Telekinetically actuated mannequin arm model

Abstract

The application discloses a kind of electric signal transmission's dummy arm model, belong to automobile safety test equipment and bionic robot technical field.The application aims at solving the technical problems of traditional crash test dummy structure rigidity, unable to simulate human body collision stress defense action, existing bionic driving technology slow response, unable to reproduce human nerve conduction mechanism, poor test data accuracy.The whole of the present application is integrated by three core systems of bionic electric signal sensing system, intention recognition system, stress execution system.The application can accurately restore the instinctive stress action such as lifting hand to protect head and supporting limbs in traffic accident, and has fast closed-loop response speed and high motion simulation degree, which effectively improves the data scientificity and reliability of automobile crash safety test and pedestrian protection test, and can be applied to bionic robot, rehabilitation exoskeleton and other technical fields.

Description

Technical Field

[0001] This invention belongs to the fields of automotive safety testing equipment and bionic robot technology. Specifically, it relates to a dummy arm model with electrical signal transmission, which is suitable for simulating human stress in automotive collision safety testing and pedestrian protection testing. It can also be extended to the fields of intelligent bionic equipment such as bionic robotic arms and rehabilitation exoskeletons. Background Technology

[0002] Automotive crash testing is a core testing method for evaluating vehicle safety performance and optimizing vehicle body protection structure. As the core testing vehicle, the biomimicry of crash test dummies directly determines the accuracy and reference value of crash injury assessment data.

[0003] Currently, most traditional crash test dummies on the market use rigid mechanical structures or simple articulated transmission structures, which can only simulate the static posture of the human body and cannot replicate the instinctive stress response of the human body in a real traffic accident. At the moment of a real traffic accident, the impact of the vehicle collision will cause occupants and pedestrians to instinctively make defensive actions such as raising their hands to protect their heads, supporting their bodies with their arms, and using their limbs to avoid the impact. These kinds of reactive actions directly change the human body's force posture, the parts of the body affected by the impact, and the degree of injury, and are key factors affecting the realism of crash tests.

[0004] In existing technologies, some studies have attempted to optimize the biomimetic performance of dummy models using materials such as pneumatic artificial muscles and shape memory alloys. However, these technologies have significant drawbacks: First, the response speed is slow, failing to match the millisecond-level stress response requirements at the moment of impact; second, the control logic is simple, unable to dynamically adjust the amplitude of movements and limb stiffness according to the impact intensity; third, there is a lack of simulation of the human neural conduction mechanism, only achieving passive mechanical movements, resulting in extremely low biomimetic realism; and fourth, poor environmental adaptability, with the core flexible materials easily failing due to dry environments. These problems prevent existing dummy models from realistically replicating human collision stress behavior, ultimately leading to significant discrepancies between collision test data and actual human injuries in traffic accidents, seriously affecting the scientific validity of vehicle safety performance assessments.

[0005] In summary, there is an urgent need for a bionic dummy arm model that can simulate the human nerve conduction mechanism, has a fast response speed, high motion simulation, can dynamically adapt to collision intensity, and has excellent environmental stability, in order to overcome many shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, a dummy arm model driven by electrical signals is provided. The model is composed of three integrated systems: a bionic electrical signal sensing system, an intention recognition system, and a stress-driven execution system. These three systems work together in a step-by-step manner to achieve closed-loop bionic control of perception, recognition, and execution.

[0007] To address the above deficiencies, this invention provides a dummy arm model driven by electrical signals, comprising a bionic electrical signal sensing system, an intent recognition system, and a stress-responsive execution system; The aforementioned biomimetic electrical signal sensing system is used to detect limb deformation signals and acceleration stress signals generated during a collision in real time. The intent recognition system is electrically connected to the biomimetic electrical signal sensing system and is used to amplify, filter, threshold and quantize the acquired raw sensing signals, and output control signals of corresponding parameters according to the collision intensity. The stress-responsive execution system is electrically connected to the intent recognition system and includes a main driver and a stiffness adjuster; it is used to receive control signals and synchronously complete arm joint flexion and extension movements and joint impedance adjustment to simulate human collision stress response.

[0008] Furthermore, the biomimetic electrical signal sensing system is equipped with a piezoelectric thin film sensor, which is attached to the surface of the dummy arm bone or inside the skin layer to simulate human periosteum sensing and skin tactile sensing, respectively; the output charge of the piezoelectric thin film sensor is proportional to the stress it is subjected to, and the charge signal is converted into a voltage signal through a charge amplifier.

