Orthopedic intelligent nursing system and method based on Internet of Things

Abstract

The invention relates to the technical field of medical data processing, and discloses an orthopedic intelligent nursing system and method based on the Internet of Things, and the system comprises the following modules: a pre-evaluation module which is used for collecting the personal comprehensive information of a patient and carrying out the comprehensive evaluation of the patient; the rehabilitation training monitoring module comprises a wearing monitoring unit and a guiding unit; the wearable monitoring unit is arranged at a specified body part of a patient according to a corresponding rehabilitation training requirement and obtains corresponding rehabilitation training process data, and the guiding unit is used for guiding the patient to perform rehabilitation training; and the online evaluation module is used for calculating and obtaining rehabilitation training action deviation data according to the rehabilitation training process data and obtaining rehabilitation training indexes in combination with the personal comprehensive information. Rehabilitation training indexes are obtained through the personal comprehensive information of the patient and the rehabilitation training action deviation data, and therefore the current recovery condition of the patient can be judged more comprehensively and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and in particular to an orthopedic intelligent nursing system and method based on the Internet of Things. Background Art

[0002] Rehabilitation training and nursing for orthopedic patients is a key link in promoting functional recovery and preventing complications. Because fractures are also accompanied by severe damage to surrounding tissues and the recovery period is long, the rehabilitation training and nursing process is very important.

[0003] In the existing technology, most of the mid- and late-stage rehabilitation training and care for orthopedic patients are assisted by equipment. In the mid-stage, passive or active training is used to prevent joint stiffness. In the late stage, muscle strength and endurance training is carried out to restore daily activities and gradually bear weight to rebuild bone strength.

[0004] However, in the existing technology, medical personnel will directly evaluate the patient's recovery status by observing the patient's rehabilitation training movements during rehabilitation training. This is not accurate due to subjective factors, which brings uncertainty to subsequent risk assessments.

[0005] To this end, the present invention proposes an orthopedic intelligent nursing system and method based on the Internet of Things to address the shortcomings of the existing technology. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent orthopedic care system and method based on the Internet of Things to solve the problem that in the prior art, medical personnel's observation and assessment of the patient's recovery status during rehabilitation training is not accurate due to subjective factors, which leads to uncertainty in subsequent risk assessment.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An IoT-based orthopedic smart care system includes the following modules:

[0009] A pre-assessment module is used to collect comprehensive personal information of the patient and conduct a comprehensive assessment of the patient;

[0010] Rehabilitation training monitoring module, including wearable monitoring unit and guidance unit;

[0011] The wearable monitoring unit is set on a designated body part of the patient according to the corresponding rehabilitation training requirements to obtain corresponding rehabilitation training process data, and the guiding unit is used to guide the patient to perform the rehabilitation training;

[0012] An online evaluation module, configured to calculate and obtain rehabilitation training movement deviation data based on the rehabilitation training process data, and obtain rehabilitation training indicators in combination with the personal comprehensive information;

[0013] A rehabilitation care management module, used to record and schedule the execution of rehabilitation training for the patient;

[0014] The rehabilitation training evaluation module is used to evaluate the patient's current recovery status based on the rehabilitation training indicators.

[0015] Preferably, the comprehensive personal information includes the patient's current age A and osteoblast ratio Oste;

[0016] The working process of the online evaluation module includes:

[0017] a, performing age assessment and osteogenesis assessment based on the current age A and the osteoblast ratio Oste;

[0018] b, frequency assessment based on rehabilitation training process data;

[0019] c. Based on the rehabilitation training movement deviation data Q and the average value of historical rehabilitation training movement deviation data Conduct movement deviation assessments;

[0020] d. Obtain the rehabilitation training index G by comprehensive calculation based on the personal comprehensive information, age assessment result, osteogenesis assessment result, frequency assessment result and movement deviation assessment result.

