Apparatus and method for interfering wearable photoplethysmography device

Abstract

The invention relates to the technical field of mobile and wireless security, and discloses a device and method for interfering wearable photoplethysmography equipment, and the device comprises an infrared light source which is configured to emit a modulated infrared light beam; the control unit is configured to generate a modulation waveform and control the infrared light source to emit an infrared light beam according to the modulation waveform so as to change the heart rate and the oxyhemoglobin saturation reading of the wearable photoplethysmography equipment; wherein the control unit is configured to generate a modulation waveform by using an adaptive genetic algorithm based on the upper layer structure and the lower layer structure, and the algorithm improves the manipulation effect on the reading by optimizing indexes related to the manipulation effect. Therefore, in the medical health data safety test process, the controllable interference data can be quickly and accurately generated, so that the accuracy and reliability of the medical health data safety test process are ensured. In addition, confusion and cheating of data attack behaviors can be realized, and the security of the medical health data is improved.

Description

Technical Field

[0001] The present application relates to the field of mobile and wireless security technologies, and in particular to an apparatus and method for interfering with a wearable photoplethysmography device. Background Art

[0002] Driven by convenience, cost-effectiveness, and user-friendliness, the rapid development of the Internet of Things in healthcare has exposed numerous vulnerabilities resulting from a lack of effective regulation. Critical among these are the eavesdropping of vital signs and the exploitation of communication vulnerabilities that undermine data privacy. Furthermore, insecure mobile medical applications further facilitate unauthorized access and manipulation, misleading doctors, caregivers, and family members, and endangering patients.

[0003] In the existing medical and health data security testing process, various interference methods mainly focus on exploiting vulnerabilities in data transmission protocols and databases to change the vital sign readings of wearable devices. However, the existing security testing process ignores the risk of unreliable PPG readings, which in turn makes the security testing of medical and health data unreliable.

[0004] Therefore, in the existing medical and health data security testing process, there is a problem of how to generate controllable interference data based on the PPG detection principle to achieve accurate and reliable medical and health data security testing.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application. Summary of the Invention

[0006] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0007] The apparatus and method for interfering with a wearable photoplethysmography device provided by the embodiments of the present disclosure can solve the problems of uncontrollable interference data and inaccurate security testing of medical and health data.

[0008] An embodiment of the present disclosure provides an apparatus for interfering with a wearable photoplethysmography device, the apparatus comprising:

[0009] an infrared light source configured to emit a modulated infrared light beam;

[0010] a control unit configured to generate a modulation waveform and control the infrared light source to emit an infrared light beam according to the modulation waveform to change the heart rate and blood oxygen saturation readings of the wearable photoplethysmography device;

[0011] The control unit is configured to generate a modulation waveform using an adaptive genetic algorithm based on an upper structure and a lower structure, and the algorithm improves the manipulation effect on the reading by optimizing indicators related to the manipulation effect.

[0012] In some embodiments, the adaptive genetic algorithm adopts a two-layer structure, including an upper layer structure and a lower layer structure:

[0013] The upper structure is configured to screen short waveform segments with good performance and determine the initial population of the lower structure through multiple generations of iteration. Each generation performs the following steps:

[0014] A Gaussian process surrogate model is constructed using a Bayesian optimization algorithm. The Gaussian process surrogate model is used to predict the expected improvement value of the candidate waveform segment based on the evaluated waveform segment and its interference success rate.

[0015] According to the expected improvement value, waveform segments with potential to meet the preset conditions are selected from the candidate waveform segments as experimental samples;

[0016] Evaluate the interference success rate of experimental samples through experiments, and use the evaluation results to update the Gaussian process agent model;

[0017] Based on the interference success rate evaluated in the experiment, waveform segments whose interference success rate meets the preset success threshold are selected as the initial population of the lower structure;

[0018] The lower structure is configured to generate its initial population by combining short waveform segments determined by the upper structure, and optimize the combination of the waveform segments through a genetic algorithm to improve the waveform's ability to resist interference suppression. The genetic algorithm includes:

[0019] Generate offspring sequences by performing a single-point crossover operation at the substring boundary and a mutation operation by replacing the substring with other substrings whose Hamming distance is less than a preset threshold;

[0020] The sequence for the next generation of iteration is selected based on the anti-interference index, and the crossover, mutation and selection steps are repeated until the anti-interference index becomes stable.

[0021] In some embodiments, the anti-interference index is calculated as follows:

[0022]

[0023] in:

[0024] AIM stands for Anti-Interference Index;

[0025] PSL is the autocorrelation sidelobe, defined as PSL = max(|R(k)| / R(0), k≠0), where k = 1, 2, ..., N-1, and R(k) is the autocorrelation function;

[0026] SE is the spectral entropy, defined as p(f) is the normalized power spectrum;

[0027] w PSL and w SE are the weights of the autocorrelation sidelobes and spectral entropy, respectively, and w PSL +w SE =1;

[0028] PSL min and PSL max are the minimum and maximum values ​​of the autocorrelation sidelobes in the current population, respectively;

[0029] SE min and SE max are the minimum and maximum spectral entropy in the current population, respectively.

