A control method for a medical simulation robot

Abstract

The application relates to a control method of a medical simulation robot, comprising the following steps: adjusting the temperature of a temperature adjusting instrument of a palm part of the simulation robot through an adaptive temperature control algorithm; based on signals of each joint part of a skeleton, and through a posture control algorithm, the activity posture of each joint of the simulation hand is selected and controlled, so that the simulation robot touches a to-be-medically-comforted part; when in a patting mode, the simulation hand pats the back of a patient; when in a light touching mode, the simulation hand lightly touches the back or chest of the patient; when in a light holding mode, the simulation hand lightly holds the hands of the patient; based on a light sensing signal of the palm part, blood oxygen saturation information of the hands of the patient is acquired, and a health monitoring algorithm is used for analysis, so as to generate a patient health state report. Compared with the prior art, the simulation hand has multiple functions, including comforting, temperature adjustment, posture control, light receptor monitoring and the like, can realize adaptive adjustment, and improves the comfort and satisfaction of the patient.

Description

Technical Field

[0001] This invention relates to the field of simulated hand technology, and in particular to a control method for a medical simulated robotic hand. Background Technology

[0002] With rapid societal development and the increasing severity of aging, the challenges facing healthcare are becoming more complex and diverse. To improve efficiency and reduce the risk of cross-infection, many medical institutions have implemented a no-family-accompaniment policy in departments such as the ICU, NICU, and emergency rooms. The majority of patients in these departments are elderly, infants, and critically ill patients. Entering an unfamiliar environment without family accompaniment, they are prone to separation anxiety, irritability, and other negative emotions. These negative emotions not only affect treatment outcomes but may also trigger a series of adverse clinical behaviors, such as delirium and refusal of treatment, increasing the difficulty and risk of clinical nursing work.

[0003] Currently, there are two main types of methods for addressing patient emotional well-being and physiological monitoring: one is through manual intervention by medical staff, and the other is using simple assistive devices for basic physiological parameter monitoring. However, these existing technologies have many shortcomings.

[0004] First, manual procedures performed by medical staff are not only time-consuming and labor-intensive, but also highly susceptible to subjective factors, making it difficult to guarantee consistency and accuracy. Especially in situations of staff shortages, medical staff cannot consistently and reliably provide necessary reassurance and monitoring services to patients, resulting in unresolved emotional issues. Furthermore, prolonged manual procedures can increase the workload of medical staff, leading to decreased work efficiency and even burnout.

[0005] Secondly, while some currently used assistive devices can provide basic physiological parameter monitoring, their functions are limited and lack diverse and personalized reassurance capabilities. These devices typically only measure single parameters such as heart rate and blood oxygen saturation, failing to provide a comprehensive assessment of the patient's overall health. Furthermore, their use often requires active patient cooperation, which is insufficient for patients with impaired consciousness or low cooperation levels. In addition, existing devices lack interaction with the patient's emotional state during use, failing to play a role in reassuring the patient. Therefore, developing a control method for a medical robotic hand is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a control method for a medical simulated robotic hand. The control method enables the simulated hand to have multiple functions, including soothing, temperature regulation, posture control, and photoreceptor monitoring. Adaptive adjustment is achieved through built-in algorithms to improve patient comfort and satisfaction.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] This invention provides a control method for a medical robotic arm, comprising the following steps:

[0009] A1: The temperature of the temperature regulator in the palm of the simulated robotic hand is adjusted using an adaptive temperature control algorithm;

[0010] A2: Based on signals from various joints of the skeleton, and using a posture control algorithm, the simulated hand's joints are selected and controlled to move in the area requiring medical comfort, achieving the following comforting modes:

[0011] When in tapping mode, a simulated hand gently taps the patient's back.

[0012] When in stroking mode, the simulated hand gently strokes the patient's back or chest;

[0013] In the light grip mode, the simulated hand gently grips the patient's hands;

[0014] A3: In the light grip mode, the system acquires the patient's blood oxygen saturation information based on the light-sensing signal of the palm, and analyzes it based on the health monitoring algorithm to generate a patient health status report.

[0015] Furthermore, in A1, the adaptive temperature control algorithm includes the following formula:

[0016] T set =T patient +ΔT;

[0017] Among them, T set The target temperature set for the temperature regulator of the robotic hand's palm, T patient The current temperature of the patient's skin is ΔT, which is the temperature difference set according to the patient's needs.

[0018] The adaptive temperature control algorithm includes a feedback control-based adjustment mechanism:

[0019]

[0020] Among them, T desired K is the preset temperature required by the patient. p K i and K dThese are the proportional, integral, and derivative control parameters, respectively.

[0021] Furthermore, in A1, the process of adjusting the temperature of the temperature regulator on the palm of the simulated robotic hand includes:

[0022] The heating / cooling unit of the temperature regulator automatically adjusts the temperature to the target temperature T based on the calculation results of the built-in adaptive temperature control algorithm. set ;

[0023] The temperature regulation process of the temperature controller is monitored and adjusted in real time by the built-in temperature regulation feedback control system, so that the temperature is stable within the set range.

[0024] The temperature controller's data and adjustment status are displayed in real time via a touch screen, allowing users to select temperature adjustment modes and set parameters.

