Method and system for optimizing hardness of seat cushion of seat for old people

Abstract

The invention provides a method and system for optimizing the hardness of a seat cushion of an old person. The method comprises the steps that lower limb myoelectricity data and cushion body pressure distribution data of the old person in the STS movement process are collected; performing root mean square and median frequency analysis on the myoelectricity data; extracting a maximum pressure value, an average pressure value, an average contact area and a maximum contact area from the body pressure distribution data; the processed myoelectricity data and the body pressure distribution data are imported into statistical analysis software for correlation analysis and quadratic linear regression analysis; according to the analysis result, the target hardness of the cushion material is selected, and the old people seat cushion is designed. According to the scheme, by means of surface myoelectricity testing and body pressure distribution testing, the comfort condition of still sitting of the old people when the old people use cushions with different hardness and the fatigue and force application level condition of lower limb muscles during STS are tested, analysis is conducted in combination with subjective feelings of subjects, and then the cushion with the ideal hardness is designed.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, and in particular relates to a method and system for optimizing the softness and hardness of seat cushions for elderly people. Background Technology

[0002] With the arrival of an aging society, the self-care ability of the elderly has become an important factor in family happiness. Literature review shows that the self-care ability of the elderly is closely related to STS (sit-to-stand), and STS is inseparable from the chairs for the elderly. In current research, few studies have explored the impact of seat cushion firmness on STS, while a large number of studies have already been conducted on comfort.

[0003] As people age, their physiological functions gradually decline, with a noticeable deterioration in the function of muscles, bones, and limbs, especially in the lower limbs and joints, leading to a significant decrease in sitting and standing ability. Reduced lower limb muscle strength and functional decline severely impact the ability of older adults to transition from sitting to standing. In daily life, the action of standing up from a chair is essential, requiring an average of 60 repetitions per day, approximately 3 times per hour. Although the sitting-standing process is brief, it is an extremely important task, demonstrating the self-care ability of older adults. Furthermore, sitting-standing exercises can be used for lower limb rehabilitation training and can also assess the degree of lower limb muscle strength and weakness, making them a highly reliable tool.

[0004] The ability to transition from sitting to standing is essential for maintaining independence in daily life. It forms the basis for everyday activities such as eating, toileting, dressing, and walking. If this ability declines, older adults may be unable to perform simple activities independently and become dependent on others. This loss of ability also reduces freedom of movement; difficulty sitting up can lead to reduced outings, social interactions, and activity participation, accelerating physical decline. Paying attention to the sitting-to-standing transition in older adults can prevent falls and complications. Older adults with poor sitting-to-standing ability are prone to losing balance when standing or sitting down, leading to falls. Statistics show that approximately 30% of falls in people over 65 occur during sitting-to-standing transitions, and falls are a leading cause of fractures, disability, and even death in the elderly. Reduced activity due to difficulty sitting up can lead to muscle atrophy, joint stiffness, and even complications such as pressure sores and deep vein thrombosis.

[0005] The sit-to-stand transition also reflects the overall health of older adults. It is an indicator of muscle strength and coordination: the sit-to-stand transition requires lower limb muscle strength (such as the quadriceps and glutes), core stability, and balance; a decline in these abilities often foreshadows sarcopenia, arthritis, or neurological disorders (such as Parkinson's disease). The number of times the "30-second Chair-Stand Test" can be completed can indirectly reflect cardiorespiratory endurance; a decrease in the number of tests may indicate a decline in cardiorespiratory function.

[0006] Therefore, analyzing the impact of seat cushion firmness on the STS (Self-Sensitive Tissue) of the elderly in order to optimize seat cushions is a direction that urgently needs research. Summary of the Invention

[0007] The main objective of this invention is to propose a method and system for optimizing the firmness of seat cushions for the elderly, aiming to analyze the impact of cushion firmness on the STS (Self-Sensitive Tissue) of the elderly in order to optimize the seat cushion.

[0008] To achieve the above objectives, this invention proposes a method for optimizing the firmness of a seat cushion for the elderly, comprising the following steps: Collect lower limb electromyography data and seat pressure distribution data of elderly people during STS exercise; Root mean square and median frequency analyses were performed on the electromyography data. The maximum pressure value, average pressure value, average contact area, and maximum contact area are extracted from the body pressure distribution data. Correlation analysis and quadratic linear regression analysis were performed on the processed electromyography data and body pressure distribution data; Based on the analysis results, select cushion materials with appropriate hardness and design seat cushions for elderly people.