[0030] In some embodiments, the infrared light source emits an infrared light beam having a wavelength in the range of 500 nanometers to 1000 nanometers.

[0031] In some embodiments, the jamming success rate is determined by:

[0032] For n participants wearing wearable photoplethysmography devices, m interference experiments were conducted using waveform segments;

[0033] Determine the number of successful interferences q, where successful interference refers to the waveform segment causing a predetermined change in the participant's target physiological parameter;

[0034] The interference success rate is calculated as

[0035] In some embodiments, the target physiological parameter is selected from at least one of heart rate and blood oxygen saturation, and the preset change is an increase or decrease in the value of the target physiological parameter.

[0036] In some embodiments, the adaptive genetic algorithm generates a modulation waveform in the form of a binary string, and the control unit is configured to control the infrared light source to emit a modulated infrared light beam based on the binary string using at least one modulation method selected from on-off keying modulation and pulse width modulation.

[0037] In some embodiments, the Gaussian process proxy model uses a Hamming distance kernel function, and the Gaussian process proxy model includes a noise regularization term.

[0038] In some embodiments, the genetic algorithm of the lower structure uses the short waveform segments screened out by the upper structure as coding units for combinatorial optimization, and both crossover operations and mutation operations are performed on the basis of the short waveform segments.

[0039] The present disclosure also provides a method for interfering with readings of a wearable photoplethysmography device, the method comprising:

[0040] A modulation waveform is generated using an adaptive genetic algorithm based on the upper and lower structures, wherein the algorithm improves the manipulation effect on the reading by optimizing an indicator related to the manipulation effect, wherein the modulation waveform generated by the adaptive genetic algorithm based on the upper and lower structures is obtained according to the above-mentioned device;

[0041] Controlling the infrared light source to emit a modulated infrared beam according to a modulation waveform;

[0042] Directing a modulated infrared beam toward a wearable photoplethysmography device can alter the device's heart rate and / or blood oxygen saturation readings.

[0043] The apparatus and method for interfering with a wearable photoplethysmography device provided by the embodiments of the present disclosure can achieve the following technical effects:

[0044] The apparatus and method for interfering with a wearable photoplethysmography device, as proposed in the embodiments of the present disclosure, achieves control over interference with the heart rate and blood oxygen saturation readings of the interfering object. Furthermore, the selected optimization framework is simple, effective, and has good convergence, enabling rapid convergence to a waveform with a high success rate. Therefore, during existing medical and health data security testing processes, controllable interference data can be quickly and accurately generated to ensure the accuracy and reliability of the medical and health data security testing process. Furthermore, data attacks can be obfuscated and deceived, thereby improving the security of medical and health data.

[0045] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0047] Figure 1 1 is a schematic diagram of a device structure for interfering with a wearable photoplethysmography device provided by an embodiment of the present disclosure;

[0048] Figure 2 This is a schematic diagram of the overall architecture of a schematic model of interference of an infrared LED on a wearable PPG device provided by an embodiment of the present disclosure;

[0049] Figure 3Schematic diagram of a false heart rate measurement value and a reference heart rate value of a wearable PPG device in an interference-enhanced heart rate reading provided by an embodiment of the present disclosure;

[0050] Figure 4 Schematic diagram of the error between a false heart rate measurement value and a reference heart rate value of a wearable PPG device in reducing interference in heart rate readings provided by an embodiment of the present disclosure;

[0051] Figure 5 Schematic diagram of the error between a false blood oxygen saturation reading and a reference blood oxygen saturation reading in an embodiment of the present disclosure when interference with the blood oxygen saturation reading is increased;

[0052] Figure 6 Schematic diagram of the error between a false blood oxygen saturation reading and a reference blood oxygen saturation reading in reducing interference in blood oxygen saturation readings provided by an embodiment of the present disclosure;

[0053] Figure 7 1 is a flow chart of a method for interfering with a wearable photoplethysmography device provided by an embodiment of the present disclosure;

[0054] Figure 8 1 is a schematic diagram of the device structure of an interference wearable photoplethysmography device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0056] The terms "first," "second," and the like in the embodiments of the present disclosure are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to facilitate the description of the embodiments of the present disclosure herein. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0057] Unless otherwise stated, the term "plurality" means two or more.

[0058] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0059] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0060] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0061] In order to solve the above problems, the present disclosure provides an apparatus and method for interfering with a wearable photoplethysmography device.

[0062] Focusing on existing healthcare data security testing scenarios, the following describes, with reference to the accompanying figures, the apparatus and method for interfering with a wearable photoplethysmography device, as provided in the present disclosure. Furthermore, the apparatus and method for interfering with a wearable photoplethysmography device can be used in data security countermeasures to confuse and deceive attackers, thereby improving the security of healthcare data.