[0025] Furthermore, in A2, the process of selecting and controlling the movement postures of each joint of the simulated hand through the posture control algorithm includes:

[0026] Obtain the current joint position data P of the simulated hand current ;

[0027] Receive the target joint position P input by the user. target ;

[0028] Based on joint position data and real-time patient feedback data, the optimized target joint position P is calculated. optimized :

[0029] P optimized =P target +α(P patient_response -P current );

[0030] Among them, P patient_response The data represents the patient's real-time response, and α is the optimization coefficient.

[0031] Calculate the joint position error E = P optimized -P current ;

[0032] The posture control algorithm generates a control signal C based on the joint position error E. The control signal C is calculated using the following formula:

[0033]

[0034] Among them, K p K i and K d These are the proportional, integral, and derivative control parameters, respectively.

[0035] The control signal C is sent to the signal receiver, which controls the servo motors of each joint of the simulated hand to adjust the movement posture of each joint to the optimized target position P. optimized ;

[0036] The built-in posture control algorithm monitors and adjusts joint positions in real time;

[0037] The posture control data and status are displayed in real time via a touch screen, allowing users to select posture control modes and set parameters through the touch screen.

[0038] Furthermore, in A3, the specific process for obtaining information on the patient's blood oxygen saturation includes:

[0039] The infrared light reflection signal from the photoreceptors in the palm area of ​​the simulated robotic hand is acquired, the received signal is converted into an electrical signal, and the corresponding light intensity data I is recorded. 660 and I 940 ;

[0040] The signal processing unit of the simulated robotic arm is used to filter and denoise the acquired electrical signals to extract the effective signal I. 660_eff and I 940_eff ;

[0041] The preprocessed effective signal is transmitted to the built-in health monitoring algorithm for blood oxygen saturation calculation and analysis.

[0042] Calculate the infrared light absorption rate A 660 and A 940 :

[0043]

[0044] Among them, I in_660 and I in_940 The light intensities emitted by the 660nm and 940nm infrared emitters, respectively;

[0045] Based on infrared light absorption rate A 660 and A 940 Calculate blood oxygen saturation SpO2:

[0046]

[0047] Where α and β are constants calibrated based on clinical data.

[0048] Furthermore, in A3, the process of obtaining information about a patient's blood oxygen saturation includes:

[0049] The calculated SpO2 blood oxygen saturation data is compared with the patient's standard healthy data to determine if any abnormalities exist.

[0050] The built-in health monitoring algorithm dynamically analyzes blood oxygen saturation data, generates a patient health status report, and updates the data in real time.

[0051]

[0052] Where γ, δ, and ∈ are constants calibrated based on clinical data, SpO 2,avg This represents the average blood oxygen saturation from historical data.

[0053] Health monitoring data and analysis results are displayed in real time via a touch screen. Users can view detailed health data and reports, including historical data trend charts and current health status assessments.

[0054] Furthermore, in A2, when in tapping mode, the control process of the simulated robotic arm includes the following sub-steps:

[0055] Obtain the current pose data P of the simulated hand current ;

[0056] Calculate the target posture P of the tapping motion tap :

[0057] P tap =P current +ΔP tap ;

[0058] Among them, P tap For the target posture of the tapping action, P current For the current pose data of the simulated hand, ΔP tap Preset tap displacement vector;

[0059] The attitude control algorithm generates control signal C tap :

[0060]

[0061] Among them, C tap For control signals, K p1 K i1 K d1 These are the proportional, integral, and derivative control parameters, respectively.

[0062] Control signal C tap The signal is sent to the signal receiver, where the simulated hand performs a tapping motion.

[0063] Furthermore, in A2, when in stroking mode, the control process of the simulated robotic arm includes the following sub-steps:

[0064] Obtain the current pose data P of the simulated hand current ;

[0065] Calculate the target posture P of the stroking action stroke :

[0066] P stroke =P current +ΔP stroke ;

[0067] P stroke For the target posture of the stroking action, P current For the current pose data of the simulated hand, ΔP stroke This is the preset displacement vector for the gentle touch;

[0068] The attitude control algorithm generates control signal C stroke :

[0069]

[0070] Among them, C stroke For control signals, K p2 K i2 K d2 These are the proportional, integral, and derivative control parameters, respectively.

[0071] Control signal C stroke The signal is sent to the signal receiver, where the simulated hand performs a stroking motion.

[0072] Furthermore, in A2, when in the light grip mode, the control process of the simulated robotic arm includes the following sub-steps:

[0073] Built-in soothing algorithm obtains the current posture data P of the simulated hand. current ;

[0074] Calculate the target posture P of the light grip action grasp :

[0075] P grasp =P current +ΔP grasp ;

[0076] Among them, P grasp For the target posture of the light grip action, P current For the current pose data of the simulated hand, ΔP grasp Preset light grip displacement vector;

[0077] The attitude control algorithm generates control signal C grasp :

[0078]

[0079] Among them, C grasp For control signals, K p3 K i3 Kd3 These are the proportional, integral, and derivative control parameters, respectively.

[0080] Control signal C grasp The signal is sent to the signal receiver, and the simulated hand performs a light gripping motion.