[0009] Optionally, after the step of collecting lower limb electromyography data and seat pressure distribution data of the elderly during STS exercise, the subject is asked to make a subjective evaluation after each data collection.

[0010] Optionally, in the step of collecting lower limb electromyographic data and seat pressure distribution data of elderly people during STS exercise, the lower limb electromyographic data includes electromyographic signals of the biceps femoris and rectus femoris muscles.

[0011] Optionally, the step of performing root mean square and median frequency analysis on the electromyographic data includes: Time-domain analysis was performed on the electromyography data to calculate the electromyography integral value and root mean square value; Frequency domain analysis was performed on the electromyographic data to calculate the median frequency and average power frequency.

[0012] Optionally, in the step of collecting lower limb electromyography data and seat pressure distribution data of the elderly during STS exercise, the seat pressure distribution data is collected by a body pressure distribution measurement system and used to calculate the pressure and area parameters of the contact area between the seat and the human body.

[0013] Optionally, in the step of performing correlation analysis and quadratic linear regression analysis on the processed electromyography data and body pressure distribution data, the correlation analysis is used to determine the association between the cushion firmness and subjective evaluation and objective indicators, respectively, and the quadratic linear regression analysis is used to establish a model between the cushion firmness and muscle mechanical response.

[0014] Optionally, in the step of selecting a cushion material with appropriate hardness based on the analysis results and designing a seat cushion for the elderly, the hardness of the cushion material selected based on the analysis results is 60D~70D.

[0015] Optionally, in the step of selecting a cushion material with appropriate hardness based on the analysis results and designing a seat cushion for the elderly, the hardness of the cushion material is selected as 65D based on the analysis results.

[0016] The present invention also proposes a seat for the elderly, wherein the seat cushion is made of a material with a hardness of 60D to 70D, and the hardness of the cushion material is determined based on the above-mentioned optimization method.

[0017] This invention also proposes an optimization system for the firmness of seat cushions in elderly people's chairs, used to perform the above-mentioned optimization method, the optimization system comprising: The data acquisition module is used to collect lower limb electromyography data and seat pressure distribution data of elderly people during STS exercise. The data analysis module is used to perform root mean square and median frequency analysis on the electromyography data, and to extract the maximum pressure value, average pressure value, average contact area and maximum contact area from the body pressure distribution data. It also performs correlation analysis and quadratic linear regression analysis on the processed electromyography data and body pressure distribution data. The hardness generation module is used to generate the target hardness of the cushion material based on the analysis results.

[0018] The present invention also proposes a storage medium storing a computer program, including computer program instructions, wherein when the computer program instructions are executed by a computer device, the computer device performs the above-described optimization method.

[0019] In the technical solution of this invention, surface electromyography (SEMG) and body pressure distribution (BPD) tests are used to assess the comfort of elderly people sitting with cushions of varying firmness, as well as the fatigue and exertion levels of lower limb muscles during surface-to-seat (STS) testing. The test data and firmness are then analyzed together, and an ideal firmness cushion is designed based on the analysis results. This optimization method links STS with cushion firmness, providing elderly people with a comfortable seat that meets their ergonomic needs. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an embodiment of the method for optimizing the softness and hardness of a seat cushion for the elderly provided by the present invention; Figure 2 This is the electromyography (EMG) of one of the subjects in this embodiment; Figure 3 This is a graph showing the trend of pressure index changes of the subjects on seat cushions of different hardness in this embodiment; Figure 4 This is a graph showing the trend of electromyographic parameters of the subjects in this embodiment on seat cushions of different hardness. Figure 5 This is a visualization analysis chart showing the correlation between hardness and body pressure data in this embodiment; Figure 6 This is a visualization analysis chart showing the correlation between hardness and electromyography data in this embodiment.