[0009] This system is the core sensing element of the entire device. The core sensing element uses a PVDF (polyvinylidene fluoride) piezoelectric thin-film sensor, which features microsecond-level response speed, high flexibility, and high sensitivity, enabling precise capture of limb deformation and stress changes at the moment of impact. To match the physiological sensing mechanism of the human body, this sensor can be deployed in two ways: one is to attach it to the surface of the ulna, radius, and other bones of the dummy arm to simulate the stress sensing function of the periosteum in the human body; the other is to embed it inside the skin layer of the dummy arm to simulate the tactile sensing function of human skin.

[0010] The output charge of the PVDF sensor is directly proportional to the stress it receives, calculated using the formula: Q = D³³·σ·A; where D³³ is the piezoelectric constant of the PVDF (pC / N); σ is the stress on the sensor (Pa); and A is the effective force-bearing area of ​​the sensor (m²). The weak charge signal output by the sensor is converted into a recognizable voltage signal by a charge amplifier, using the formula: Vout = Q / Cf = D³³·σ·A / Cf; where Cf is the feedback capacitance of the charge amplifier. This conversion enables the effective acquisition of the weak sensing signal. Simultaneously, leveraging the inherent microsecond-level response advantage of the PVDF sensor, a low-pass filtering algorithm is used to slow down the original signal, ensuring that the overall sensing response speed accurately matches the physiological sensing speed of the human body, thus avoiding motion distortion caused by an excessively fast response.

[0011] Furthermore, the intent recognition system includes a charge amplifier, a second-order active bandpass filter, a high-speed comparator, an MCU controller, and an ADC sampling module. The second-order active bandpass filter has a passband of 0.1Hz to 100Hz and is used to filter out static contact interference signals while retaining valid collision dynamic signals. The high-speed comparator has a built-in fixed trigger threshold, which triggers subsequent action responses only when the processed voltage signal is greater than or equal to the trigger threshold. The MCU controller uses an STM32 series microcontroller, which quantifies the collision intensity through the ADC module and outputs a PWM control signal with a corresponding duty cycle. The duty cycle of the PWM signal is linearly positively correlated with the collision intensity, and the duty cycle adjustment range is 20% to 100%. The range of limb movement amplitude under different forces is matched by changing the duty cycle.

[0012] Furthermore, the intent recognition system is equipped with a variable delay module. The variable delay module matches the human nerve conduction speed of 40-70m / s according to the signal transmission distance between the sensor and the actuator, simulates the human physiological nerve delay, and accurately replicates the timing characteristics of the human stress response.

[0013] This system serves as the core of the overall machine control, responsible for sensor signal processing, collision intent determination, and control command output. In the signal processing stage, the voltage signal output by the sensor is first filtered by the second-order active bandpass filter. The passband of the second-order active bandpass filter is set to 0.1Hz to 100Hz, which can effectively filter out interference signals such as daily static contact and low-frequency vibration, and retain only the dynamic effective signal at the moment of collision, thereby improving the signal recognition accuracy. The transfer function of the second-order active bandpass filter is: H(s)=ω0·s / s²+ω0·s / Q+ω0²; where ω0=2π√(fL+fH), fL=0.1Hz, fH=100Hz, and the quality factor Q is set to 0.7 to 1 to ensure the stability and accuracy of signal filtering.

[0014] During the threshold triggering phase, a fixed voltage trigger threshold Vth is set via the high-speed comparator, corresponding to the physiological perception thresholds of human pain and avoidance. The determination logic is as follows: when the processed real-time voltage signal V(t) ≥ Vth, the high-speed comparator outputs a valid trigger signal to initiate subsequent action responses; when V(t) < Vth, no action is output to avoid false triggering. Preferably, the 2.5V threshold corresponds to a standard 10N impact force, suitable for conventional automotive collision testing scenarios.

[0015] During the command output phase, the MCU controller performs 1024-level quantization processing on the valid signal through the ADC module, and outputs PWM control signals with different duty cycles based on the quantized values ​​through linear mapping, thereby achieving precise matching between collision intensity and movement amplitude. The PWM duty cycle calculation formula is: D = Dmin + x / 1023 · (Dmax - Dmin); where x is the ADC quantization value, ranging from 0 to 1023, and Dmin and Dmax are the minimum and maximum duty cycles, respectively, with an optimal adjustment range of 20% to 100%. The larger the duty cycle, the stronger the actuator driving force and the greater the arm movement amplitude.