[0021] Preferably, the rehabilitation training movement deviation data Q is obtained by the following formula:

[0022] Q=αη m +(1-α)h m

[0023] Among them, Q is the deviation data of rehabilitation training action, α is the weight coefficient, η m is the infrared synchronous receiving index of the patient during the rehabilitation training cycle, h m It is an indicator of the sound wave detection distance during the patient's rehabilitation training cycle.

[0024] Preferably, when the rehabilitation training is limb synchronization training, the infrared synchronization reception index η m The methods for obtaining include:

[0025] Calculate and obtain the infrared synchronous receiving data η corresponding to the jth group of rehabilitation training actions j ,Each set of rehabilitation training movements includes n movement cycles;

[0026]

[0027] According to the preset data screening rules, the infrared synchronous receiving data n j After screening, calculate and obtain the infrared synchronous reception index η m ;

[0028]

[0029] Among them, T leftk T is the time it takes for the left limb to complete the kth rehabilitation training action, rightk T is the time it takes for the right limb to complete the kth rehabilitation training action, left，total T is the longest time to complete the left limb rehabilitation training action, right，total The longest completion time of the right limb rehabilitation training action, s j The screening factor is obtained according to the preset data screening rule.

[0030] Preferably, the acoustic wave detection distance indicator h m The methods for obtaining include:

[0031] Calculate and obtain the acoustic wave detection distance data h corresponding to the infrared non-synchronization moment in the jth group of rehabilitation training movements j :

[0032]

[0033] After filtering the acoustic wave detection distance data according to the preset data screening rules, the acoustic wave detection distance index h is calculated and obtained. m :

[0034]

[0035] Among them, h li h is the height of the left limb sound wave transmitter from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, ri is the height of the right limb sound wave receiver from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, d lr is the distance between the sound wave transmitter worn on the left limb and the sound wave receiver worn on the right limb, v is the speed of sound propagation at the current temperature, t 声 It is the time from when the sound wave transmitter transmits the sound wave to when the sound wave receiver receives the sound wave.

[0036] Preferably, the screening factor s j The following judgment logic is used:

[0037]

[0038] Among them, η min is the worst infrared synchronous reception index in the historical data of patients of the same age group, h maxThis is the maximum acoustic wave detection distance in historical data for patients of the same age group.

[0039] Preferably, whether the rehabilitation training plan is in line with the patient's current condition is determined by referring to the following inequality:

[0040] G>G max

[0041] G min <G≤G max

[0042] G <G min

[0043] Among them, G is the rehabilitation training index of the patient's current state, G max is the maximum value of the historical rehabilitation training index of the patient’s age group, G min The minimum historical rehabilitation training index of the patient's age group;

[0044] When the patient's rehabilitation training index G is at the historical maximum rehabilitation training index G max The time between the minimum value of historical rehabilitation training indicators G min , then it is determined that the current rehabilitation plan is in line with the patient's current condition;

[0045] When the patient's rehabilitation training index G is less than the historical minimum rehabilitation training index G min When , it is determined that the current rehabilitation plan is not suitable for the patient's current condition;

[0046] When the patient's rehabilitation training index G is greater than the historical maximum rehabilitation training index G max When the patient's current condition is close to complete recovery, it is judged that the patient's current condition is close to complete recovery.

[0047] The present invention also provides an orthopedic intelligent nursing method based on the Internet of Things, comprising the following steps:

[0048] S1. The nursing staff collects the patient's personal comprehensive information through the pre-assessment module and conducts a comprehensive assessment of the patient;

[0049] S2. The nurse puts on the wearable monitoring unit for the patient, guides the patient to perform straight leg raising rehabilitation training, and records the rehabilitation training data;

[0050] S3. Calculate and obtain rehabilitation training movement deviation data based on the rehabilitation training process data, and obtain rehabilitation training indicators in combination with the personal comprehensive information;

[0051] S4. Recording and scheduling the rehabilitation training for the patient;

[0052] S5. Evaluate the patient's current recovery status based on the patient's rehabilitation training indicators.