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] To better describe and illustrate the embodiments of this application, reference may be made to one or more accompanying drawings, but the additional details or examples used to describe the drawings should not be considered as limiting the scope of any of the inventive creations of this application, the embodiments or preferred methods described herein.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0026] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. It should be understood that the following embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0027] The self-care ability of older adults is closely related to sit-to-stand (STS), and STS is inseparable from elderly chairs. In current research, few studies have explored the impact of seat cushion firmness on STS, while a large number of studies have already been conducted on comfort.

[0028] Therefore, this invention proposes a method for optimizing the firmness of seat cushions for elderly people. Figure 1 For one embodiment of this optimization method, please refer to [link / reference]. Figure 1 The specific steps are as follows.

[0029] Step S1: Collect lower limb electromyography data and seat pressure distribution data of elderly people during STS exercise.

[0030] It should be noted that STS is an abbreviation for sit-to-stand, meaning sit-to-stand or sitting-to-standing exercises. STS is an important functional activity. With increasing age, lower limb muscle strength decreases, and older adults, especially those affected by illness, are prone to failing STS exercises, thus losing their ability to care for themselves. STS exercises are affected by external and internal factors. External factors mainly include the environment and seating, while internal factors include lower limb muscles, knee joints, ankle joints, and hip joints.

[0031] The transition of posture in STS exercise can be roughly divided into three stages: the first stage is the preparation stage, in which the upper limbs lean forward and the center of gravity shifts forward, reducing the horizontal distance between the feet and the center of gravity of the upper limbs; the second stage is the standing stage, in which the lower limbs exert force and the body segments above the knees rotate around the knees, allowing the body to lift off the seat; the third stage is the completion of the standing action.

[0032] In this embodiment, data acquisition is achieved through the following experimental process.

[0033] Step S11: Select the experimental cushion.

[0034] This embodiment uses a sponge cushion. In practical applications, other cushion materials can also be selected. The cushion information used in this embodiment is shown in Table 1.

[0035] Table 1 Seat Cushion Information

[0036] Step S12: Select the experimental chair.

[0037] The seat height adjustment range used in this embodiment is 36cm-44cm. It is a standard seat type without armrests or a backrest. The seat cushion can be switched at any time. Experimental data is recorded as long as the subject sits down smoothly without the seat cushion tilting.

[0038] Step S13: Measure the softness and hardness of the sponge cushion.

[0039] In this embodiment, the hardness of the seat cushion was tested using a Shore hardness tester LX-F, a special tester for sponge. The tester was placed in the center of the sponge and then placed stably on the sponge before taking the reading. The hardness of the four seat cushions were 50D, 65D, 75D, and 85D, respectively.

[0040] Step S14: Set up the experimental procedure.

[0041] This embodiment references numerous studies, and the specific method is to set the experiment duration to 1 minute (the more frequent and shorter the interval between STS, the more fatigued the lower limbs).

[0042] Because the seated position provides good support, this embodiment mainly analyzes and compares the data when standing. The invariants controlled in this embodiment are: seat height is the same as the popliteal joint height; no armrests, 80% seat depth with backrest; relaxed sitting posture, maintaining stillness; when standing, arms are crossed over the chest to minimize the impact of the upper limbs on STS, and legs are straight and back is upright.

[0043] Step S15: Select experimental subjects.

[0044] This study recruited 22 elderly individuals aged 50 and above as the experimental group, with an average age of 61 years, an average height of 160.6 cm, an average weight of 66 kg, and an average BMI of 25.6. None of the participants had any serious muscular, neurological, or skeletal diseases (e.g., Parkinson's disease, sarcopenia) that could severely impair their motor or cognitive abilities. All participants consented to the study, and each was provided with a certain fee. The participants' information is shown in Table 2.

[0045] Table 2 Subject Information

[0046] Step S16: Select experimental equipment.

[0047] The equipment selected in this embodiment is as follows.

[0048] Surface electromyography instrument and system: ERGOLABEXG; Smart wearable physiological instrument, ERGOLAB platform, MOTIONLAB; Body pressure distribution testing system: Body pressure measurement system (BPMS) provided by Tekscan, Inc., USA.

[0049] Step S17: Test the experimental subjects.