[0063] Reference Figure 1 The device for interfering with a wearable photoplethysmography device proposed in this application includes an infrared light source 101 and a control unit 102. Figure 2 , the device uses an infrared light source 101 ( Figure 2 The invention relates to a wearable photoplethysmography (PPG) device (including a photoelectric detector (PD) and its own LED) that emits a modulated infrared beam x(t) to interfere with the target wearable photoplethysmography (PPG) device (including a photoelectric detector (PD) and its own LED).

[0064] In actual interference scenarios, the effective signal strength F(t) of the modulated infrared beam emitted by the infrared light source 101 will be affected by a variety of complex physical propagation factors when it reaches the target PPG device and affects it. These factors include but are not limited to the distance d between the light source and the target, the light source's angular characteristics Φ, and the half-power angle Φ. 1 / 2 , the angle ψ at which the light beam impinges on human skin, and the reflection, absorption, and scattering properties of human skin tissue for infrared light of specific wavelengths. Furthermore, the target PPG device's own photodetector PD is subject to interference from environmental noise when receiving its physiological signal I0. The combined effect of these physical factors makes accurate modeling from x(t) to F(t) highly complex and variable in practical applications.

[0065] Precisely because of these complex physical propagation characteristics and environmental factors, this application proposes a method for directly optimizing and screening interfering modulation waveforms using a two-layer adaptive genetic algorithm. This algorithm's goal is not to explicitly model and inversely calculate every physical propagation parameter. Instead, it uses a data-driven approach to iteratively search for and generate modulation waveforms that most effectively alter the target PPG device readings under realistic experimental conditions that incorporate all these physical propagation effects. Therefore, through its experimental feedback mechanism, this optimization algorithm indirectly adapts to and overcomes these complex propagation influences to achieve the best interference effect.

[0066] Combine Figure 1 and Figure 2 Devices that interfere with wearable photoplethysmography devices may include:

[0067] an infrared light source 101 configured to emit a modulated infrared light beam;

[0068] a control unit 102 configured to generate the modulation waveform and control the infrared light source to emit an infrared light beam according to the modulation waveform to change the heart rate and blood oxygen saturation readings of the wearable photoplethysmography device;

[0069] In typical wearable device photoplethysmography (PPG) signal acquisition, its built-in photodetector usually does not strictly distinguish the wavelength of the incident light, and its effective sensing spectrum range generally covers 500 nanometers to 1000 nanometers. Although the device will subsequently separate the received mixed light signal into different signal channels according to the preset wavelength through a time-division multiplexing synchronization mechanism with its own light-emitting diode (LED), in order to ensure that the external interference signal emitted by the infrared light source 101 can be effectively received by the photodetector of the target PPG device and produce an interference effect, the wavelength of the infrared light beam emitted by the infrared light source 101 should be set within the range of 500 nanometers to 1000 nanometers. Therefore, in this embodiment, the control unit 102 controls the infrared light source 101 (e.g., an infrared LED) to operate within this wavelength range.

[0070] After determining the emission wavelength range of the infrared light source 101, the control unit 102 needs to select a suitable modulation method to convert the digital sequence generated by the subsequent optimization algorithm into a physical signal that controls the output of the infrared light source 101. In the research and experiments of this application, two basic digital modulation technologies were focused on to drive the infrared light source 101: On-Off Keying (OOK) modulation and Pulse Width Modulation (PWM). The reason for choosing these two modulation methods is that their implementation circuits are relatively simple, require fewer components, and the physical size can be controlled at the square millimeter level, which makes the jamming device containing the infrared light source 101 and the corresponding modulation circuit have good concealment when actually deployed. Subsequent experimental results (such as the attached Figure 3 To the attached Figure 6 The figures and related descriptions will demonstrate that, by using two different modulation schemes, OOK or PWM, to drive the infrared light source 101, the control unit 102 can interfere with the heart rate and blood oxygen saturation readings of the target PPG device to varying degrees and characteristics. Based on the binary sequence generated by the optimization algorithm and combined with a preset or dynamically selected OOK or PWM modulation scheme, the control unit 102 precisely controls the output of the infrared light source 101, causing it to emit a specifically modulated infrared beam.

[0071] In a preferred embodiment of the present application, the control unit 102 adopts a modulation waveform generation method based on a two-layer adaptive genetic algorithm. The algorithm includes an upper layer structure and a lower layer structure, and is specifically implemented as follows:

[0072] The upper layer structure is designed to screen out segments with a high interference success rate from short binary strings (a 16-bit 0 / 1 string in this embodiment) by combining Bayesian optimization with genetic thinking.