[0081] Furthermore, A2 also includes a constraint mode: the simulated hand is controlled by a constraint algorithm to gently grasp the patient's forearm and apply a preset pressure;

[0082] The constraint algorithm includes the following sub-steps:

[0083] Obtain the pressure data F currently applied by the simulated hand. current ;

[0084] Set target pressure F target Adjust the pressure level according to the patient's needs and condition;

[0085] Calculate the error E of the applied pressure F =F target -F current ;

[0086] Generate pressure control signal C F :

[0087]

[0088] Among them, C F K is the pressure control signal. p4 K i4 K d4 These are the proportional, integral, and derivative control parameters, respectively.

[0089] Pressure control signal C F The signal is sent to the signal receiver, where the simulated hand performs a pressure adjustment action;

[0090] The applied pressure is monitored and adjusted in real time through a constraint algorithm to ensure that the applied pressure is within a safe range and meets the needs of different patients.

[0091] Pressure control data and status are displayed in real time via a touch screen, allowing users to select pressure control modes and set parameters.

[0092] Compared with the prior art, the present invention has the following technical advantages:

[0093] The beneficial effects of this invention are:

[0094] (1) This invention uses a simulated hand "soothing" mode to perform actions such as patting, stroking, and grasping on the patient, simulating the comforting behavior of family members, providing warmth and a sense of security to the patient, and effectively reducing separation anxiety and irritability in patients without family members by their side. The built-in soothing algorithm ensures the precision and gentleness of the actions, improving the patient's comfort and satisfaction.

[0095] (2) The simulated hand of this invention integrates multiple functions such as temperature regulation, posture control, and photoreceptor physiological monitoring, and can be flexibly adjusted and configured according to the specific needs of the patient. Through the touch screen, users can easily select different function modes to realize a variety of clinical operations, which greatly improves the efficiency and flexibility of the equipment.

[0096] (3) The adaptive temperature control algorithm, posture control algorithm, and health monitoring algorithm built into this invention enable the simulated hand to dynamically adjust according to the patient's real-time status, ensuring the accuracy and reliability of various operations. For example, the temperature adjustment algorithm can automatically adjust the temperature of the simulated hand according to the patient's needs, providing a comfortable touch; the posture control algorithm can precisely control each joint of the simulated hand, ensuring natural and effective movements; and the health monitoring algorithm can monitor the patient's blood oxygen saturation and heart rate in real time, providing accurate health data analysis.

[0097] (4) This invention uses a simulated hand "pressing" mode to automatically apply pressure to stop bleeding after blood collection or puncture, saving medical staff time and energy and reducing the burden of manual operation. In addition, the "restraint" mode can effectively control mildly agitated patients, prevent their involuntary movements from interfering with treatment, and ensure the smooth progress of clinical work.

[0098] (5) The photoreceptor and health monitoring algorithm built into the simulated hand of this invention can monitor the patient's blood oxygen saturation and heart rate in real time and generate a detailed health status report. Through the touch screen, users can view historical data trend charts and current health status assessments, promptly identify potential health problems, and receive personalized health advice and care plans.

[0099] (6) All operations of the simulated hand of this invention are controlled by built-in algorithms, ensuring the safety and stability of the operation process. For example, the temperature regulation algorithm and pressure control algorithm can monitor and adjust the operating parameters in real time to prevent discomfort or injury to the patient caused by excessive temperature or pressure. The fixation device and connecting arm of the simulated hand are reasonably designed and can be firmly fixed to the bed rail to prevent accidental fall or movement during use. Attached Figure Description

[0100] Figure 1 This is a schematic diagram illustrating the implementation, use, and control process of a specific solution in the embodiment;

[0101] Figure 2 This is a structural block diagram of a multifunctional simulated hand proposed in this invention. Detailed Implementation

[0102] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0103] Example 1

[0104] For the implementation, use, and control process of this invention, please refer to [link / reference]. Figure 1 and 2 Specifically, it includes the following steps:

[0105] S1. Prepare a simulated hand. The simulated hand is made of simulated silicone material, with an internal metal skeleton for support and movable joints to allow the simulated hand to change postures.

[0106] S2. Install a touch screen on the frame of the simulated hand. The touch screen is used to select the function mode and interact with the built-in artificial intelligence system through user input.

[0107] S3. A temperature regulator is installed at the base of the palm of the simulated hand. The temperature regulator has an adjustment range of 0℃-37℃ and is automatically adjusted according to the patient's needs by a built-in adaptive temperature control algorithm.

[0108] S4. Install signal receivers at each joint of the endoskeleton of the simulated hand. The signal receivers select and control the movement posture of each joint of the simulated hand through a built-in posture control algorithm.

[0109] S5. Install a photoreceptor on the palm of the simulated hand. The photoreceptor is used to measure the patient's blood oxygen saturation by contact with the patient. The data is transmitted to the built-in health monitoring algorithm for analysis.

[0110] S6. The simulated hand is fixed to the bed rail by a fixing device and a connecting arm, and can be disassembled and its position changed at will. The connecting arm is equipped with a sensor for real-time monitoring and adjustment of the simulated hand's position.