[0050] This embodiment first performs an electromyography (EMG) test: the subject's leg muscles are bulged to locate the correct muscle position. Before applying electrode patches to the muscles, they are wiped with alcohol to remove sebum and reduce interference with the experiment. The distance between two electrode patches is 20mm, and the other electrode is placed on the bone of the leg. The subject sits on a cushion, and after sitting still, the seat height is adjusted to be the same as the popliteal joint. The seat has no armrests or backrest, and the seat depth is 80% of the seat width. The subject maintains an upright posture, with hands clasped across the chest, feet flat on the ground, and knees, ankles, and hips all at 90 degrees. Upon hearing the command "Stand," the subject stands up from the seat. The rhythm and speed of standing are free, but the standing time must not exceed 5 seconds, otherwise it is considered invalid. A pre-experiment is conducted on the subjects before the formal experiment begins. After the formal experiment begins, each cushion is tested 3 times. After the test, the subject remains standing until the next cushion is replaced, and then the above steps are repeated. Data is recorded within the first 5 seconds after standing is completed.

[0051] Then, the body pressure distribution test was conducted: a comfort evaluation was completed, including 7 procedures.

[0052] Procedure 1: Prepare the test chair; Procedure 2: Calibrate the pressure distribution measurement system; Procedure 3: Explain the testing process to the test personnel; Procedure 4: Establish the concept of comfort; Procedure 5: Measure body pressure distribution; Procedure 6: Complete the subjective rating form; Program 7: Loop Test.

[0053] In procedure 6, after each cushion test, the subjects were asked to fill out a questionnaire, the contents of which are shown in Table 3.

[0054] Table 3 Subjective Feelings Questionnaire

[0055] Step S17: Generate test results.

[0056] Electromyography (EMG) data observation: This test used two channels, therefore the EMG data consists of two line segments. Please refer to [link / reference]. Figure 2 The upper line segment represents channel one, showing the electromyographic response of the biceps femoris muscle, while the lower line segment represents channel two, showing the response of the rectus femoris muscle. This graph captures the electromyographic changes in elderly individuals while they are standing. 50D, 65D, 75D, and 85D represent the firmness of the cushion used in the test. The stronger the change in the line segment, the greater the force of the movement.

[0057] from Figure 2The results show that the rectus femoris muscle exhibits relatively small changes, while the biceps femoris muscle shows a stronger response. Among the four hardness levels, the 85D cushion produces continuous and strong peaks, while the 50D and 75D show relatively weak peaks, and the 65D cushion exhibits obvious peak changes. It should be noted that the electromyography (EMG) images extracted in this study represent segments with relatively obvious EMG responses.

[0058] Body pressure data observation: Pressure values, contact area, and center movement trajectory are obtained from the body pressure distribution measurement system. The maximum contact area refers to the largest contact surface when the human body contacts the cushion during the test. Generally, the larger the contact area between the person and the cushion, the more dispersed the pressure on the lower limbs, resulting in better comfort. The average contact area is the average contact area between the person and the cushion throughout the entire test; a larger average contact area indicates better comfort. The maximum pressure value is the instantaneous moment of maximum force during the test, while the average pressure value is the average pressure magnitude throughout the entire test. A higher pressure value indicates a greater reaction force experienced by the subject; the greater the force, the worse the comfort.

[0059] In the graph exported by the system, the pressure value can be read from the lower right corner of the graph, but the contact area needs to be exported in CSV format for calculation. In this embodiment, the subject with the highest pressure peak was the 85D cushion, and the subject with the lowest was the 50D cushion. For a more objective analysis, this embodiment will statistically analyze the pressure values ​​and contact area values ​​of all subjects and import them into the MATLAB system for linear regression analysis.

[0060] Step S2: Perform root mean square and median frequency analysis on the electromyography data.

[0061] In this embodiment, the analysis process is implemented through the following specific steps.

[0062] Step S21: Process the electromyography data using time-domain analysis.

[0063] Time-domain analysis treats electromyography (EMG) signals as a function of time, calculating statistical indicators such as the signal mean, variance, and amplitude to reflect changes in signal amplitude over time. Currently, the most common approach involves shaping and filtering the signal, then calculating statistical indicators such as the signal mean and amplitude histogram to reflect changes in signal amplitude over time. Commonly used indicators include the integral electromyography (IEMG) value and the root mean square (RMS) value, both of which are formulated as follows.

[0064] iEMG formula:

[0065] RMS formula:

[0066] In the formula, t refers to any time interval, and T is the complete period.

