and the fitting part, and a second connection part between the second mounting part and the fitting part.

6. 2. The walking monitoring and health management system of claim 1, wherein when the bending angle is within the danger range, the microprocessor further counts the duration of the bending angle to obtain a staying time, and when the staying time is longer than a threshold time, the microprocessor further controls the alarm to output another warning signal.

7. The walking monitoring and health management system of claim 6, wherein the microprocessor determines the threshold time according to the age, activity information and activity state of the user.

8. 2. The walking monitoring and health management system of claim 1, wherein the first IMU and the second IMU are 9-axis IMUs, the 9-axis IMUs including an accelerometer, a magnetometer, and a gyroscope, and the microprocessor calibrates a sensing value of the gyroscope according to one or two sensing values ​​of the accelerometer and the magnetometer, and then performs the operation (a) to improve accuracy.

9. The gait monitoring and health management system of claim 1, wherein the activity information is input into a machine learning model of the microprocessor, and the machine learning model further classifies the user's walking status, and the walking status includes a stair climbing state, a stair descending state, a slope climbing state, a slope descending state, and a flat ground walking state.

10. a remote host in communication with the communication interface of the local host; the microprocessor outputs the activity information to the remote host via the communications interface; The ambulatory monitoring and health management system of claim 1 , wherein the remote host performs a statistical analysis of the activity information, and provides activity modification solutions to the user according to the activity information.

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