[0073] The optimization and screening process begins with an initialization step. In this step, the control unit randomly generates 32 16-bit binary strings as the initial candidate set. In order to evaluate the actual interference effect of these initial strings, interference experiments will be conducted on them one by one, and their respective interference success rates SR(x) will be recorded. The interference success rate SR(x) is defined here as the number of times q that the target PPG device reading successfully causes a preset change, divided by the total number of interference experiments (i.e., the product of the number of interferences m for each participant and the number of participants n: SR(x) = q / (m*n)). These initially generated binary strings and their corresponding experimentally measured SR(x) values ​​will together constitute the initial training data set for the subsequent construction of the Gaussian process surrogate model.

[0074] In order to be able to effectively predict the interference effect of untested binary strings based on existing experimental data and guide the subsequent optimization search direction, the control unit constructs and maintains a Gaussian process proxy model. The core of the Gaussian process model lies in the kernel function it selects. In this embodiment, the Hamming distance kernel function is used to measure the similarity between binary strings. Its specific form is k(x,x') = exp(-αHD(x,x')), where HD(x,x') represents the Hamming distance between binary strings x and x'. In the experiment of this embodiment, the attenuation factor α is set to 0.05. Based on this Gaussian process model, for any new binary string x* that has not yet been experimentally evaluated, the control unit can predict the mean μ(x*) and uncertainty (in terms of variance σ) of the possible interference success rate. 2 (denoted by x*). The calculation of these predictions follows the standard Gaussian process regression formula:

[0075] μ(x * )=K(x * ,X)[K(X,X)+σ 2 n I] -1 y,

[0076] σ 2 (x * )=K(x * ,x * )-K(x * ,X)[K(X,X)+σ 2 n I] -1 K(X,x * ),

[0077] In the formula, X represents the set of binary strings that have been experimentally evaluated, y is the SR observation value corresponding to these strings, K is the corresponding kernel matrix calculated by the selected kernel function, and I is the identity matrix. In order to more accurately model the noise that may exist in actual observations, the observation noise variance σ is introduced into the model 2 n In this embodiment, the standard deviation of the observation noise is set to 0.05.

[0078] After the Gaussian process model is constructed and initialized, the optimization and screening process enters an iterative optimization loop. In each iteration, the control unit first generates a new set of candidate binary strings by selecting and performing genetic operations on the existing binary strings in the current population (the initial population is the aforementioned 32 strings). In the experiment of this embodiment, the size of the candidate population generated in each generation is approximately 50. The specific structure is: by extracting parents from the current population and selecting them based on the GP model, performing a crossover operation to generate 25 offspring, performing a mutation operation to generate 15 offspring, and additionally randomly generating 10 new individuals. The selection operation involves extracting three strings from the current population, selecting the individual with the best predicted SR as one of the parents using the Gaussian process model, and repeating this process to obtain several parents. The genetic operations include single-point random crossover, two-point random crossover, and bit-by-bit mutation. In the experiment of this embodiment, the mutation probability of each bit is set to 0.2. To avoid unnecessary repeated experiments, the system ensures that the newly generated candidate strings are not exactly the same as all the evaluated strings, and regenerates them if duplication occurs.

[0079] Next, to decide which newly generated candidate strings are most worthy of expensive real-world jamming experiments, the control unit uses Expected Improvement (EI) as the acquisition function. For each candidate string, the current Gaussian process model is used to predict its μ(x*) and σ 2 (x*), and calculate its EI value accordingly. The calculation formula of EI value is:

[0080]

[0081] In this formula, f max represents the highest jamming success rate SR value observed in all binary strings that have been experimentally evaluated so far, while Φ(·) and They represent the cumulative distribution function (CDF) and probability density function (PDF) of the standard normal distribution, respectively. The control unit then selects the 10 candidate strings with the highest EI values ​​and conducts actual interference experiments on them to obtain their true SR(x) values. It should be noted that the number of candidate strings with the highest EI values ​​can be adjusted according to actual conditions and is not limited here.

[0082] After obtaining a new batch of experimental data, the control unit adds these newly evaluated binary strings and their corresponding SR values ​​to the training dataset of the Gaussian process model. The updated complete dataset is then used to update or retrain the Gaussian process model to improve the accuracy of the model's subsequent predictions. Furthermore, to maintain and evolve the population of high-performing binary strings, all experimentally evaluated strings are sorted according to their true SR values, and the 32 highest-performing strings are selected to form the next iteration population, thus maintaining a population size of 32. It should be noted that the number of these selected highest-performing strings can be adjusted based on actual conditions and is not limited here.

[0083] This iterative optimization process will continue until the preset termination condition is met. In the experiment of this embodiment, the termination condition is set to reach the maximum number of iterations of 20 rounds. At this time, it is observed that the average success rate of the selected substring waveforms can reach more than 80%, indicating that the optimization has converged to an excellent level. The optimization and screening process of the first stage ends here. The 32 16-bit binary strings that are finally retained in the population are regarded as high-quality short waveform segments that have been optimized and screened. They will serve as basic building blocks for the subsequent second-stage long waveform combination optimization.