[0053] Beneficial effects of the present invention:

[0054] 1. The present invention obtains the personal comprehensive information of patients with knee fractures through a pre-evaluation module, and then uses a monitoring instrument to collect rehabilitation training data, thereby obtaining rehabilitation training movement deviation data, and finally obtaining rehabilitation training indicators, so as to more comprehensively and accurately judge the patient's current recovery status, solving the problem in the existing technology that medical staff are not accurate in judging the patient's recovery status due to subjective factors.

[0055] 2. The present invention uses infrared synchronous receiving indicators and sound wave detection distance to judge the standard degree of rehabilitation training movements when the patient is undergoing rehabilitation training. If the movement is not standard, an alarm will be issued in time to remind the patient to adjust the movement; if the movement is too difficult, the medical staff will be reminded to reduce the intensity of rehabilitation training.

[0056] 3. The present invention obtains weight coefficients based on rehabilitation training movement deviation data, infrared synchronization data indicators and sound wave detection distance of patients during the rehabilitation cycle, combined with the type of monitoring instrument worn by the patients, and finally obtains accurate rehabilitation training movement deviation data, thereby providing accurate data for the patients' rehabilitation training indicators.

[0057] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 This is a schematic diagram of the system framework of an orthopedic intelligent nursing system and method based on the Internet of Things of the present invention.

[0060] Figure 2 This is a flowchart of the steps of an orthopedic intelligent nursing system and method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0062] See also Figure 1 As shown, the present invention is an orthopedic intelligent nursing system based on the Internet of Things, which includes the following modules:

[0063] Pre-assessment module, used to collect comprehensive personal information of patients and conduct comprehensive assessment of patients;

[0064] Specifically, the pre-assessment module uses a standardized scale to realize automated data collection, covering admission assessment, daily living ability assessment, and special risk assessments such as falls, pressure sores, venous thrombosis, and nutrition. The patient's comprehensive personal information includes the patient's past medical history, the patient's current age, whether the patient smokes, drinking habits, and the patient's current osteoblast ratio (Oste).

[0065] Rehabilitation training monitoring module, including wearable monitoring unit and guidance unit;

[0066] The wearable monitoring unit is set on the patient's designated body part according to the corresponding rehabilitation training requirements, and obtains the corresponding rehabilitation training process data and rehabilitation training movement deviation data. The guidance unit is used to guide the patient to perform rehabilitation training;

[0067] Specifically, the monitoring device worn by the patient includes an infrared transmitter, an infrared receiver, an acoustic wave transmitter, and an acoustic wave receiver. The receiving area of the monitoring device is divided into three categories according to age groups: young people (18-44 years old) (people under 18 years old also use the young people's model), middle-aged people (45-60 years old), and elderly people (over 60 years old).

[0068] Specifically, the older the patient, the slower their movements and the greater the error. Therefore, the larger the receiving area of the monitoring instrument should be. The infrared transmitter and the sound wave wearer should be worn on the patient's left limb, and the infrared receiver and the sound wave receiver should be worn on the patient's right limb. Ensure that there is no obstruction between the infrared transmitter and the infrared receiver, and between the sound wave transmitter and the sound wave receiver. At the same time, the infrared monitoring instrument and the sound wave monitoring instrument should be worn in a vertical relationship and fit together.

[0069] After the monitoring device is put on, the patient is guided to conduct rehabilitation training to promote recovery of the fracture site.

[0070] Online evaluation module, used to calculate and obtain rehabilitation training movement deviation data based on rehabilitation training process data, and obtain rehabilitation training indicators based on personal comprehensive information;

[0071] Specifically, during the rehabilitation training process, the patient collects rehabilitation training process data, and calculates rehabilitation training movement deviation data based on the rehabilitation training process data to determine whether the patient's movements are qualified and effective. Based on the patient's personal comprehensive information and the rehabilitation training movement deviation data, the patient's rehabilitation training indicators are obtained.