[0016] In the physiological delay simulation stage, in order to accurately replicate the timing characteristics of human nerve conduction, a variable delay module is set up in the system. Based on the signal conduction distance L between the sensor and the actuator, combined with the human nerve conduction velocity vnerve (40~70m / s, preferably 50m / s), the physiological delay time Δt is calculated and matched. The calculation formula is: Δt=L / vnerve. Through software delay compensation, the circuit hardware delay deviation is eliminated, and the timing of the real human stress response is completely simulated.

[0017] The overall circuit hardware delay of this system can be controlled within 1 to 5 ms, and with precise software delay, the biomimetic realism of the action response is guaranteed.

[0018] Furthermore, the main actuator is a micro servo motor or pneumatic artificial muscle, used to drive the arm joint to complete large-amplitude flexion and extension movements; the stiffness adjuster is an IPMC ion polymer metal composite material, and the IPMC material is installed in parallel on the arm joint damper to adjust the joint elastic modulus and impedance in real time, simulating the tense contraction state of human muscles.

[0019] Furthermore, the surface of the IPMC material is provided with a composite moisturizing encapsulation layer, which includes an ionogel film and a parylene water-retaining film. The ionogel is prepared by dissolving PVDF-HFP and EMIM-TFSI in acetone at a mass ratio of 1:4. The composite moisturizing encapsulation layer is used to ensure the working stability of the IPMC material in a dry environment.

[0020] This system is the core of the entire machine's execution. The main driver uses a micro servo motor or a pneumatic artificial muscle. The micro servo motor is suitable for high-precision, small-amplitude, rapid flexion and extension scenarios, operates at 6V, and can stably output torque to drive arm joint movement. The pneumatic artificial muscle is suitable for large-stroke, flexible swinging motion scenarios, with a contraction ratio of up to 25%, meeting the motion requirements of pedestrian protection testing. The joint flexion and extension angle of the servo motor is linearly related to the PWM duty cycle, calculated by the formula: θ=θmin+D·(θmax-θmin), which allows for precise control of the arm flexion and extension angle. The stiffness adjuster, installed in parallel on the arm joint damper, is the core flexible adjustment component. It can change its elastic modulus in real time according to the control voltage converted by PWM, simulating the tense contraction characteristics of human muscles. The IPMC elastic modulus adjustment formula is: E(Vc)=E0·(1+k·Vc / Vmax); where E0 is the initial elastic modulus of the IPMC, k is the stiffness adjustment coefficient (value 2-4), Vc is the real-time control voltage, and Vmax is the maximum control voltage. As the control voltage increases, the IPMC elastic modulus increases, and the overall joint impedance Z increases synchronously. The joint impedance formula is: Z=Zservo+ZIPMC(Vc), accurately replicating the mechanical characteristics of muscle tension.

[0021] To address the industry challenge that IPMC materials perform optimally only in humid environments and are prone to failure in dry environments, this invention employs a composite moisturizing encapsulation process: first, the IPMC sheet is immersed in a 0.1M LiCl solution to complete ion exchange; after drying, a 50μm thick ionogel film (prepared by mixing PVDF-HFP and EEMIM-TFSI in a 1:4 mass ratio) is coated; finally, a 2μm thick parylene water-retaining film is wrapped around it. This process ensures that the IPMC material can operate stably for more than 6 months in a dry environment with a relative humidity of 30%, significantly improving the environmental adaptability and service life of equipment.

[0022] Furthermore, the biomimetic electrical signal sensing system, intention recognition system, and responsive execution system constitute a complete closed-loop control link, and the overall signal response and action execution closed-loop response time is controlled within the range of 1 to 10 ms.

[0023] After the device is powered on and initialized, the bionic electrical signal sensing system collects the force deformation signal of the arm in real time and converts it into a raw charge signal. The charge signal is amplified, filtered and denoised by the intention recognition system to obtain an effective voltage signal. The high-speed comparator completes the threshold determination. After confirming the collision trigger, the MCU quantifies the collision intensity through the ADC and outputs a PWM control signal with the corresponding duty cycle. At the same time, the variable delay module matches the human neurophysiological delay and accurately controls the action triggering sequence. Finally, the stress-reactive execution system is controlled to work synchronously. The main driver drives the arm to complete the flexion and extension defense action, and the IPMC regulator synchronously adjusts the joint stiffness to simulate the muscle tension state. The entire closed-loop action can be completed within 10ms, which fully meets the dynamic response requirements of automobile collision testing. Beneficial effects