[0084] The lower layer of the present invention utilizes 32 high-quality, 16-bit short waveform segments selected from the upper layer as basic building blocks. A genetic algorithm optimizes the combination of these short segments to construct a 128-bit long sequence composed of eight short segments. These long sequences are designed to have more complex pattern characteristics, making them more difficult to predict and filter out by the adaptive algorithm of the target PPG device, thereby achieving more effective interference.

[0085] First, during the encoding and initialization steps, the long interference waveform is encoded as a 128-bit sequence composed of eight 16-bit short segments selected from the first phase. The control unit constructs an initial population of 100 such 128-bit long waveform sequences. This initial population is generated by randomly selecting and concatenating long sequences from a library of 32 short segments. A heuristic rule is also introduced, specifically prioritizing the arrangement of substrings that demonstrated a high "interference success rate" in the first phase of the experiment, in order to initially include individuals with high potential.

[0086] Next, to evaluate the potential interference performance of each 128-bit long waveform sequence, the control unit uses a comprehensive anti-jamming integrated measure (AIM). The AIM indicator is designed to balance two key characteristics of the waveform: one is non-periodicity, which is reflected by a low autocorrelation sidelobe level (PSL); the other is the dispersion of spectral energy, which is reflected by a high spectral entropy (SE). The calculation of PSL is based on the autocorrelation function R of the sequence. xx (k) For a binary sequence s with a length of N (in this embodiment, N=128), its autocorrelation function is defined as This function measures the similarity of a sequence to itself at different delays k. PSL is thus defined as:

[0087]

[0088] The calculation of SE first obtains the power spectrum P(f) of the long waveform sequence, normalizes it to obtain the probability distribution P(f), and the spectrum entropy SE is defined as The final AIM index is obtained by weighted summation of these two normalized indicators. The specific calculation formula is:

[0089]

[0090] Weight coefficient w PSL and w SE In the experiments of this embodiment, the initial setting is 0.5. min and PSL max are the minimum and maximum values ​​of the autocorrelation sidelobes in the current population, SE min and SE max are the minimum and maximum spectral entropy in the current population, respectively.

[0091] The optimization process then enters an iterative loop within the genetic algorithm. In each iteration, parent selection is first performed: 10 sequences are randomly sampled from the current population of 100 sequences, and the individuals with the best AIM index are selected as parents. The crossover operation is performed using 16-bit short waveform segments as the basic unit, using a single-point crossover method, with the crossover point selected at the boundary between these segments. The mutation operation involves the complete replacement of one or more 16-bit short segments within the long waveform. The new segments are randomly selected from the 32 segments selected in the first stage, and this replacement is performed only on segments with a Hamming distance of less than 3 to the current substring. This ensures that the new sequence performs local optimization without completely destroying the established excellent properties. Through the crossover and mutation operations, in this embodiment, each generation produces a total of 100 offspring individuals, of which 70 are generated by crossover and 30 by mutation. The population is then updated: the newly generated 100 offspring are merged with the original parent population (100). Calculate the AIM values ​​of all individuals in the new population, sort them according to the AIM value, and select the 100 individuals with the highest fitness to enter the next generation, thereby keeping the population size unchanged.

[0092] The iterative process of this genetic algorithm will continue until the preset termination condition is met. In this embodiment, the second stage optimization process is terminated when it is observed that the average value or optimal value of the AIM of the population no longer shows significant improvement over multiple generations of iterations, that is, when the algorithm reaches a convergence state.

[0093] Finally, after the genetic algorithm iterations are complete, the control unit retains 100 candidate long waveforms from the resulting population. To ultimately determine the optimal interference waveform, actual interference experiments are conducted on each of these candidate long waveforms, and their interference success rates (SR(x)) in real-world scenarios are evaluated, using the same definition as in the first stage. Ultimately, the waveform with the highest SR(x) value is selected as the optimal interference waveform output by the present invention.

[0094] In this embodiment, at least two modulation modes are supported: On-Off Keying (OOK) modulation and Pulse Width Modulation (PWM).

[0095] For On-Off Keying (OOK) modulation:

[0096] The control unit directly maps each bit in the 128-bit binary sequence to the on / off state of the infrared light source. Specifically, when a bit in the sequence is '1', the infrared light source is on and emits infrared light for the corresponding time unit; when a bit in the sequence is '0', the infrared light source is off and does not emit infrared light for the corresponding time unit.

[0097] For Pulse Width Modulation (PWM):

[0098] The control unit maps each bit (or combination of bits) in the 128-bit binary sequence to the duty cycle of a PWM signal cycle. In this embodiment, each bit in the sequence directly determines the duty cycle of a PWM cycle. For example, when a bit in the sequence is '0', the control unit generates a PWM signal with a low duty cycle (20%) to drive the infrared light source; when a bit in the sequence is '1', a PWM signal with a high duty cycle (80%) is generated.