[0111] S7. Select the "Soothing" mode via the touchscreen. The mode includes the following sub-steps:

[0112] Select the "tap" function, and a simulated hand will gently tap the patient's back under the control of a built-in soothing algorithm;

[0113] Select the "Soothing" function, and the simulated hand will gently stroke the patient's back or chest under the control of the built-in soothing algorithm;

[0114] Select the "Gentle Grip" function, and the simulated hand will gently grip the patient's hands under the control of the built-in soothing algorithm;

[0115] S8. Select the "Functional Training" mode via the touch screen. The mode includes the following sub-steps:

[0116] Place the back of the patient's hand on the simulated palm and use Velcro to fix the patient's finger joints to the simulated hand;

[0117] The built-in functional training algorithm controls the simulated hand to clench and relax at a certain frequency through a signal receiver, so as to perform hand function training for patients.

[0118] S9. Select the "Press" mode via the touch screen. The mode includes the following sub-steps:

[0119] The built-in pressure control algorithm controls the simulated hand to automatically extend two fingers to press the puncture site after the blood collection is completed;

[0120] Set the compression duration; the built-in compression control algorithm adjusts the compression time based on the patient's coagulation function.

[0121] S10. Select "Monitoring" mode via the touchscreen. The mode includes the following sub-steps:

[0122] Photoreceptors measure oxygen saturation and heart rate while the patient is in contact with the prosthetic hand;

[0123] Measurement data is transmitted to a built-in health monitoring algorithm for analysis and displayed on an electronic screen;

[0124] S11. Select the "Constraint" mode via the touch screen. The mode includes the following sub-steps:

[0125] The built-in constraint algorithm controls the simulated hand to gently grasp the patient's forearm and apply a certain amount of pressure;

[0126] Adjust the pressure level to suit the needs of different patients; suitable for patients with mild agitation.

[0127] S12. Select the "Physical Cooling" mode via the touchscreen. The mode includes the following sub-steps:

[0128] Built-in physical cooling algorithm adjusts the temperature of the simulated hand to 0℃-8℃;

[0129] The simulated hand, gloved, is placed on the patient's groin area. The simulated hand automatically applies pressure to fix the area, thus achieving physical cooling.

[0130] In this embodiment, S3 includes the following steps:

[0131] S31. Install a temperature regulator at the base of the palm of the simulated hand. The temperature regulator has an adjustment range of 0℃-37℃.

[0132] S32. The temperature controller includes a temperature sensor and a heating / cooling unit. The temperature sensor is used to monitor the temperature of the part of the simulated hand that is in contact with the patient in real time.

[0133] S33. Input the data from the temperature sensor into the built-in adaptive temperature control algorithm. The adaptive temperature control algorithm dynamically adjusts the temperature according to the patient's needs and real-time monitoring data.

[0134] S34. The adaptive temperature control algorithm includes the following formulas:

[0135] T set =T patient +ΔT;

[0136] Among them, T set The target temperature set for the temperature controller, T patient The current temperature of the patient's skin is ΔT, which is the temperature difference set according to the patient's needs.

[0137] S35. The adaptive temperature control algorithm incorporates a feedback control-based adjustment mechanism:

[0138]

[0139] Among them, T desired K is the preset temperature required by the patient. p K i and v d These are the proportional, integral, and derivative control parameters, respectively.

[0140] S36. The heating / cooling unit of the temperature controller automatically adjusts the temperature to the target temperature T based on the calculation results of the built-in adaptive temperature control algorithm. set ;

[0141] S37. The temperature regulation process is monitored and adjusted in real time by the built-in temperature regulation feedback control system, so that the temperature is stable within the set range.

[0142] S38. The data and adjustment status of the temperature regulator are displayed in real time via a touch screen. Users can select the temperature adjustment mode and set parameters via the touch screen.

[0143] In this embodiment, S4 includes the following steps:

[0144] S41. Install signal receivers at each joint of the endoskeleton of the simulated hand. The signal receivers are used to receive posture control signals.

[0145] S42. The signal receiver controls the movement posture of each joint of the simulated hand through a built-in posture control algorithm.

[0146] S43. The posture control algorithm includes the following steps:

[0147] S431. Obtain the current joint position data P of the simulated hand. current ;

[0148] S432, Receive the target joint position P input by the user. target ;

[0149] S433. Based on joint position data and real-time patient feedback data, calculate the optimized target joint position P. optimized :

[0150] P optimized =P target +α(P patient_response -P current );

[0151] Among them, P patient_response The data represents the patient's real-time response, and α is the optimization coefficient.

[0152] S43.4 Calculate the joint position error E = P optimized -P current ;

[0153] S44. The posture control algorithm generates a control signal C based on the joint position error E. The control signal C is calculated using the following formula:

[0154]

[0155] Among them, K p K i and K d These are the proportional, integral, and derivative control parameters, respectively.

[0156] S45. The control signal C is sent to the signal receiver, which controls the servo motors of each joint of the simulated hand to adjust the movement posture of each joint to the optimized target position P. optimized ;

[0157] S46. Built-in posture control algorithm monitors and adjusts joint position in real time;

[0158] S47. Posture control data and status are displayed in real time via a touch screen. Users can select posture control modes and set parameters via the touch screen.

[0159] In this embodiment, S5 includes the following steps:

[0160] S51. A photoreceptor is installed in the palm of the simulated hand. The photoreceptor is used to measure the patient's blood oxygen saturation by contact with the patient.

[0161] S52. The photoreceptor includes an infrared emitter and a receiver. The infrared emitter is used to emit an infrared beam, and the receiver is used to receive the reflected infrared beam. The wavelengths of the infrared light are set to 660nm and 940nm, respectively, to measure the absorption of oxyhemoglobin and deoxyhemoglobin.