[0072] The working process of the online assessment module includes:

[0073] a, Age assessment and osteogenesis assessment based on current age A and osteoblast ratio Oste;

[0074] b. Evaluate the frequency of rehabilitation training of patients based on the rehabilitation training process data;

[0075] c. Based on the rehabilitation training movement deviation data Q and the average value of historical rehabilitation training movement deviation data Conduct movement deviation assessments;

[0076] d. Rehabilitation training index G is obtained by comprehensive calculation based on personal comprehensive information, age assessment results, osteogenesis assessment results, frequency assessment results, and movement deviation assessment results.

[0077] Rehabilitation care management module, used to record and schedule the execution of patients' rehabilitation training;

[0078] Specifically, the rehabilitation nursing management module supports the entry of structured electronic medical records, realizes the automatic archiving of nursing documents and seamless connection of cross-shift information, and significantly reduces the error rate of manual records. The examination appointment module links the hospital HIS system through IoT devices, intelligently allocates examination time periods and pushes reminders in real time, solving the efficiency bottleneck of traditional manual appointments. The execution order management module relies on RFID technology to track the execution status of medical orders, automatically verifies the completion of nursing operations and generates visual reports, realizes real-time interaction of ward terminal device data through edge computing gateways, and uses lightweight encrypted transmission to protect patient privacy and security, greatly reducing the time for writing nursing documents, improving the response speed of risk assessment, and reducing the missed detection rate of medical order execution. At the same time, it provides support for clinical decision-making through big data analysis, effectively promoting the standardization and precision of orthopedic nursing services.

[0079] The recovery assessment module is used to evaluate the patient's current recovery status based on rehabilitation training indicators.

[0080] The rehabilitation training index G is obtained using the following formula:

[0081]

[0082] Among them, G is the rehabilitation training index, A is the patient's current age, A0 is the minimum age of the preset age group, Oste is the osteoblast ratio, and Q is the patient's rehabilitation training movement deviation data. is the average deviation of historical rehabilitation training movements from the data, f is the training frequency during the patient's rehabilitation training cycle, and f i is the preset training frequency.

[0083] In this embodiment, the rehabilitation training index is set to G, A0 is the minimum age of the preset age group. For example, if the patient is a 20-year-old young person, A0 is set to 18 years old, the minimum value of the young age group (18-44 years old). Oste is the osteoblast percentage. Based on the patient's personal comprehensive information, the osteoblast percentage Oste in the patient's body is calculated to determine the patient's bone reconstruction efficiency, thereby better providing the patient with a rehabilitation training plan.

[0084]

[0085] Among them, f is the training frequency during the patient's rehabilitation training cycle, e is the patient's training period, and s is the number of rehabilitation training movements during the patient's rehabilitation training cycle.

[0086] Specifically, when patients visit the hospital, medical staff will set up a rehabilitation training plan for them based on their current age and the proportion of osteoblasts in their body. During the rehabilitation process, medical staff will judge the patient's current recovery status based on the rehabilitation training movement deviation data and training frequency.

[0087] The rehabilitation training movement deviation data Q is obtained by the following formula:

[0088] Q=αη m +(1-α)h m

[0089] Among them, Q is the deviation data of rehabilitation training action, α is the weight coefficient, η m is the infrared synchronous receiving index of the patient during the rehabilitation training cycle, h m It is an indicator of the sound wave detection distance during the patient's rehabilitation training cycle.

[0090] In this embodiment, the patient's rehabilitation training movement deviation data Q consists of two parts: infrared synchronous reception index η m Distance from acoustic wave detection h m ;

[0091] Before the patient undergoes rehabilitation training, the patient wears an infrared transmitter and an infrared receiver on their left and right limbs respectively. When the patient's left and right limbs are approximately in the same position, the infrared rays emitted by the infrared transmitter can be received by the infrared receiver. When the patient's left and right limbs perform rehabilitation training movements respectively, the infrared transmitter and infrared receiver worn by the patient will move synchronously with the patient's left and right limbs. The time it takes for the infrared receiver to successfully receive the infrared rays during the rehabilitation training process can be used to determine whether the patient's movements are standard and consistent during the rehabilitation training process.