[0099] The control unit converts the optimized binary sequence into a corresponding drive signal using either the OOK or PWM modulation schemes described above, based on a preset configuration or dynamic selection. This drive signal is then applied to an infrared light source (such as an infrared LED), causing it to emit a modulated infrared beam according to the generated modulation waveform. This beam is then used to interfere with the target wearable PPG device.

[0100] In a specific embodiment, based on Figure 1 The device used to interfere with the wearable photoplethysmography device was used to perform interference experiments and record the results.

[0101] First, we tested the "heart rate" interference, using OOK and PWM modulation techniques. The specific configurations are as follows:

[0102] OOK modulation method: Use a commercial infrared LED with a wavelength of 850nm, flashing at 0.25s intervals at 5W power;

[0103] PWM modulation method: PWM infrared LED with a symbol period (duration of each PWM signal modulation period) of 0.2-0.8s, a wavelength of 850nm, and a power of 5W.

[0104] Twenty-three participants (19 healthy individuals and four with respiratory diseases) wore the interfered device. We interfered with it at a distance of 100 cm and an incident angle of 30°. We used the proposed infrared waveform optimization framework to select the optimal waveform, and recorded heart rate values ​​during the non-interference period as reference values ​​for the wearable device. In addition, we used electrocardiography (ECG) to obtain the actual heart rate for a more comprehensive evaluation of the interference effect.

[0105] 1. Heart rate increase experiment

[0106] In a total of 2000 "heart rate increase" interferences (each interference was 1 minute), Figure 3 The error distribution between the PPG device measurements and the reference heart rate is shown:

[0107] OOK: The jamming success rate was 83.35%, of which 54.00% of the jamming errors exceeded 4 bpm;

[0108] PWM: The interference success rate was 75.00%, and 44.10% of the errors were greater than 4 bpm;

[0109] In addition, the Bland-Altman plot ( Figure 3 ) shows that, under OOK modulation, 95% of the error values ​​are between -3.56bpm and 11.66bpm; under PWM modulation, 95% of the error values ​​are between -3.85bpm and 9.63bpm. The upper limits of consistency reach 11.66bpm (OOK) and 9.63bpm (PWM), respectively, indicating that within this range, both OOK and PWM have a high probability of causing PPG devices to overestimate heart rate.

[0110] When compared with the true heart rate of ECG, 72.20% and 66.64% of the instances of OOK and PWM, respectively, have errors greater than 4 bpm.

[0111] It can be seen that in this scenario, the OOK modulation mode has a slightly better interference enhancement effect than PWM.

[0112] 2. Heart rate reduction experiment

[0113] Similarly, we counted the deviations between the PPG device measurements and the reference heart rate for 2000 “heart rate reduction” disturbances (1 minute per disturbance), as shown in Figure 2. Figure 4 As shown:

[0114] OOK: The jamming success rate was 86.35%, of which 58.95% had errors exceeding 4 bpm;

[0115] PWM: The interference success rate was 74.35%, of which 47.35% had errors greater than 4 bpm;

[0116] In addition, the Bland-Altman plot ( Figure 4 ) shows that under OOK modulation, 95% of the error values ​​are between -12.48bpm and 2.94bpm; under PWM modulation, 95% of the error values ​​are between -10.70bpm and 4.48bpm. The lower limits of consistency reach -12.48bpm (OOK) and -10.70bpm (PWM), respectively, indicating that within this range, both OOK and PWM have a high probability of causing PPG devices to underestimate heart rate.

[0117] When compared with ECG values, 70.00% and 55.10% of the samples of OOK and PWM, respectively, have errors greater than 4 bpm.

[0118] Overall, OOK modulation is slightly better than PWM in both "increasing" and "decreasing" heart rate readings.

[0119] 2. Blood oxygen saturation interference experiment and results

[0120] The experimental settings of the blood oxygen saturation interference experiment were the same as those of the heart rate interference experiment.

[0121] Because healthy individuals typically have blood oxygen saturation close to 100%, there's limited room for further "improvement" in these readings. Therefore, we only counted cases where the reference oxygen saturation was strictly below 100%. In addition to reference readings from the PPG device during non-interference periods, we also used a shielded clinical oximeter to record actual blood oxygen saturation. The experiment also employed both OOK and PWM infrared modulation methods.

[0122] 1. Blood oxygen saturation increase experiment

[0123] We performed a total of 2,000 "blood oxygen saturation increase" interferences (each interference lasted 1 minute). Figure 5 The error distribution between the fake blood oxygen saturation and the reference blood oxygen saturation is shown (the blood oxygen saturation of the subjects ranges from about 89% to 99%):

[0124] OOK: 61.70% of interferences resulted in elevated blood oxygen saturation, and 17.00% of the samples had errors exceeding 4%;

[0125] PWM: The interference success rate reached 71.70%, and 19.00% of the errors were greater than 4%;

[0126] In addition, the Bland-Altman plot ( Figure 5 ) shows that under OOK modulation, the 95th percentile error ranges from -3.27% to 6.17%; under PWM modulation, the 95th percentile error ranges from -3.73% to 6.13%. The upper limits of consistency reach 6.17% (OOK) and 6.13% (PWM), respectively, indicating that within this range, both modulation methods can cause PPG devices to overestimate blood oxygen saturation.