[0162] S53, the photoreceptor performs data acquisition and preprocessing through a built-in signal processing unit:

[0163] The infrared emitter alternately emits 660nm and 940nm infrared light at a set frequency;

[0164] The receiver synchronously receives the reflected infrared light, converts the received signal into an electrical signal, and records the corresponding light intensity data I. 660 and I 940 ;

[0165] The signal processing unit filters and denoises the acquired electrical signal to extract the effective signal I. 660_eff and I 940_eff ;

[0166] S54. The preprocessed effective signal is transmitted to the built-in health monitoring algorithm for blood oxygen saturation calculation and analysis:

[0167] Calculate the infrared light absorption rate A 660 and A 940 :

[0168]

[0169] Among them, I in_660 and I in_940 The light intensities emitted by the 660nm and 940nm infrared emitters, respectively;

[0170] Based on infrared light absorption rate A 660 and A 940 Calculate blood oxygen saturation SpO2:

[0171]

[0172] Where α and β are constants calibrated based on clinical data;

[0173] The calculated SpO2 blood oxygen saturation data is compared with the patient's standard healthy data to determine if any abnormalities exist.

[0174] The S55 features a built-in health monitoring algorithm that dynamically analyzes blood oxygen saturation data, generates patient health status reports, and updates the data in real time.

[0175]

[0176] Where γ, δ, and ∈ are constants calibrated based on clinical data, SpO 2,avg This represents the average blood oxygen saturation from historical data.

[0177] S56. Health monitoring data and analysis results are displayed in real time via a touch screen. Users can view detailed health data and reports, including historical data trend charts and current health status assessments, through the touch screen.

[0178] S57 features a built-in health monitoring algorithm that provides corresponding health advice based on the patient's real-time blood oxygen saturation data and issues alarm signals to notify medical staff in abnormal situations.

[0179] In this embodiment, S7 includes the following steps:

[0180] S71. Select "Soothing" mode via the touch screen to enter the soothing function selection interface;

[0181] S72. Select the "Tap" function. The Tap function includes the following sub-steps:

[0182] S721, built-in soothing algorithm to obtain current posture data of simulated hand P current ;

[0183] S722, Calculate the target posture P of the tapping motion. tap :

[0184] P tap =P current +P tap ;

[0185] Among them, P tap For the target posture of the tapping action, P current For the current pose data of the simulated hand, ΔP tap Preset tap displacement vector;

[0186] S723, Attitude control algorithm generates control signal C tap :

[0187]

[0188] Among them, C tap For control signals, K p1 K i1 K d1 These are the proportional, integral, and derivative control parameters, respectively.

[0189] S724, control signal c tap The signal is sent to the signal receiver, where a simulated hand performs a tapping motion.

[0190] S73. Select the "Smooth" function. The "Smooth" function includes the following sub-steps:

[0191] S731, built-in soothing algorithm to obtain current posture data of simulated hand P current ;

[0192] S732, Calculate the target posture P of the stroking action. stroke :

[0193] P stroke =P current +ΔP stroke ;

[0194] Among them, P stroke For the target posture of the stroking action, P current For the current pose data of the simulated hand, ΔP stroke This is the preset displacement vector for the gentle touch;

[0195] S733, attitude control algorithm generates control signal C stroke :

[0196]

[0197] Among them, C stroke For control signals, K p2 K i2 K d2 These are the proportional, integral, and derivative control parameters, respectively.

[0198] S734, control signal C stroke The signal is sent to the signal receiver, where a simulated hand performs a stroking motion.

[0199] S74. Select the "Grip" function. The Grip function includes the following sub-steps:

[0200] S741, built-in soothing algorithm to obtain current posture data of simulated hand P current ;

[0201] S742. Calculate the target posture P of the light grip action. grasp :

[0202] P grasp =P current +ΔP grasp ;

[0203] Among them, P grasp For the target posture of the light grip action, P current For the current pose data of the simulated hand, ΔP grasp Preset light grip displacement vector;

[0204] S743, Attitude control algorithm generates control signal C grasp:

[0205]

[0206] Among them, C grasp For control signals, K p3 K i3 K d3 These are the proportional, integral, and derivative control parameters, respectively.

[0207] S744, control signal C grasp The signal is sent to the signal receiver, and the simulated hand performs a light gripping motion.

[0208] In this embodiment, S11 includes the following steps:

[0209] S111. Select "Constraint" mode via the touch screen to enter the constraint function selection interface;

[0210] S112, Built-in constraint algorithm controls the simulated hand to gently grasp the patient's forearm and apply a certain pressure;

[0211] S113. The constraint algorithm includes the following sub-steps:

[0212] S1131. Obtain the pressure data F currently applied by the simulated hand. current ;

[0213] S1132, Set target pressure F target Adjust the pressure level according to the patient's needs and condition;

[0214] S1133, Calculate the error E of the applied pressure. F =F target -F current ;

[0215] S114, Generate pressure control signal C F :

[0216]

[0217] Among them, C F K is the pressure control signal. p4 K i4 K d4 These are the proportional, integral, and derivative control parameters, respectively.

