CN113812943A

CN113812943A - Evaluation system and evaluation method for predicting falling risk of patient with sarcopenia

Info

Publication number: CN113812943A
Application number: CN202110954093.1A
Authority: CN
Inventors: 王枚; 梁新生; 李长江; 张玲; 杨越
Original assignee: Fifth Affiliated Hospital of Xinjiang Medical University
Current assignee: Fifth Affiliated Hospital of Xinjiang Medical University
Priority date: 2021-08-19
Filing date: 2021-08-19
Publication date: 2021-12-21

Abstract

The invention relates to a risk assessment system for predicting the falling of a patient with sarcopenia, which comprises a walking stick, a radio detection device, a tremor frequency sensor, a first pressure detection sensor, a telescopic rod, a walking head and a microprocessor. According to the invention, the control part of the risk evaluation system for the tumble of the sarcopenia patients is arranged in the cane used by the old when the old walks, a comprehensive tumble risk evaluation function is finally formed by acquiring pressure, muscle tremor frequency and human body image data and carrying out wireless transmission, whether the old tumbles or is about to tumble is judged by the value of the risk evaluation function, and an acousto-optic alarm is given out when the old is predicted to tumble. Therefore, compared with the existing prediction method or prediction system adopting a single detection mode, the method has the following advantages: the method can obviously improve the accuracy and precision of prediction and realize quick prediction and prevention.

Description

Evaluation system and evaluation method for predicting falling risk of patient with sarcopenia

Technical Field

The invention relates to the technical field of medical detection, in particular to a risk assessment system and an assessment method for predicting falling of a patient with sarcopenia.

Background

The old people often fall down due to osteoporosis, rheumatism, arthritis and sarcopenia, and the falling down easily causes serious injury and even breakage of the fragile bones, wherein the falling down caused by the sarcopenia is an important factor threatening the health of the old people, and the falling down risk is increased along with the deterioration of the muscle strength of the old people and the weakening of the balance capability and the reaction capability. The walking quality of the old can be pre-evaluated, so that the walking behavior can be guided, active and effective intervention can be performed, further, the falling occurrence of the old can be greatly reduced, the falling harm can be reduced, and the corresponding intervention measures can be formulated according to the falling risk of the old, so that the falling occurrence can be effectively avoided, and the risk evaluation of the falling is very important. In the prior art, fall risk assessment is generally performed by two methods, one is to detect a sensor on a human body wearing device and perform analysis and judgment through detection data, and the other is to perform analysis and judgment through acquiring an image of the walking posture of an old person and performing analysis and judgment through image data:

the method for judging through the sensor on the human body wearing equipment generally comprises the following steps: calculating and analyzing according to the sole pressure information to obtain human gait data, and obtaining a moving track curve of the human gravity center projected on the sole according to the sole pressure information and the gait data; the human body gait data and the human body gravity center movement track curve are compared with the normal human body gait data and the normal human body gravity center movement track curve respectively, and when the gait data or the human body gravity center movement track curve exceeds the corresponding normal range, the falling risk of the old people is proved to be higher. However, sometimes, the old falls down due to non-bone diseases such as paroxysmal pain caused by rheumatoid arthritis, hypertension, hypoglycemia, and muscular tremor caused by sarcopenia, and such bone diseases are often sudden when the old walks, and at this time, the old may fall down due to these sudden conditions, however, such sudden conditions often cannot be detected by using a single sensor.

The method for judging through the sensor on the human body wearing equipment generally comprises the following steps: the method comprises the steps of installing a camera in a place where the old people often move to obtain monitoring video data, cutting each frame of image in the monitoring video data to obtain a human body image at least containing human body joints, processing the human body image into a human body image to be recognized with a preset standard size, estimating a network model based on a pre-trained human body posture, obtaining joint coordinate data in the human body image to be recognized, estimating the falling risk of the old people based on the joint coordinate data of continuous multi-frame images, estimating the falling risk based on the human body posture obtained by a machine learning model, reducing the use amount of a detection sensor to a certain extent and reducing the influence of a wearing device on the life of the old people, wherein the image detection is only used for judging the falling risk estimation when the old people have large limb actions, but before the old people fall due to less muscle diseases, large limb movements are usually infrequent, so it is difficult to accurately predict whether an elderly sarcopenia patient is falling, either by simple image determination or by sensor determination on a wearable device.