[0024] Compared with existing technologies, the dummy arm model with electrical signal transmission provided by this invention has extremely high biomimetic simulation. It simulates the complete physiological mechanisms of human nerve conduction, signal delay, and muscle stress contraction through electrical signal transmission, breaking the limitations of purely mechanical movements in traditional dummies. It can accurately reproduce instinctive reactive actions such as raising the hand to protect the head, supporting the limbs, and swinging to avoid collisions at the moment of impact. The measured action data matches the real human reaction data with 92%, significantly improving the biofidelity of the test. The response is precise and controllable. Through threshold judgment, linear adjustment of PWM duty cycle, and precise matching of physiological delay, it can adaptively adjust the amplitude of movement, response timing, and limb stiffness according to different collision forces and test scenarios, avoiding the defects of fixed response and poor adaptability of traditional equipment. It is suitable for various car collision and pedestrian protection test scenarios. The drive performance is excellent. It adopts a hybrid drive architecture of servo motors or pneumatic artificial muscles and IPMC, balancing large-amplitude limb movements with high-precision stiffness adjustment. It perfectly replicates the dual characteristics of dynamic expansion and contraction of human muscles under stress, and the mechanical simulation effect is far superior to that of a single drive structure. The test data is reliable, realistically reproducing human collision stress behavior and eliminating the deviation between traditional testing equipment and real traffic accidents, effectively improving the scientific rigor and reference value of vehicle collision injury assessment and vehicle safety performance testing data. It exhibits strong environmental adaptability, solving the problem of flexible smart materials failing in dry environments through a unique IPMC composite moisture-retaining encapsulation process. The equipment can operate stably for extended periods in conventional indoor dry environments, boasting a long service life and low maintenance costs. Its applications are wide-ranging, not only suitable for automotive safety collision testing and pedestrian protection testing, but also applicable to multiple technological fields such as bionic robotic arms, rehabilitation exoskeletons, and intelligent bionic prostheses, possessing extremely high promotional value. Attached Figure Description

[0025] Figure 1 This is a diagram of the overall system structure of the present invention.

[0026] Figure 2 This is a flowchart of the overall system of the present invention.

[0027] Among them: 1. Bionic electrical signal sensing system, 2. Intention recognition system, 3. Responsive execution system, 4. Piezoelectric thin film sensor, 5. Charge amplifier, 6. Second-order active bandpass filter, 7. High-speed comparator, 8. MCU controller, 9. ADC sampling module, 10. Main driver, 11. Stiffness regulator, and the arrows indicate the direction of system signal transmission. Detailed Implementation

[0028] The invention will now be further described with reference to the accompanying drawings.

[0029] In one embodiment, the present invention is used for conventional vehicle collision and trolley collision safety testing, with the following specific configuration. Bionic electrical signal sensing system: A 20mm×10mm×0.1mm PVDF piezoelectric film sensor is attached to the surface of the ulna and radius bones of the dummy arm to simulate periosteal stress sensing; a sensor of the same specification is embedded in the skin layer of the palm to simulate tactile sensing and collect collision force signals from all directions.

[0030] Intent recognition system: The charge amplifier uses the TL081 operational amplifier chip, and the second-order active bandpass filter has an optimized passband of 0.5Hz to 80Hz to match the frequency band of automotive collision signals; the high-speed comparator trigger threshold is set to 2.5V, corresponding to a standard impact force of 10N; the MCU uses the STM32F103 microcontroller, with an ADC sampling rate of 1kHz and a PWM output frequency of 1kHz; the shoulder joint signal transmission distance is set to 0.1m to match a nerve conduction velocity of 50m / s, and the software delay is 2ms to accurately simulate the nerve delay of the human upper limb.

[0031] The reactive actuation system features 6V miniature servo motors (3kg·cm torque) installed at both the shoulder and elbow joints as main drivers. A 15mm×5mm×0.2mm IPMC stiffness adjuster is connected in parallel at the joint rotation axis, encapsulated with Nafion iontophoresis gel and parylene composite moisture-retaining material. Upon collision triggering, the MCU outputs an 80% duty cycle PWM signal, causing the IPMC's elastic modulus to increase threefold within 50ms. This synchronously increases joint stiffness, and the servo motor drives the arm to flex and extend forward 30°, precisely simulating the defensive action of raising the hand to protect the head during a collision.

[0032] Test results: In the 50km / h trolley collision test, the model in this embodiment can complete a full set of defensive actions 80ms before the collision. The action posture, amplitude, and timing are 92% consistent with the measured data of human volunteers, and the test stability is excellent.

[0033] In another embodiment, compared to the first embodiment, the present invention is used for vehicle-pedestrian collision protection performance testing. The sensing system places PVDF sensors only in the skin interlayer on the outside of the arm to adapt to the force characteristics of a pedestrian's unilateral collision; the trigger threshold of the intent recognition system is reduced to 1.5V to correspond to slight touch impact force, improving the sensitivity of weak collision signals; the execution system uses a pneumatic artificial muscle with a contraction ratio of 25% to replace the micro servo motor, adapting to the flexible movement characteristics of a pedestrian's arm swinging naturally after being hit, while the IPMC stiffness adjustment structure and packaging process remain unchanged.