[0218] S115, Transfer pressure control signal C F The signal is sent to the signal receiver, where the simulated hand performs a pressure adjustment action;

[0219] S116. The built-in constraint algorithm monitors and adjusts the applied pressure in real time to ensure that the applied pressure is within a safe range and adapts to the needs of different patients.

[0220] S117. Pressure control data and status are displayed in real time via a touch screen. Users can select pressure control modes and set parameters through the touch screen.

[0221] In this embodiment, the prosthetic hand is made of simulated silicone material, with an internal metal skeleton for support and movable joints to allow for changing postures. A touchscreen is installed on the hand's frame, allowing users to select function modes and interact with the built-in artificial intelligence system. A temperature regulator is installed at the base of the hand, automatically adjusting the temperature according to the patient's needs using a built-in adaptive temperature control algorithm, with a temperature range of 0℃-37℃. Signal receivers are installed at each joint of the internal skeleton, controlling the movement of each joint using a built-in posture control algorithm. A photoreceptor is installed in the palm of the hand to measure the patient's blood oxygen saturation through contact, transmitting the data to a built-in health monitoring algorithm for analysis. The prosthetic hand is fixed to the bed rail using a fixing device and connecting arm, allowing for easy disassembly and repositioning. The connecting arm is equipped with sensors for real-time monitoring and adjustment of the hand's position.

[0222] The prosthetic hand of this invention has multiple functional modes, including a "soothing" mode, a "functional exercise" mode, a "pressing" mode, a "monitoring" mode, and a "restraint" mode, which can be selected and adjusted according to different clinical needs. For example, in "soothing" mode, the prosthetic hand can perform actions such as patting, stroking, and grasping, providing warmth and a sense of security and reducing the patient's anxiety; in "functional exercise" mode, the prosthetic hand can help the patient perform hand function exercises to prevent muscle atrophy; in "pressing" mode, the prosthetic hand can automatically press on the puncture site, saving medical staff time and energy; in "monitoring" mode, the prosthetic hand can monitor the patient's blood oxygen saturation and heart rate data in real time and generate a health status report; in "restraint" mode, the prosthetic hand can gently grasp the patient's forearm and apply appropriate pressure to prevent involuntary movements from interfering with treatment.

[0223] Application Example 1

[0224] Scene Description

[0225] In the intensive care unit (ICU) of a large general hospital, medical staff face a large number of critically ill patients requiring close monitoring every day. These patients are mostly elderly, infants, and seriously ill patients. In unfamiliar environments without family members to accompany them, they are prone to separation anxiety and irritability. Elderly patients, especially those who have been bedridden for a long time, are prone to muscle atrophy and other complications due to limited limb movement. Furthermore, the busy schedules of medical staff make it difficult to dedicate sufficient time and energy to comforting and caring for each patient.

[0226] Application scenarios

[0227] In this context, the hospital implements the solution of this invention to improve the quality of patient care and the work efficiency of medical staff.

[0228] 1. Emotional reassurance:

[0229] A 75-year-old male patient in the ICU required prolonged bed rest following heart surgery. Lacking family members to accompany him, he exhibited significant anxiety and unease daily. The nurse used a touchscreen to select a "soothing" mode for the simulated hand, first choosing the "patting" function. The simulated hand gently patted the patient's back, mimicking a comforting gesture from a family member. Next, the nurse switched to the "stroking" function, where the simulated hand softly stroked the patient's chest and arms, providing continuous psychological comfort. Finally, the nurse selected the "gentle grip" function, where the simulated hand warmly held the patient's hand, conveying a sense of security. After a week of continuous use, the patient's anxiety and irritability significantly decreased, and his sleep quality also improved.

[0230] 2. Functional exercises:

[0231] Another 62-year-old female patient in the ICU suffered from hemiplegia due to a stroke and prolonged bed rest, resulting in severe muscle atrophy in her right hand. The nurse placed her hand in the palm of a prosthetic hand and secured the finger joints with Velcro. By selecting the "functional exercise" mode via a touchscreen, the prosthetic hand clenched and relaxed at a set frequency, providing hand function exercises for the patient. After two weeks of use, the patient's right hand muscle strength recovered, and finger dexterity improved.

[0232] 3. Apply pressure to stop the bleeding:

[0233] A 65-year-old male patient required prolonged pressure on the bleeding site after an arterial blood draw. Because the nurse needed to care for other patients, she couldn't stay by his side for extended periods. The nurse selected the "compression" mode using a simulated hand on a touchscreen. The simulated hand extended two fingers and applied pressure to the blood draw site with the set force and duration to ensure effective hemostasis. The entire process was stable and continuous, and the patient did not experience uncontrollable bleeding.

[0234] 4. Health monitoring:

[0235] A 55-year-old female patient in the ICU required continuous monitoring of her blood oxygen saturation. Traditional finger pulse oximeters often detached due to the patient's unconscious movements, causing data interruptions. A prosthetic hand equipped with photoreceptors in the palm area, using a "monitoring" mode, can measure blood oxygen saturation and heart rate in real time while the patient is in contact with the prosthetic hand, displaying the data on an electronic screen. Built-in health monitoring algorithms analyze the data and generate health status reports, allowing doctors to promptly understand the patient's health condition. After a week of monitoring, data stability significantly improved, with no interruptions observed.