[0092] And the completeness of the patient's rehabilitation training movements can be recorded throughout the process through the infrared emitter and the infrared sensor. The position can be determined by using the infrared emitter and the infrared sensor, so that the patient's movements can be judged. When the infrared emitter and the infrared sensor do not reach the designated position after a complete movement trajectory or do not reach the signal designated position at all, a prompt will be issued to the patient, and the infrared synchronization data and the sound wave detection data of this time will not be adopted. When the infrared emitter and the infrared sensor reach the designated position after a complete trajectory, no prompt will be issued, and the infrared synchronization data and the sound wave detection data of this action will be adopted.

[0093] α is the weight coefficient, which is related to the monitoring instrument worn by the patient. The receiving area of the monitoring instrument worn by patients of different age groups is different. For example, when the patient is older, the action time is longer and the synchronization is worse than that of young people, which will cause the infrared synchronization fluctuation to be larger. Therefore, the receiving area of the monitoring instrument worn needs to be increased. After using a monitoring instrument with a larger area, the infrared synchronization reception index η m The data will perform better, so we need to further focus on the acoustic detection distance indicator h m data, so the weight coefficient α needs to be reduced.

[0094] In summary, combining the weight coefficient α with the infrared synchronous reception index η of the patient's recovery period m Distance index h from acoustic wave detection m , the rehabilitation training movement deviation data Q is obtained, and combined with the patient's personal information and other historical data, the final rehabilitation training index G is obtained according to the rehabilitation training index formula.

[0095] When the rehabilitation training is limb synchronization training, such as synchronous bending training of the upper limbs, the left and right upper arms need to be bent synchronously. At this time, the patient wears an infrared transmitter on the left upper limb and an infrared receiver on the right upper limb. Then the patient is guided to perform synchronous bending training on both upper limbs at the same time. The infrared synchronous reception index η m The methods for obtaining include:

[0096] Calculate and obtain the infrared synchronous receiving data η corresponding to the jth group of rehabilitation training actions j ,Each set of rehabilitation training movements includes n movement cycles;

[0097]

[0098] Among them, T leftk T is the time it takes for the left limb to complete the kth rehabilitation training action, rightk T is the time it takes for the right limb to complete the kth rehabilitation training action, left，total T is the longest time to complete the left limb rehabilitation training action, right，tital The longest completion time for right limb rehabilitation training movements.

[0099] Specifically, when the patient's left and right limbs perform a rehabilitation training action, the completion time of the left limb is recorded as T leftk , the completion time of the right limb is recorded as T rightk , and the synchronization time when the infrared receiver successfully receives the infrared ray can be determined based on the minimum time of the action completed by the left and right limbs, and recorded as min(T leftk , T rightk );

[0100] The patient has n action cycles in a rehabilitation training cycle j, and the total time for the patient's left and right limbs to complete the action in this cycle is T left，total With T right，total .

[0101] Thus, according to the infrared synchronous receiving data η j formula:

[0102]

[0103] In the patient's rehabilitation training cycle j, the sum of the longest time to complete each rehabilitation training action and the shortest time to complete each rehabilitation training action are selected to obtain the infrared synchronous receiving data η of this rehabilitation training cycle. j .

[0104] According to the preset data screening rules, the infrared synchronous receiving data n j After screening, calculate and obtain the infrared synchronous reception index η m ;

[0105]

[0106] Among them, s j is the screening factor obtained according to the preset data screening rules, Δt η The infrared synchronous receiving data η in this cycle j The total number of action cycles.

[0107] In addition to the above-mentioned synchronized movement training, coordination training can also be used to evaluate the integrity of a specified movement using mature motion capture-related technologies. Through synchronized movement training and coordination training, the patient's current recovery status can be more accurately judged.