[0034] Test results: The model in this embodiment has a response time of approximately 12ms, which can accurately simulate the instinctive arm swinging and limb avoidance reaction of a pedestrian when encountering a vehicle collision. It is perfectly adapted to pedestrian protection test conditions and the test data has high accuracy.

[0035] The above are merely preferred embodiments of the present invention. Any equivalent substitutions and improvements made by those skilled in the art without departing from the concept of the present invention shall fall within the protection scope of the present invention.

Claims

1. A dummy arm model powered by electrical signals, characterized in that, This includes biomimetic electrical signal sensing systems, intention recognition systems, and reactive execution systems; among them, The aforementioned biomimetic electrical signal sensing system is used to detect limb deformation signals and acceleration stress signals generated during a collision in real time. The intent recognition system is electrically connected to the bionic electrical signal sensing system and is used to amplify, filter, threshold determine, and quantize the acquired raw sensing signals, and output control signals of corresponding parameters according to the collision intensity. The stress-responsive execution system is electrically connected to the intent recognition system and includes a main driver and a stiffness adjuster for receiving control signals and synchronously completing arm joint flexion and extension movements and joint impedance adjustment to simulate human collision stress response.

2. The dummy arm model with electrical signal transmission according to claim 1, characterized in that, The biomimetic electrical signal sensing system includes a piezoelectric thin film sensor, which is attached to the surface of the dummy arm bone or inside the skin layer to simulate human periosteum sensing and skin tactile sensing, respectively. The output charge of the piezoelectric thin film sensor is proportional to the stress it is subjected to, and the charge signal is converted into a voltage signal by a charge amplifier.

3. The dummy arm model with electrical signal transmission according to claim 1, characterized in that, The intent recognition system includes a charge amplifier, a second-order active bandpass filter, a high-speed comparator, an MCU controller, and an ADC sampling module. The second-order active bandpass filter has a passband of 0.1Hz to 100Hz and is used to filter out static contact interference signals while retaining dynamic valid collision signals. The high-speed comparator has a fixed trigger threshold, and subsequent action responses are triggered only when the processed voltage signal is greater than or equal to the trigger threshold.

4. The dummy arm model with electrical signal transmission according to claim 3, characterized in that, The MCU controller uses an STM32 series microcontroller, which quantifies the collision intensity through an ADC module and outputs a PWM control signal with a corresponding duty cycle. The duty cycle of the PWM signal is linearly positively correlated with the collision intensity, and the duty cycle adjustment range is 20% to 100%. The range of limb movements under different forces is matched by changing the duty cycle.

5. The dummy arm model with electrical signal transmission according to claim 3, characterized in that, The intent recognition system also includes a variable delay module. This module matches the human nerve conduction speed of 40-70 m / s according to the signal transmission distance between the sensor and the actuator, simulating the human physiological nerve delay and accurately replicating the timing characteristics of the human stress response.

6. The dummy arm model with electrical signal transmission according to claim 1, characterized in that, The main actuator is a micro servo motor or pneumatic artificial muscle, used to drive the arm joint to complete large-amplitude flexion and extension movements; the stiffness adjuster is an IPMC ion polymer metal composite material, and the IPMC material is installed in parallel on the arm joint damper to adjust the joint elastic modulus and impedance in real time, simulating the tension and contraction state of human muscles.

7. The dummy arm model with electrical signal transmission according to claim 6, characterized in that, The IPMC material has a composite moisturizing encapsulation layer on its surface. The composite moisturizing encapsulation layer includes an ion gel film and a parylene water-retaining film. The ion gel is prepared by dissolving PVDF-HFP and EMIM-TFSI in acetone at a mass ratio of 1:

4. The composite moisturizing encapsulation layer is used to ensure the working stability of the IPMC material in a dry environment.

8. The dummy arm model with electrical signal transmission according to claim 1, characterized in that, The biomimetic electrical signal sensing system, intention recognition system, and responsive execution system constitute a complete closed-loop control link, and the overall signal response and action execution closed-loop response time is controlled within the range of 1 to 10 ms.

9. A car crash test dummy, characterized in that, A dummy arm model comprising the electrically driven arm as described in any one of claims 1-8.

10. A bionic robotic arm, characterized in that, A dummy arm model comprising the electrically driven arm as described in any one of claims 1-8.

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