[0236] 5. Temperature control:

[0237] A 75-year-old patient with a high fever needed physical cooling. Traditional ice packs are prone to insecure fixation and require frequent replacement. The nurse selected the "physical cooling" mode using a prosthetic hand via a touchscreen. The prosthetic hand was set to 0°C, gloved, and placed on the patient's groin. Applying pressure secured the hand, providing continuous and stable cooling. After four hours of continuous cooling, the patient's temperature successfully returned to normal.

[0238] 6. Patient restraint:

[0239] An 80-year-old male patient with Alzheimer's disease frequently pulled involuntarily at medical equipment at night. The nurse selected the "restraint" mode for the prosthetic hand via a touchscreen. The prosthetic hand gently gripped the patient's forearm and applied appropriate pressure to prevent him from interfering with the treatment. The gentle touch of the prosthetic hand avoided the discomfort of traditional restraints, and the patient's agitated behavior was effectively controlled.

[0240] To verify the effectiveness of the present invention, a comparative experiment was conducted for one month, comparing the performance of the method of the present invention with that of the traditional method in several aspects:

[0241] Table 1 Comparison data between the method of the present invention and the conventional method

[0242] project The method of the present invention Traditional methods Emotional calming effect 85% of patients experienced improved mood. 60% of patients experienced improved mood. Functional training effect Muscle strength increased by 30% Muscle strength increased by 15% Success rate of applying pressure to stop bleeding 98％ 85％ Health monitoring data interruption rate 2％ 20％ Physical cooling effect Body temperature returned to normal in 4 hours Body temperature returns to normal within 6 hours constraint effect 90% of patients' behavior was under control 70% of patients' behavior was under control Nursing staff workload Reduce by 40% No significant changes

[0243] Referring to Table 1 above, the control method of the present invention performs excellently in terms of emotional soothing, functional exercise, pressure hemostasis, health monitoring, temperature regulation and patient restraint, effectively improving the quality of patient care and reducing the workload of nursing staff.

[0244] This invention utilizes a simulated hand "soothing" mode to perform actions such as patting, stroking, and gently grasping the patient, mimicking the comforting behavior of family members. This provides patients with warmth and a sense of security, effectively reducing separation anxiety and irritability when no family members are present. The built-in soothing algorithm ensures the precision and gentleness of the movements, enhancing patient comfort and satisfaction.

[0245] The simulated hand of this invention integrates multiple functions such as temperature regulation, posture control, and photoreceptor physiological monitoring, and can be flexibly adjusted and configured according to the specific needs of patients. Through the touchscreen, users can easily select different function modes to perform various clinical operations, greatly improving the efficiency and flexibility of the equipment.

[0246] The invention incorporates adaptive temperature control, posture control, and health monitoring algorithms, enabling the simulated hand to dynamically adjust according to the patient's real-time condition, ensuring the accuracy and reliability of all operations. For example, the temperature adjustment algorithm automatically adjusts the temperature of the simulated hand according to the patient's needs, providing a comfortable feel; the posture control algorithm precisely controls each joint of the simulated hand, ensuring natural and effective movements; and the health monitoring algorithm monitors the patient's blood oxygen saturation and heart rate in real time, providing accurate health data analysis.

[0247] This invention utilizes a simulated hand "pressing" mode to automatically apply pressure to stop bleeding after blood collection or puncture, saving medical staff time and effort and reducing the burden of manual operation. Furthermore, the "restraint" mode effectively controls mildly agitated patients, preventing their involuntary movements from interfering with treatment and ensuring the smooth progress of clinical work.

[0248] This invention utilizes a simulated hand with built-in photosensors and a health monitoring algorithm to monitor a patient's blood oxygen saturation and heart rate in real time, generating detailed health status reports. Through a touchscreen, users can view historical data trend charts and current health status assessments, promptly identifying potential health problems and receiving personalized health advice and care plans.

[0249] All operations of the simulated hand in this invention are controlled by built-in algorithms, ensuring the safety and stability of the operation process. For example, the temperature regulation algorithm and pressure control algorithm can monitor and adjust the operating parameters in real time to prevent discomfort or injury to the patient caused by excessive temperature or pressure. The simulated hand's fixing device and connecting arm are reasonably designed to be securely fixed to the bed rail, preventing accidental detachment or movement during use.

[0250] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A control method for a medical simulation robotic arm, characterized in that, Includes the following steps: A1: The temperature of the temperature regulator in the palm of the simulated robotic hand is adjusted using an adaptive temperature control algorithm; A2: Based on signals from various joints of the skeleton, and using a posture control algorithm, the simulated hand's joints are selected and controlled to move in the area requiring medical comfort, achieving the following comforting modes: When in tapping mode, a simulated hand gently taps the patient's back. When in stroking mode, the simulated hand gently strokes the patient's back or chest; In the light grip mode, the simulated hand gently grips the patient's hands; A3: In the light grip mode, based on the light sensing signal of the palm, the blood oxygen saturation information of the patient's hand is obtained, and analyzed based on the health monitoring algorithm to generate a patient health status report; In A1, the adaptive temperature control algorithm includes the following formula: ； in, The target temperature set for the temperature regulator of the robotic arm's palm area. The patient's current skin temperature. The temperature difference is set according to the patient's needs; The adaptive temperature control algorithm includes a feedback control-based adjustment mechanism: ； in, The preset temperature is the temperature required by the patient. , and These are the proportional, integral, and derivative control parameters, respectively.

2. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A1, the process of adjusting the temperature of the temperature regulator on the palm of the simulated robotic hand includes: The heating / cooling unit of the temperature regulator automatically adjusts the temperature to the target temperature based on the calculation results of the built-in adaptive temperature control algorithm. ; The temperature regulation process of the temperature controller is monitored and adjusted in real time by the built-in temperature regulation feedback control system, so that the temperature is stable within the set range. The temperature controller's data and adjustment status are displayed in real time via a touch screen, allowing users to select temperature adjustment modes and set parameters.

3. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A2, the process of selecting and controlling the movement postures of each joint of the simulated hand through a posture control algorithm includes: Obtain the current joint position data of the simulated hand ; Receive target joint position input by the user ; Calculate the optimized target joint position based on joint position data and real-time patient feedback data. : ； in, For patients' real-time response data, To optimize the coefficients; Calculate joint position error ; The posture control algorithm generates a control signal C based on the joint position error E. The control signal C is calculated using the following formula: ； in, , and These are the proportional, integral, and derivative control parameters, respectively. The control signal C is sent to the signal receiver, which controls the servo motors of each joint of the simulated hand to adjust the movement posture of each joint to the optimized target position. ; The built-in posture control algorithm monitors and adjusts joint positions in real time; The posture control data and status are displayed in real time via a touch screen, allowing users to select posture control modes and set parameters through the touch screen.

4. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A3, the specific process for obtaining information about a patient's blood oxygen saturation includes: The infrared light reflection signal from the photoreceptors in the palm area of ​​the simulated robotic hand is acquired, the received signal is converted into an electrical signal, and the corresponding light intensity data is recorded. and ; The signal processing unit of the simulated robotic arm is used to filter and denoise the acquired electrical signals to extract the effective signals. and ; The preprocessed effective signal is transmitted to the built-in health monitoring algorithm for blood oxygen saturation calculation and analysis. Calculate infrared light absorptivity and : ； ； in, and The light intensities emitted by the 660nm and 940nm infrared emitters, respectively; Based on infrared light absorption rate and Calculate blood oxygen saturation : ； in, and These are constants calibrated based on clinical data.

5. The control method for a medical simulation robotic arm according to claim 4, characterized in that, In A3, the process of obtaining information about a patient's blood oxygen saturation includes: The calculated blood oxygen saturation The data is compared with the patient's standard health data to determine if any abnormalities exist; The built-in health monitoring algorithm dynamically analyzes blood oxygen saturation data, generates a patient health status report, and updates the data in real time. ； in, , and These are constants calibrated based on clinical data. This represents the average blood oxygen saturation from historical data. Health monitoring data and analysis results are displayed in real time via a touch screen. Users can view detailed health data and reports, including historical data trend charts and current health status assessments.

6. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A2, when in tapping mode, the control process of the simulated robotic arm includes the following sub-steps: Obtain current pose data of the simulated hand ; Calculate the target posture of the tapping motion : ； in, The target posture for the light tapping action. For the simulation of the current hand posture data, Preset tap displacement vector; The attitude control algorithm generates control signals. : ； in, For control signals, These are the proportional, integral, and derivative control parameters, respectively. control signal The signal is sent to the signal receiver, where the simulated hand performs a tapping motion.

7. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A2, when in stroking mode, the control process of the simulated robotic arm includes the following sub-steps: Obtain current pose data of the simulated hand ; Calculate the target posture of the stroking action : ； The target posture for the stroking motion. For the simulation of the current hand posture data, This is the preset displacement vector for the gentle touch; The attitude control algorithm generates control signals. : ； in, For control signals, These are the proportional, integral, and derivative control parameters, respectively. control signal The signal is sent to the signal receiver, where the simulated hand performs a stroking motion.

8. The control method for a medical simulation robotic arm according to claim 1, characterized in that, In A2, when in the light grip mode, the control process of the simulated robotic arm includes the following sub-steps: Built-in soothing algorithm obtains the current posture data of the simulated hand. ; Calculate the target posture of the light grip action : ； in, The target posture for the light grip action. For the simulation of the current hand posture data, Preset light grip displacement vector; The attitude control algorithm generates control signals. : ； in, For control signals, These are the proportional, integral, and derivative control parameters, respectively. control signal The signal is sent to the signal receiver, and the simulated hand performs a light gripping motion.

9. The control method for a medical simulation robotic arm according to claim 1, characterized in that, A2 also includes a constraint mode: a constraint algorithm controls the simulated hand to gently grasp the patient's forearm and apply a preset pressure. The constraint algorithm includes the following sub-steps: Obtain the pressure data currently applied by the simulated hand. ; Set target pressure Adjust the pressure level according to the patient's needs and condition; Calculate the error of the applied pressure ; Generate pressure control signal : ； in, For pressure control signals, These are the proportional, integral, and derivative control parameters, respectively. Pressure control signal The signal is sent to the signal receiver, where the simulated hand performs a pressure adjustment action; The applied pressure is monitored and adjusted in real time through a constraint algorithm to ensure that the applied pressure is within a safe range and meets the needs of different patients. Pressure control data and status are displayed in real time via a touch screen, allowing users to select pressure control modes and set parameters.

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