Therefore, how to quickly acquire risk data before the elderly fall and accurately and quickly predict gait balance and fall risk of the elderly become a technical problem which needs to be solved by the technical personnel in the field.

Disclosure of Invention

The invention aims to improve the accuracy of risk assessment and provide a risk assessment system and an assessment method for predicting the falling of patients with sarcopenia.

In order to solve the technical problem, the utility model provides a, a risk assessment system that prediction senile muscle is few patient tumbles, including leaning on cane, radio detection device, tremor frequency sensor, first pressure detection sensor, telescopic link, leaning on ground head and microprocessor. Lean on the stick and include the handle and with the loop bar that the handle is connected, be provided with microprocessor and alarm unit in the handle, the lower part of handle is provided with radio detection device, loop bar and telescopic link sliding connection, the top of telescopic link is provided with second pressure detection sensor, the bottom of telescopic link articulates there is leaning on the ground head, alarm unit, radio detection device and second pressure detection sensor all are connected with the microprocessor electricity.

Radio detection device is to old person's low limbs position transmission shortwave signal, and then judges the position of low limbs position in the space through the radio signal who gathers, with the old person's of gathering low limbs position signal of telecommunication data send to microprocessor, and microprocessor will judge old walking gesture according to the historical data of typeeing, and then can judge whether old person has the possibility of tumbleing.

The tremor frequency sensor is used for measuring the tremor frequency of leg muscles or the tremor frequency of knee joints, the tremor frequency sensor is bound on a part of the lower limbs of the elderly, which is easy to tremor or tremble, through the human body wearing device, and the tremor frequency sensor is sent to the microprocessor in a wireless communication mode to perform parameter processing.

The first pressure detection sensors are arranged in shoes worn by old people, in order to better measure sole pressure, the first pressure detection sensors are respectively arranged at the heels, the front soles and the outer sides of the soles of the human bodies corresponding to insoles or the shoes, the pressures detected at the heels, the front soles and the outer sides of the soles are F11, F12 and F13, each foot stepping and leg lifting action is completed in a walking cycle, and then the detected data are sent to the microprocessor for parameter processing in a wireless communication mode.

Lean on the cane both can be used for the old person to lean on ground to support, also can regard as a measurement alarm device, through after detecting old person's each item parameter, if judge that this old person has the risk of tumbleing, then send audible-visual annunciation, in time remind the old person to take precautions against, for example when audible-visual annunciation, the old person should hold the handle of leaning on the cane tightly, makes the telescopic link stretch out the length of telescopic link the shortest simultaneously, relies on two legs and the triangular supports effect of leaning on the cane, prevents to tumble.

The sub-unit connection spring of second pressure detection sensor, the sub-unit connection telescopic link of spring, the top of telescopic link and the bottom of loop bar all are provided with the spacing ring, through the effect of spacing ring and spring, the telescopic link can realize freely sliding in the loop bar, the telescopic link stretches out the length of loop bar and is short more, then second pressure detection sensor 7's pressure is big more, if the pressure that second pressure detection sensor detected surpasss normally to lean on the cane press the pressure interval, then can judge that the old person is about to fall down.

The pole leaning head is made of elastic rubber materials and is hinged with the bottom of the telescopic rod, so that the pole leaning head can adapt to different grounds, and the pole leaning support can be prevented from sliding, and the support is more stable.

After the microprocessor collects various data and carries out comprehensive analysis, the old people are judged to have the falling risk, and then the alarm unit is controlled to give out audible and visual alarm to prompt the old people to adjust the walking posture.

The first pressure detection sensor, the second pressure detection sensor, the radio detection device, the tremor frequency sensor are in communication connection with the signal processing unit, the signal processing unit is electrically connected with the signal transceiving unit, the signal transceiving unit is connected with the microprocessor, the microprocessor can process parameters uploaded by various sensors and the radio detection device, calculate walking posture data of the old, compare the walking posture data of the old with a preset threshold range (the preset threshold range is historical data of a memory in the memory, the memory is electrically connected with the microprocessor), if the walking posture data exceeds the preset walking posture threshold, the microprocessor controls the alarm unit to give out an acousto-optic alarm, if the walking posture data does not exceed the preset walking posture threshold, the parameter detection step is returned, the first pressure, the second pressure, the tremor frequency sensor and the signal processing unit are continuously detected, Tremor frequency and human image data.