[0108] While the patient is doing synchronous bending training of the upper limbs, the patient needs to wear a sound wave transmitter on the left upper limb and a sound wave receiver on the right upper limb. The sound wave receiver receives the sound waves emitted by the sound wave transmitter, and the straight-line distance between the sound wave transmitter and the sound wave receiver can be determined, thereby obtaining the position difference between the left upper limb and the right upper limb. The sound wave detection distance index h m The methods for obtaining include:

[0109] Calculate and obtain the acoustic wave detection distance data h corresponding to the infrared non-synchronization moment in the jth group of rehabilitation training movements j :

[0110]

[0111] Among them, h li h is the height of the left limb sound wave transmitter from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, ri is the height of the right limb sound wave receiver from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, d lr is the distance between the sound wave transmitter worn on the left limb and the sound wave receiver worn on the right limb, v is the speed of sound propagation at the current temperature, t 声 It is the time from when the sound wave transmitter transmits the sound wave to when the sound wave receiver receives the sound wave.

[0112] In this embodiment, the patient is equipped with a sound wave transmitter and a sound wave receiver. The main purpose is to cooperate with the infrared transmitter and infrared receiver to detect when the patient's infrared is out of sync during rehabilitation training and obtain the distance difference between the patient's left and right upper limbs.

[0113] Specifically, by obtaining the time for the patient's left and right limbs to complete the movements recorded during the rehabilitation training, the time when the patient's left and right limbs are out of sync during the rehabilitation training can be obtained. When the left and right limbs are out of sync, the distance between the sound wave transmitter and the sound wave receiver can be obtained based on the process of the sound wave transmitter transmitting the sound wave and the sound wave receiver receiving the sound wave, thereby obtaining the complete distance data between the patient's left and right limbs;

[0114] By obtaining the height h of the acoustic wave transmitter worn by the patient's left limb from the ground during rehabilitation training li Height h from the sound wave receiver worn on the right limb ri , we can get the height difference between the left and right limbs of the patient during rehabilitation training (hli -h ri ), and the distance between the acoustic wave transmitter and the acoustic wave sensor worn on the left and right limbs can be recorded as the distance d between the patient's legs. lr , thus the acoustic wave detection distance can be obtained

[0115] Similarly, the sound wave detection distance h between the sound wave transmitter and the sound wave receiver can also be obtained based on the propagation speed of the sound wave at the current temperature and the time it takes for the sound wave receiver to receive the sound wave emitted by the sound wave transmitter. j =vt 声 , and then reversely deduce the height difference between the patient's left and right upper limbs;

[0116] According to the preset data screening rules, the acoustic wave detection distance data h j After screening, calculate and obtain the acoustic wave detection distance index h m :

[0117]

[0118] Where Δt h h is the acoustic wave detection distance data within this period j The total number of detections, according to the obtained acoustic wave detection index h m , combined with infrared synchronous receiving index η m , that is, during the patient's rehabilitation training process, the data of the patient's left and right limb rehabilitation movements are recorded, so as to further obtain the rehabilitation training movement deviation data Q.

[0119] Screening factors j The following judgment logic is used:

[0120]

[0121] Among them, η min is the worst infrared synchronous reception index in the historical data of patients of the same age group, h max The maximum acoustic wave detection distance in historical data of patients of the same age group;

[0122] In this embodiment, the screening factor s j The main purpose is to comprehensively judge the standard degree of the patient's action when performing rehabilitation training. When the patient performs rehabilitation training, the infrared synchronous reception index η obtained by the monitoring equipment is used. m Distance from acoustic wave detection h m , judge whether the patient's action is standard and qualified, and the infrared synchronous receiving data η j Distance data h from acoustic wave detection j Is it valid?

[0123] Screening factors j The judgment results are as follows:

[0124] If h yi <h yimax ,η min <η simultaneously satisfies, then s j The output is 1, indicating that the patient's action is standard and the infrared synchronous receiving data η j Distance data h from acoustic wave detection j Effective adoption;

[0125] If h j <h max or η min <η j Or other, then s j The output is 0, and a prompt alarm is issued to the patient to remind the patient to adjust the rehabilitation training action. The infrared synchronous receiving data η j Distance data h from acoustic wave detection j Invalid and not to be used;

[0126] Whether the rehabilitation training plan is suitable for the patient's current condition refers to the following inequality:

[0127] G>G max

[0128] G min <G≤G max

[0129] G <G min

[0130] Among them, G is the rehabilitation training index of the patient's current state, G max is the maximum value of historical rehabilitation training indicators similar to the patient's personal comprehensive information, G min The minimum value of historical rehabilitation training indicators similar to the patient's personal comprehensive information;

[0131] When the patient's rehabilitation training index G is at the historical maximum rehabilitation training index G max The time between the minimum value of historical rehabilitation training indicators G min , then it is determined that the current nursing rehabilitation plan is in line with the patient's current condition;

[0132] When the patient's rehabilitation training index G is less than the historical minimum rehabilitation training index G min When the patient is in a critical condition, the current nursing and rehabilitation plan is deemed inappropriate for the patient's current condition;

[0133] When the patient's rehabilitation training index G is greater than the historical maximum rehabilitation training index G max When the fracture status of the patient is close to complete recovery,

[0134] Compare the patient's final rehabilitation training indicators with the historical rehabilitation training indicators similar to the patient's personal information to determine the patient's current recovery status and the adaptability of rehabilitation training, and make real-time adjustments to the patient to ensure that the patient can recover as soon as possible.

[0135] See also Figure 2 As shown, the present invention also provides an orthopedic intelligent nursing method based on the Internet of Things, comprising the following steps:

[0136] S1. Nursing staff collects the patient's comprehensive personal information through the pre-assessment module and conducts a comprehensive assessment of the patient;

[0137] S2. The nurse puts on the wearable monitoring unit for the patient, guides the patient to perform straight leg raising rehabilitation training, and records the rehabilitation training data;

[0138] S3. Calculate and obtain rehabilitation training movement deviation data based on the rehabilitation training process data, and obtain rehabilitation training indicators based on the individual's comprehensive information;

[0139] S4. Record and schedule the patient's rehabilitation training;

[0140] S5. Evaluate the patient's current recovery status based on the patient's rehabilitation training indicators.

[0141] This nursing method is applied to the above-mentioned IoT-based orthopedic intelligent nursing system, and the effect obtained is the same as the above-mentioned effect, which will not be described in detail here.

[0142] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. An orthopedic intelligent nursing system based on the Internet of Things, characterized by: Includes the following modules: A pre-assessment module is used to collect comprehensive personal information of the patient and conduct a comprehensive assessment of the patient; Rehabilitation training monitoring module, including wearable monitoring unit and guidance unit; The wearable monitoring unit is set on a designated body part of the patient according to the corresponding rehabilitation training requirements to obtain corresponding rehabilitation training process data, and the guiding unit is used to guide the patient to perform the rehabilitation training; An online evaluation module, configured to calculate and obtain rehabilitation training movement deviation data based on the rehabilitation training process data, and obtain rehabilitation training indicators in combination with the personal comprehensive information; A rehabilitation care management module, used to record and schedule the execution of rehabilitation training for the patient; The rehabilitation training evaluation module is used to evaluate the patient's current recovery status based on the rehabilitation training indicators.

2. The orthopedic intelligent nursing system based on the Internet of Things according to claim 1 is characterized in that: The personal comprehensive information includes the patient's current age A and osteoblast ratio Oste; The working process of the online evaluation module includes: a, performing age assessment and osteogenesis assessment based on the current age A and the osteoblast ratio Oste; b. Evaluate the frequency of rehabilitation training of patients based on the rehabilitation training process data; c. Perform movement deviation assessment based on the rehabilitation training movement deviation data Q and the average value Q of historical rehabilitation training movement deviation data; d. Obtain the rehabilitation training index G by comprehensive calculation based on the personal comprehensive information, age assessment result, osteogenesis assessment result, frequency assessment result and movement deviation assessment result.

3. The orthopedic intelligent nursing system based on the Internet of Things according to claim 2 is characterized in that: The rehabilitation training movement deviation data Q is obtained by the following formula: Q=αη m +(1-a)h m Among them, Q is the deviation data of rehabilitation training action, α is the weight coefficient, η m is the infrared synchronous receiving index of the patient during the rehabilitation training cycle, h m It is an indicator of the sound wave detection distance during the patient's rehabilitation training cycle.