[0127] When compared with the actual blood oxygen saturation, 17.89% and 20.22% of the instances of OOK and PWM, respectively, have errors greater than 4%.

[0128] 2. Blood oxygen saturation reduction experiment

[0129] For 2000 "blood oxygen saturation reduction" interferences (each interference lasts 1 minute), Figure 6 The error distribution of the fake blood oxygen saturation and the reference blood oxygen saturation is shown:

[0130] OOK: The success rate is 76.70%, of which 28.35% of the samples have an error greater than 4%;

[0131] PWM: success rate 78.00%, with 27.70% of errors exceeding 4%;

[0132] In addition, the Bland-Altman plot ( Figure 6 ) shows that under OOK modulation, the 95% error ranges from -7.18% to 2.62%; under PWM modulation, the 95% error ranges from -6.77% to 2.49%. The lower limits of consistency reach -7.18% (OOK) and -6.77% (PWM), respectively, indicating that within this range, both modulation methods can cause PPG devices to overestimate blood oxygen saturation.

[0133] Compared with the true blood oxygen saturation, 33.61% and 33.05% of the instances of OOK and PWM, respectively, have errors greater than 4%.

[0134] It can be seen that the PWM modulation method is slightly better in the blood oxygen saturation interference.

[0135] The device for interfering with a wearable photoplethysmography device proposed in the embodiments of the present disclosure controls the interference with the heart rate and blood oxygen saturation readings of the interfering object. Furthermore, the selected optimization framework is simple, effective, and has good convergence effects, enabling rapid convergence to a waveform with a high success rate. Therefore, in the existing medical and health data security testing process, controllable interference data can be generated to analyze and discover the generation behavior of the interference data during the test, thereby ensuring the accuracy and reliability of the medical and health data security testing process. Furthermore, the interference generation method can also be used in the process of data security confrontation to confuse and deceive attack behaviors, thereby improving the security of medical and health data.

[0136] and Figure 1 Corresponding to the apparatus for interfering with a wearable photoplethysmography device, the present disclosure also provides a method for interfering with a wearable photoplethysmography device, such as Figure 7 As shown, the method may specifically include:

[0137] S701, using the adaptive genetic algorithm based on the upper structure and the lower structure to generate a modulation waveform, the algorithm optimizes the indicators related to the manipulation effect to improve the manipulation effect of the reading, wherein the modulation waveform generated by the adaptive genetic algorithm based on the upper structure and the lower structure is based on Figure 1 obtained by the device in;

[0138] S702, controlling the infrared light source to emit a modulated infrared light beam according to the modulation waveform;

[0139] S703 , directing the modulated infrared light beam to the wearable photoplethysmography device to change the heart rate and / or blood oxygen saturation readings of the device.

[0140] In addition, it should be noted that the various parameters selected and used in this disclosure can be adjusted according to actual conditions and are not limited here.

[0141] Combine Figure 8 As shown, the embodiment of the present disclosure also provides a device 800 for interfering with a wearable photoplethysmography device, including a processor 804 and a memory 801. Optionally, the system may also include a communication interface 802 and a bus 803. The processor 804, the communication interface 802, and the memory 801 can communicate with each other via the bus 803. The communication interface 802 can be used for information transmission. The processor 804 can call the logic instructions in the memory 801 to execute the method of interfering with the wearable photoplethysmography device of the above embodiment.

[0142] In addition, the logic instructions in the memory 801 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0143] Memory 801, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 804 executes the program instructions / modules stored in memory 801 to perform functional applications and data processing, thereby implementing the method for interfering with a wearable photoplethysmography device in the above-mentioned embodiments.

[0144] The memory 801 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 801 may include high-speed random access memory and non-volatile memory.

[0145] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to interfere with a wearable photoplethysmography device.

[0146] The aforementioned computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0147] The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method of the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code, or a transient storage medium.

[0148] The above description and accompanying drawings sufficiently illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Unless expressly required, individual components and functions are optional, and the order of operation may vary. Portions and features of some embodiments may be included in or replaced with portions and features of other embodiments. As used in the description of the embodiments, unless the context clearly indicates otherwise, the singular forms "a," "an," and "the" are intended to include the plural forms as well. Similarly, the term "and / or," as used in this application, means including any and all possible combinations of one or more of the associated listed items. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of the stated features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof. In the absence of further limitations, the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, or device comprising the elements. In this document, each embodiment may focus on the differences from other embodiments, and similar parts between the embodiments can be referenced to each other. For methods, products, etc. disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, then the relevant parts can be referenced to the description of the method section.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, the functional units in the embodiments of the present disclosure may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0151] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0152] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0157] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0158] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of this disclosure can be achieved, and this document is not limited here.