Based on the risk assessment system for predicting the falling of the patient with sarcopenia, the invention provides a risk assessment method for predicting the falling of the patient with sarcopenia, which comprises the following steps:

step 1: human body parameter acquisition: acquiring pressure parameters through a first pressure detection sensor and a second pressure detection sensor, acquiring frequency parameters through a tremor frequency sensor, and acquiring image parameters through a radio detection device;

wherein, the first pressure detecting sensors are arranged in the left and the right shoes, and the pressure at the outer sides of the heel, the front sole and the sole detected by the left foot is respectively F11, F12 and F13; the pressure at the heel, the forefoot and the lateral side of the sole detected by the right foot is respectively F21, F22 and F23; the pressure detected by the second pressure detection sensor 7 is F31.

Step 2: normalizing the data, processing the pressure parameter to obtain pressure time equivalent L1, processing the frequency parameter to obtain frequency time equivalent L2, and processing the image parameter to obtain image time equivalent L3;

and step 3: calculating the walking posture, and determining a fall risk assessment function eta (N1, N2 and N3) according to L1, L2 and L3, wherein the steps are as follows:

N1＝L1/L1(5)

N2＝L2/L2(6)

N3＝L3/L3(7)

in the formula, L1 is a standard pressure time equivalent when walking, L2 is a standard frequency time equivalent when walking, L3 is a standard image time equivalent when walking, and L1, L2, and L3 are obtained from historical data.

And 4, step 4: on the basis of the step 3, the falling risk assessment result of the patient with sarcopenia is as follows:

1) when N1<1, N2<1, and N3<1, η ═ 0, the interpretation result is "abnormal, fall risk is moderate", and parameter detection is continued;

2) when N1 is greater than 1, N2 is less than 1, and N3 is less than 1, eta is 1, the interpretation result is 'normal, the fall risk is low', and the parameter detection is continued;

3) when N1 is greater than 1, N2 is greater than 1, and N3 is less than 1, eta is 2, the interpretation result is abnormal, the fall risk is moderate, and the parameter detection is continued;

4) when N1 is greater than 1, N2 is greater than 1, and N3 is greater than 1, eta is 3, the interpretation result is that the danger and the falling risk are high, and the control alarm unit gives out audible and visual alarm;

5) when N1<1, N2>1 and N3<1, eta is 4, the interpretation result is 'abnormal and falling risk is moderate', and the parameter detection is continued;

6) when N1<1, N2>1 and N3>1, eta is 5, the interpretation result is 'danger and fall risk is high', and the control alarm unit gives out audible and visual alarm.

In summary, according to the risk assessment system and the assessment method for predicting the falling of the sarcopenia patients, the control part of the risk assessment system for the falling of the sarcopenia patients is arranged in the walking stick used by the old when the old walks, a comprehensive falling risk assessment function is finally formed by collecting pressure, muscle tremor frequency and human body image data and carrying out wireless transmission, whether the old falls or is about to fall is judged according to the value of the risk assessment function, and an audible and visual alarm is given when the old is predicted to fall. Therefore, compared with the existing prediction method or prediction system adopting a single detection mode, the method has the following advantages: the method can obviously improve the accuracy and precision of prediction and realize quick prediction and prevention.

Drawings

FIG. 1 is a block diagram of an evaluation system for predicting the fall risk of sarcopenia patients according to the present invention;

FIG. 2 is a schematic view of the cane of the present invention;

FIG. 3 is a flowchart of an evaluation method of the evaluation system for predicting the fall risk of sarcopenia patients according to the present invention;

in the figure, 1-walking stick, 2-local detection device, 3-tremor frequency sensor, 4-first pressure detection sensor, 5-telescopic rod, 6-walking head, 7-second pressure detection sensor, 8-spring.