4. The orthopedic intelligent nursing system based on the Internet of Things according to claim 2 is characterized in that: When the rehabilitation training is limb synchronization training, the infrared synchronization receiving index η m The methods for obtaining include: Calculate and obtain the infrared synchronous receiving data η corresponding to the jth group of rehabilitation training actions j ,Each set of rehabilitation training movements includes n movement cycles; According to the preset data screening rules, the infrared synchronous receiving data n j After screening, calculate and obtain the infrared synchronous reception index η m ; Among them, T leftk Y is the time it takes for the left limb to complete the kth rehabilitation training action, rightk T is the time it takes for the right limb to complete the kth rehabilitation training action, left，total T is the longest time to complete the left limb rehabilitation training action, right，total The longest completion time of the right limb rehabilitation training action, s j is the screening factor obtained according to the preset data screening rule, Δt η The infrared synchronous receiving data η in this cycle j The total number of action cycles.

5. The orthopedic intelligent nursing system based on the Internet of Things according to claim 3 is characterized in that: The acoustic wave detection distance indicator h m The methods for obtaining include: Calculate and obtain the acoustic wave detection distance data h corresponding to the infrared non-synchronization moment in the jth group of rehabilitation training movements j : After filtering the acoustic wave detection distance data according to the preset data screening rules, the acoustic wave detection distance index h is calculated and obtained. m : Among them, h li h is the height of the left limb sound wave transmitter from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, ri is the height of the right limb sound wave receiver from the ground when the infrared is not synchronized during the k-th movement rehabilitation training, d lr is the distance between the sound wave transmitter worn on the left limb and the sound wave receiver worn on the right limb, v is the speed of sound propagation at the current temperature, t 声 Δt is the time from when the sound wave transmitter transmits the sound wave to when the sound wave receiver receives the sound wave. h h is the acoustic wave detection distance data within this period j The total number of detections.

6. The orthopedic intelligent nursing system based on the Internet of Things according to claim 4 is characterized in that: The screening factor s j The following judgment logic is used: Among them, η min is the worst infrared synchronous reception index in the historical data of patients of the same age group, h max This is the maximum acoustic wave detection distance in historical data for patients of the same age group.

7. The orthopedic intelligent nursing system based on the Internet of Things according to claim 2 is characterized in that: Whether the rehabilitation training plan is suitable for the patient's current condition is determined by the following inequality: G>G max G min <G≤G max G<G min Among them, G is the rehabilitation training index of the patient's current state, G max is the maximum value of the historical rehabilitation training index of the patient’s age group, G min The minimum historical rehabilitation training index of the patient's age group; When the patient's rehabilitation training index G is at the historical maximum rehabilitation training index G max The time between the minimum value of historical rehabilitation training indicators G min , then it is determined that the current rehabilitation plan is in line with the patient's current condition; When the patient's rehabilitation training index G is less than the historical minimum rehabilitation training index G min When , it is determined that the current rehabilitation plan is not suitable for the patient's current condition; When the patient's rehabilitation training index G is greater than the historical maximum rehabilitation training index G max When the patient's current condition is close to complete recovery, it is judged that the patient's current condition is close to complete recovery.

8. An orthopedic intelligent nursing method based on the Internet of Things, applicable to any one of the orthopedic intelligent nursing systems based on the Internet of Things according to claims 1-7, characterized in that: The following steps are involved: S1. The nursing staff collects the patient's personal comprehensive information through the pre-assessment module and conducts a comprehensive assessment of the patient; S2. The nurse puts on the wearable monitoring unit for the patient, guides the patient to perform straight leg raising rehabilitation training, and records the rehabilitation training data; S3. Calculate and obtain rehabilitation training movement deviation data based on the rehabilitation training process data, and obtain rehabilitation training indicators in combination with the personal comprehensive information; S4. making an appointment and recording the execution of the rehabilitation training for the patient; S5. Evaluate the patient's current recovery status based on the patient's rehabilitation training indicators.

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