[0067] It should be noted that iEMG integrated electromyography refers to the total area enclosed by the curve of the measured surface electromyography signal per unit time after rectification and smoothing. It represents the total discharge of motor units when the muscle is involved in activity within a certain period, reflecting the strength of the muscle's electromyographic activity over a period of time. The level of iEMG reflects the discharge magnitude of each motor unit involved in muscle contraction during exercise and the number of muscle fibers. Generally, the larger the amplitude, the more severe the fatigue. It is an important indicator for evaluating muscle fatigue.

[0068] The magnitude of the RMS (Radial Electromyography) signal depends on the amplitude of the surface electromyography (SEMG) amplitude. By comparing the RMS at different times, the timing and extent of fatigue can be determined. Generally speaking, in both static and dynamic movements, the amplitude of the SEMG signal increases from the initial state to the fatigue state; that is, the RMS increases with increasing fatigue.

[0069] Step S21: Process the electromyography data using frequency domain analysis.

[0070] Frequency domain analysis, also known as power spectrum analysis, is the analysis of the frequency characteristics of real-time biological signals. Frequency domain signals are derived from time-domain signals through a Fast Fourier Transform (FFT) and can reflect the intensity of electromyographic (EMG) signals across different frequency ranges, providing information about the frequency characteristics of the EMG signal. Frequency domain analysis metrics include: Median Frequency (Hz): This refers to the median value of the discharge frequency, specifically the median discharge frequency during muscle contraction. It generally decreases as the duration of movement increases. Due to the different ratios of fast and slow-twitch muscle fibers in skeletal muscle, the MF value varies between different parts of the skeletal muscle. Fast-twitch fibers are excited at high frequencies, while slow-twitch fibers are excited at low frequencies.

[0071] The formula for calculating MF is as follows:

[0072] In the formula, PSD refers to power spectral density, f represents frequency, that is, the number of times a periodic change is completed per unit time, and the unit is Hertz.

[0073] Mean Power Frequency (Hz): This refers to the average frequency over a given period. In a state of muscle fatigue, the surface electromyography frequency domain index MPF shows a decreasing trend.

[0074] The MPF calculation formula is as follows:

[0075] In the formula, PSD refers to power spectral density, f represents frequency, that is, the number of times a periodic change is completed per unit time, and the unit is Hertz.

[0076] Step S3: Extract the maximum pressure value, average pressure value, average contact area, and maximum contact area from the body pressure distribution data.

[0077] In this embodiment, the extraction process is implemented through the following specific steps.

[0078] Step S31: Body pressure data processing.

[0079] The pressure distribution record file exported from the testing process (filenamed "csv") was converted into a pressure matrix ASCII plain text document that can be read by a general computer. This ASCII document was then imported into Office Excel 2004 as the basis for calculation and statistical analysis. The BPMS then directly read pressure distribution indicators such as average pressure and maximum pressure contact area.

[0080] In this embodiment, the complete data of body pressure data and electromyography data obtained from steps S2 and S3 are shown in Tables 4, 5, 6, and 7.

[0081] Table 4. Body pressure and electromyography at 50D

[0082] Table 5. Body pressure and electromyography at 65D: .

[0083] Table 5. Body pressure and electromyography at 75D

[0084] Table 6. Body pressure and electromyography of 85D

[0085] Step S4: Import the processed electromyography data and body pressure distribution data into statistical analysis software for correlation analysis and quadratic linear regression analysis.

[0086] In this embodiment, the extraction process is implemented through the following specific steps.

[0087] Step S41: Perform quadratic regression linear analysis on the body pressure distribution data.

[0088] A quadratic regression linear regression was performed on the data obtained from the body pressure distribution experiment, with cushion hardness as the x-axis and various indicators as the y-axis, to obtain the following results: Figure 3 A graph showing the relationship between seat cushion firmness and various indicators. From... Figure 3As can be seen, with the increase of seat cushion hardness, the average pressure, maximum pressure, and average contact area all first decrease and then rise rapidly, while the maximum contact area shows a decreasing trend.

[0089] Therefore, the lower the hardness of the seat surface, the larger the contact area. The larger contact area will disperse the pressure on the seat surface, thus reducing the maximum pressure.