Detailed Description

The technical solution of the present invention is further described below with reference to the accompanying drawings so that those skilled in the art can understand the present invention, and the specific embodiments are as follows:

example 1

Referring to fig. 1-2, a risk assessment system for predicting the fall of a patient with sarcopenia comprises a walking stick 1, a radio detection device 2, a tremor frequency sensor 3, a first pressure detection sensor 4, a telescopic rod 5, a walking stick head 6 and a microprocessor. The walking stick 1 comprises a handle and a loop bar connected with the handle, a microprocessor and an alarm unit are arranged in the handle, a radio detection device 2 is arranged on the lower portion of the handle, the loop bar is connected with a telescopic rod 5 in a sliding mode, a second pressure detection sensor 7 is arranged at the top of the telescopic rod 5, a walking head 6 is hinged to the bottom of the telescopic rod 5, and the alarm unit, the radio detection device 2 and the second pressure detection sensor 7 are all electrically connected with the microprocessor.

Radio detection device 2 adopts the millimeter wave radar, can be to old person's low limbs position transmission short wave signal, and then judges the position of low limbs position in the space through the radio signal who gathers, sends to microprocessor through the low limbs position electrical signal data with the old person who gathers, and microprocessor will judge old walking gesture according to the historical data of typing in, and then can judge whether the old person has the possibility of tumbleing.

The tremor frequency sensor 3 is used for measuring the frequency of leg muscle tremor or the tremor frequency of knee joint, the tremor frequency sensor 3 is bound at the part of the lower limb of the elderly who easily shakes or shakes through the human body wearing device, and the tremor frequency sensor 3 is sent to the microprocessor in a wireless communication mode for parameter processing.

The first pressure detection sensors 4 are arranged in shoes worn by old people, in order to better measure sole pressure, the first pressure detection sensors 4 adopt film sensors or piezoelectric sensors, the first pressure detection sensors 4 are respectively arranged at the heels, the front soles and the outer sides of the soles of the human bodies corresponding to insoles or the interiors of the shoes, the pressures detected by the heels (relevant to backward bending and falling of the human bodies), the front soles (relevant to forward bending and falling of the human bodies) and the outer sides of the soles (relevant to inclined falling of the human bodies) are F11, F12 and F13, each foot stepping and leg lifting action is completed to be equivalent to a walking cycle, and in one walking cycle, the three first pressure detection sensors 4 send detected data to the microprocessor in a wireless communication mode for parameter processing.

The walking stick 1 can be used for supporting the old people to lean on the ground, and can also be used as a measurement alarm device, after various parameters of the old people are detected, if the old people are judged to have a falling risk, a sound-light alarm is sent out to remind the old people to take precautions in time, for example, when the sound-light alarm is given, the old people should tightly hold the handle of the walking stick 1, meanwhile, the length of the telescopic rod 5 extending out of the sleeve rod is shortest, and the old people are prevented from falling down by virtue of the triangular support effect of the two legs and the walking stick 1.

Continuing to look at fig. 2, the lower part of second pressure detection sensor 7 is connected with spring 8, and telescopic link 5 is connected to the lower part of spring 8, and the top of telescopic link 5 and the bottom of loop bar all are provided with the spacing ring, and through the effect of spacing ring and spring 8, telescopic link 5 can realize freely sliding in the loop bar, and the length that telescopic link 5 stretches out the loop bar is shorter, and then the pressure of second pressure detection sensor 7 is bigger. When the old people normally walk, the gravity of the body is mainly concentrated on the feet, the walking stick 1 does not support the gravity center of the body, therefore, the walking pressure of the walking stick 1 is in a small interval, when the user predicts that the leg muscle tremor is about to fall, the walking stick 1 can be pressed on the ground by subconscious, at the moment, the pressure detected by the second pressure detection sensor 7 can exceed the walking pressure interval of the normal walking stick 1, and whether the old people are about to fall can be judged according to the pressure change.

The ground leaning head 6 is made of elastic rubber materials, the ground leaning head 6 is hinged with the bottom of the telescopic rod 5, so that the walking stick 1 can adapt to different grounds, and the walking stick 1 can be prevented from sliding due to the inclined strut, so that the support is more stable.

Therefore, the tremor frequency sensor 3 is in a non-osseous tissue state, the first pressure detection sensor 4 and the second pressure detection sensor 7 are in a human body gravity center state, the radio detection device 2 is in a human body lower limb overall posture, and the old people can be judged to be walking, squatting or sitting, so that false alarm caused by sensor detection is avoided.