[0159] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A device for interfering with a wearable photoplethysmography device, characterized in that: The device comprises: an infrared light source configured to emit a modulated infrared light beam; a control unit configured to generate a modulation waveform and control the infrared light source to emit an infrared light beam according to the modulation waveform to change the heart rate and blood oxygen saturation readings of the wearable photoplethysmography device; The control unit is configured to generate the modulation waveform using an adaptive genetic algorithm based on an upper layer structure and a lower layer structure, and the algorithm improves the manipulation effect on the reading by optimizing indicators related to the manipulation effect.

2. The device according to claim 1, wherein The adaptive genetic algorithm adopts a two-layer structure, including an upper structure and a lower structure: The upper structure is configured to screen short waveform segments with good performance and determine the initial population of the lower structure through multiple generations of iteration. Each generation performs the following steps: Using a Bayesian optimization algorithm, a Gaussian process surrogate model is constructed, wherein the Gaussian process surrogate model is used to predict the expected improvement value of the candidate waveform segment based on the evaluated waveform segment and its interference success rate; According to the expected improvement value, selecting waveform segments with potential to meet preset conditions from the candidate waveform segments as experimental samples; Evaluate the interference success rate of the experimental samples through experiments, and use the evaluation results to update the Gaussian process agent model; Based on the interference success rate evaluated in the experiment, waveform segments whose interference success rate meets the preset success threshold are selected as the initial population of the lower structure; The lower structure is configured to generate its initial population by combining short waveform segments determined by the upper structure, and optimize the combination of the waveform segments by a genetic algorithm to improve the waveform's ability to resist interference suppression, wherein the genetic algorithm includes: Generate offspring sequences by performing a single-point crossover operation at the substring boundary and a mutation operation by replacing the substring with other substrings whose Hamming distance is less than a preset threshold; The sequence for the next generation iteration is selected according to the anti-interference index, and the crossover, mutation and selection steps are repeated until the anti-interference index tends to be stable.

3. The device according to claim 2, wherein The calculation method of the anti-interference index is as follows: in: AIM stands for Anti-Interference Index; PSL is the autocorrelation sidelobe, defined as PSL = max(|R(k)| / R(0), k≠0), where k = 1, 2, ..., N-1, and R(k) is the autocorrelation function; SE is the spectral entropy, defined as p(f) is the normalized power spectrum; w PSL and w SE are the weights of the autocorrelation sidelobe and the spectrum entropy, and w PSL +w SE =1; PSL min and PSL max are the minimum and maximum values ​​of the autocorrelation sidelobes in the current population, respectively; SE min and SE max are the minimum and maximum spectral entropy in the current population, respectively.

4. The device according to claim 1, characterized in that The wavelength of the infrared light beam emitted by the infrared light source is in the range of 500 nanometers to 1000 nanometers.

5. The device according to claim 2, wherein The interference success rate is determined by: Conducting m interference experiments using the waveform segments on n participants wearing the wearable photoplethysmography device; Determining a number q of successful interferences, wherein successful interference refers to the waveform segment causing a predetermined change in the target physiological parameter of the participant; The interference success rate is calculated as 6. The device according to claim 5, wherein The target physiological parameter is selected from at least one of heart rate and blood oxygen saturation, and the preset change is an increase or decrease in the value of the target physiological parameter.

7. The device according to any one of claims 1 to 6, wherein: The adaptive genetic algorithm generates the modulated waveform in the form of a binary string, and the control unit is configured to control the infrared light source to emit the modulated infrared light beam based on the binary string using at least one modulation method selected from on-off keying modulation and pulse width modulation.

8. The device according to claim 2, wherein The Gaussian process proxy model uses a Hamming distance kernel function, and the Gaussian process proxy model includes a noise regularization term.

9. The device according to claim 2, wherein The genetic algorithm of the lower structure uses the short waveform segments screened out by the upper structure as coding units for combinatorial optimization, and both the crossover operation and the mutation operation are performed on the short waveform segments as units.

10. A method for interfering with a wearable photoplethysmography device, characterized in that: The method comprises: generating a modulation waveform using an adaptive genetic algorithm based on the upper and lower structures, the algorithm improving the manipulation effect on the reading by optimizing an indicator related to the manipulation effect, wherein the modulation waveform generated by the adaptive genetic algorithm based on the upper and lower structures is obtained by the apparatus according to any one of claims 1 to 9; controlling the infrared light source to emit a modulated infrared light beam according to the modulation waveform; The modulated infrared light beam is directed toward the wearable photoplethysmography device to alter the device's heart rate and / or blood oxygen saturation readings.

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