[0090] Step S42: Perform quadratic regression linear analysis on the electromyography data.

[0091] A quadratic regression linear analysis was performed on the data obtained from the electromyography (EMG) experiment. The results were obtained with cushion firmness as the x-axis and RMS and MPF indices as the y-axis. Figure 4 The diagram shows the relationship between seat cushion firmness and various indicators. Blue indicates data for the biceps femoris, and green indicates data for the rectus femoris. From... Figure 4 It was found that as the hardness of the seat cushion increases, the work done by the biceps femoris increases, and the fatigue level also increases; while the work done by the rectus femoris gradually decreases, and the fatigue level also gradually increases.

[0092] Generally, the harder the object, the stronger its supporting force, and therefore the less work should be done when a person stands. In this embodiment, this phenomenon occurs in the biceps femoris because increased hardness leads to greater pressure on the biceps femoris, generating explosive force when standing, causing the force level to continuously rise, while the force level of the rectus femoris relatively decreases. Combined with step S42, it can be seen that greater hardness results in worse comfort, and comfort affects the force exertion in STS (Standing Tactics). Therefore, the decreased comfort and increased force exertion of the biceps femoris make STS increasingly difficult with increasing hardness.

[0093] Step S43: Perform correlation analysis between body pressure distribution test and electromyography data.

[0094] In this embodiment, the data is sorted according to different levels of hardness. The differences in data under different hardness levels are observed to prepare for subsequent analysis.

[0095] Stiffness and Body Pressure Data: Body pressure data from different cushion stiffness tests were imported into SPSSAU for correlation analysis, and the resulting visualizations were visualized. Figure 5 And Table 8. According to Figure 5 The results show a significant correlation between hardness and changes in maximum pressure. Therefore, based on this analysis, it can be concluded that the greater the hardness, the worse the human comfort.

[0096] Secondly, according to Table 8, the maximum pressure is significantly correlated with the average pressure, so the increase in hardness will inevitably lead to an increase in average pressure; the maximum contact area is also significantly positively correlated with both the average pressure and the maximum pressure; the average contact area is also significantly positively correlated with the maximum contact area.

[0097] Table 8 Correlation Analysis Triangle Diagram

[0098] Stiffness and electromyography (EMG) data: EMG data from different cushion stiffness tests were imported into SPSSAU for correlation analysis, and the resulting visualizations were visualized. Figure 6 And Table 9. According to Figure 6 The results showed no significant correlation between stiffness and various electromyographic indicators.

[0099] Secondly, according to Table 9, the MPF of the biceps femoris is significantly negatively correlated with RMS, and positively correlated with the RMS of the rectus femoris; the MPF of the rectus femoris is positively correlated with RMS. This indicates that during STS, the higher the force level of the rectus femoris, the easier it is for both the rectus femoris and biceps femoris, and the easier the STS becomes.

[0100] Table 9 Correlation Analysis Triangle Diagram

[0101]

[0102] Hardness and subjective evaluation: The subjective evaluation results of different seat cushion hardness tests were imported into SPSSAU for correlation analysis, resulting in Tables 10 and 11. According to Table 10, hardness showed a significant negative correlation with softness, standing fatigue, noticeable force exertion on the outer thigh, and noticeable force exertion on the thigh joint; and a significant positive correlation with seat cushion elasticity, overall feel, and pressure sensation on the inner thigh.

[0103] Table 10 Correlation Analysis Triangle Diagram

[0104] As shown in Table 10, flexibility was positively and significantly correlated with a noticeable feeling of exertion on the outer thigh and the degree of standing fatigue. The comfort level of the thighs was significantly negatively correlated with the elasticity of the seat cushion and the feeling of pressure on the inner thighs; the comfort level of the thighs near the popliteal joint was significantly positively correlated with the seat height and the elasticity of the seat cushion, and significantly negatively correlated with the feeling of significant force exertion on the inner thighs; the comfort level of the thighs near the buttocks was significantly positively correlated with the seat height and the elasticity of the seat cushion; the degree of standing fatigue was significantly negatively correlated with the feeling of pressure on the inner thighs, the seat height, and the elasticity of the seat cushion, and significantly positively correlated with the feeling of significant force exertion on the outer thighs; the feeling of pressure on the inner thighs was significantly negatively correlated with the feeling of significant force exertion on the outer thighs; the seat height was significantly positively correlated with the elasticity of the seat cushion, and significantly negatively correlated with the feeling of significant force exertion on the inner thighs; the elasticity of the seat cushion was significantly negatively correlated with the feeling of significant force exertion on the inner thighs.