Example 2

Referring to fig. 3, on the basis of embodiment 1, the connection mode of the risk assessment system of the present invention is as follows: the first pressure detection sensor, the second pressure detection sensor, the radio detection device and the tremor frequency sensor are in communication connection with the signal processing unit, the signal processing unit is electrically connected with the signal transceiving unit, the signal processing unit and the signal transceiving unit are both electrically arranged in the walking stick 1, the signal transceiving unit is connected with the microprocessor, the microprocessor can process parameters uploaded by various sensors and the radio detection device, calculate the walking posture data of the old and compare the walking posture data of the old with a preset threshold range (the preset threshold range is historical data of a memory in the memory, the memory is electrically connected with the microprocessor), if the walking posture data exceeds the preset walking posture threshold value, the microprocessor controls the alarm unit to give out an acousto-optic alarm, and if the walking posture data does not exceed the preset walking posture threshold value, the parameter detection step is returned, the first pressure, the second pressure, the tremor frequency and the human image data continue to be detected.

the walking pressure time equivalent of the old is L1, and L1 is calculated by the following formula:

V1＝(F11*t1+F12*t2+F13*t3+F21*t4+F22*t5+F23*t6+F31*t7)/M_{human being} (1)

L1＝V1/(F0*∑t_i) (2)

Wherein V1 is the walking speed, t 1-t 7 are the action time of F11-F31 pressure respectively, M_{Human being}For the weight of the elderly, ∑ t_iFor the walking cycle duration, the value of i is 1-7, and F0 is the pressure on the ground when the human body stands still.

The walking tremor frequency time equivalent of the elderly is L2, and L2 is calculated by the following formula:

L2＝μ(∑f1_i*∑t_i+∑f2_i*∑t_i) (3)

in the formula, f1_iAnd f2_iIs t_iFrequency of tremor of the left and right feet/knees over time, if at t_iWhen no tremor occurs within the time, f_iMu is a tremor constant, generally between 3 and 5.

The measured data of the radio detection device 2 is point cloud data under a three-dimensional coordinate, the point cloud data is represented by a point cloud data function, the walking image time equivalent of the old is L3, and L3 is calculated according to the following formula:

L3＝f(X(t)，Y(t)，Z(t)，Ints(t)，R(t)，G(t)，B(t))/f(X(t-1)，Y(t-1)，Z(t-1)，Ints(t-1)，R(t-1)，G(t-1)，B(t-1)) (4)

in the formula, f is an image point cloud data matrix function, X, Y, Z is a coordinate system matrix of key parts of human legs under three-dimensional coordinates, int is a point cloud density matrix, R, G and B are Red (Red), Green (Green) and Blue (Blue) in image colors, t is a current walking cycle, and t-1 is a previous walking cycle.

And step 3: calculating the walking posture, and determining a fall risk assessment function eta (N1, N2, N3) as follows:

N1＝L1/L1 (5)

N2＝L2/L2 (6)

N3＝L3/L3 (7)

Step 4, on the basis of the step 3, the risk assessment result for predicting the falling of the patient with sarcopenia is as follows:

2) when N1<1, N2<1, and N3<1, η ═ 0, the interpretation result is "abnormal, fall risk is moderate", and parameter detection is continued;

In summary, in the risk assessment system and the assessment method for predicting the fall of the sarcopenia patients, the old people like walking stick, so that the control part of the risk assessment system for the fall of the sarcopenia patients is arranged in the walking stick, the pressure on the ground, the muscle tremor frequency and the image data of the old people during walking are collected, a fall risk assessment function eta (N1, N2 and N3) is formed by calculation and analysis of a microprocessor, whether the old people falls or is about to fall is judged by taking the risk assessment function, and an acousto-optic alarm is given when the old people is predicted to fall. Therefore, compared with the existing prediction method or prediction system adopting a single detection mode, the method and the device can obviously improve the accuracy and precision of prediction and realize quick prediction and prevention.

The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the patent. It should be noted that, for those skilled in the art, various changes, combinations and improvements can be made in the above embodiments without departing from the patent concept, and all of them belong to the protection scope of the patent. Therefore, the protection scope of this patent shall be subject to the claims.

Claims

1. The utility model provides a risk assessment system that prediction senile sarcopenia patient tumbles, includes and leans on cane, radio detection device, tremble frequency sensor, first pressure detection sensor, telescopic link, lean on the ground head and microprocessor, its characterized in that: comprises a walking stick, a radio detection device, a tremor frequency sensor, a first pressure detection sensor, a telescopic rod, a walking head and a microprocessor. Lean on the stick and include the handle and with the loop bar that the handle is connected, be provided with microprocessor and alarm unit in the handle, the lower part of handle is provided with radio detection device, loop bar and telescopic link sliding connection, the top of telescopic link is provided with second pressure detection sensor, the bottom of telescopic link articulates there is leaning on the ground head, alarm unit, radio detection device and second pressure detection sensor all are connected with the microprocessor electricity.

2. The risk assessment system for predicting a fall of a patient with sarcopenia according to claim 1, wherein: the radio detection device transmits short wave signals to the lower limb parts of the old, and the positions of the lower limb parts in the space are obtained.

3. The risk assessment system for predicting a fall of a patient with sarcopenia according to claim 1, wherein: the tremor frequency sensor is bound on the part of the lower limb of the elderly, which is easy to tremor or shake, through the human body wearing device, and is used for measuring the tremor frequency of muscles of legs or the tremor frequency of knee joints, and the tremor frequency sensor is in wireless communication connection with the microprocessor.

4. The risk assessment system for predicting a fall of a patient with sarcopenia according to claim 1, wherein: the first pressure detection sensor is arranged on a shoe worn by the old.

5. The risk assessment system for predicting a fall of a patient with sarcopenia according to claim 4, wherein: the first pressure detection sensors are respectively arranged at the heels, the front soles and the outer sides of the soles of the human body corresponding to the insoles or the shoes, and are used for respectively detecting the pressure at the heels, the front soles and the outer sides of the soles.

6. A risk assessment system for predicting the fall of a patient with sarcopenia as claimed in any one of the claims 1 to 5 wherein: the lower part of the second pressure detection sensor is connected with the spring, the lower part of the spring is connected with the telescopic rod, the top end of the telescopic rod and the bottom end of the loop bar are both provided with limiting rings, and the telescopic rod can freely slide in the loop bar under the action of the limiting rings and the spring.

7. A risk assessment method for predicting a fall of a patient with sarcopenia, which is used in the risk assessment system for predicting a fall of a patient with sarcopenia as described in any one of the claims 1 to 6, wherein:

and step 3: calculating walking postures, and determining a fall risk assessment function eta (N1, N2, N3) according to L1, L2 and L3, wherein N1 is L1/L1, N2 is L2/L2, and N3 is L3/L3

In the above formulas, L1 is the standard pressure time equivalent during walking, L2 is the standard frequency time equivalent during walking, L3 is the standard image time equivalent during walking, and L1, L2, and L3 are obtained from historical data.

8. The risk assessment method for predicting the fall of a patient with sarcopenia according to claim 7, wherein: after step 3 is completed, step 4 is further included, where step 4 is specifically as follows:

1) when N1 is less than 1, N2 is less than 1, and N3 is less than 1, eta is 0, the interpretation result is 'abnormal, fall risk is moderate', and parameter detection is continued;

4) when N1 is greater than 1, N2 is greater than 1, and N3 is greater than 1, eta is 3, the interpretation result is that the danger and the falling risk are high, and the control alarm unit sends out audible and visual alarm;

5) when N1 is less than 1, N2 is greater than 1, and N3 is less than 1, eta is 4, the interpretation result is abnormal, the fall risk is moderate, and the parameter detection is continued;

6) when N1 is less than 1, N2 is more than 1, and N3 is more than 1, eta is 5, the interpretation result is 'danger and fall risk is high', and the control alarm unit gives out sound and light alarm.

9. The risk assessment method for predicting the fall of a patient with sarcopenia according to claim 7, wherein: the step 1 of acquiring the pressure parameters specifically comprises:

first pressure detection sensors are arranged in the left and right shoes of the old, and the pressure of the heel, the front sole and the outer side of the sole detected by the left foot is respectively F11, F12 and F13; the pressure at the heel, the forefoot and the lateral side of the sole detected by the right foot is respectively F21, F22 and F23; the pressure detected by the second pressure detection sensor is F31.

10. The risk assessment method for predicting the fall of a patient with sarcopenia according to claim 9, wherein: step 2 further comprises:

V1＝(F11*t1+F12*t2+F13*t3+F21*t4+F22*t5+F23*t6+F31*t7)/M_{human being} (1)

L1＝V1/(F0*∑t_i) (2)