[0105] Overall feel and firmness, comfort of the thighs near the buttocks, seat height, cushion elasticity, and outer thigh position. The sense of force exertion on the lateral side showed a significant positive correlation, while the sense of force exertion on the inner thigh and outer thigh showed a significant negative correlation.

[0106] Table 11 Correlation Analysis Triangle Diagram

[0107] The subjective evaluation factors used in this test were imported into SPSSAU for validity analysis, and the results are shown in Table 12. Validity was verified using the KMO and Bartlett tests. As can be seen from the table, the KMO value is 0.704, which is between 0.7 and 0.8, indicating that the research data is suitable for information extraction (indirectly reflecting good validity).

[0108] Table 12 Questionnaire Validity Analysis

[0109]

[0110] Step S5: Based on the analysis results, select a cushion material with appropriate hardness and design a seat cushion for the elderly. In this embodiment, the design process is implemented through the following specific steps.

[0111] In this embodiment, through correlation analysis and quadratic linear regression analysis, the study confirmed that the subjective feelings in the experiment were basically consistent with the objective test results, and the reliability and validity of the subjective questionnaire were qualified. The hardness of the cushion material can be determined by analyzing the test results.

[0112] Based on the analysis of the above data, the greater the seat hardness, the lower the force exertion level of the rectus femoris muscle, while the force exertion and fatigue levels of the biceps femoris muscle increase accordingly. At the same time, the pressure on the seat surface also increases, the contact area decreases, and according to the subjective feelings of the subjects, when the hardness increases, the pressure on the inner thighs increases significantly, indicating that the biceps femoris muscle is indeed squeezed during the process of increasing hardness, causing the muscle to exert force forcibly when standing, which increases the burden on the muscle, while the rectus femoris muscle reduces its force exertion accordingly, but the fatigue level still increases.

[0113] Observations revealed that when using a 65D seat cushion, both the average and maximum pressure were lower than those of a 50D cushion, while the maximum contact area was greater, demonstrating superior comfort compared to 50D. Furthermore, the fatigue levels of both biceps femoris muscles were lower with a 65D cushion compared to a 50D cushion, with a lower level of muscle activation in the rectus femoris. Therefore, excluding cases where elderly individuals have insufficient biceps femoris muscle activation, using a 65D cushion can reduce compression of the biceps femoris, increasing its comfort, while simultaneously providing sufficient support to ensure the completion of STS (Sustaining Traumatism) in the elderly within a short period.

[0114] Therefore, in this embodiment, the hardness of the cushion material can be obtained from the subject to be 60D~70D, preferably 65D.

[0115] Based on the results of the above embodiments, when elderly people choose seat cushions, this solution uses surface electromyography (SEMG) and body pressure distribution (STS) tests to assess the comfort of elderly people sitting with different firmness cushions and the fatigue and exertion level of lower limb muscles during STS. The test data and firmness are combined and analyzed, and a seat cushion with ideal firmness is selected or customized based on the analysis results, providing elderly people with a comfortable seat that meets their human needs.

[0116] The present invention also provides a seat for the elderly. The main structure of the seat can be designed according to the actual usage scenarios of the elderly in their daily lives (such as at home, in nursing homes, outdoor activities, etc.) and the differentiated needs of different environments for seat functions, materials, and mobility. The seat cushion is made of a material with a hardness of 60D to 70D. The material can be polymer, natural plant, etc., and the specific hardness of the material is determined based on the above-mentioned optimization method.

[0117] The present invention also proposes an optimization system for the softness and hardness of seat cushions for the elderly, which is used to perform the above-mentioned optimization method. The optimization system includes a data acquisition module, a data analysis module, and a hardness generation module.

[0118] The data acquisition module collects lower limb electromyography (EMG) data and seat pressure distribution data of elderly individuals during STS exercises. The data analysis module performs root mean square and median frequency analysis on the EMG data and extracts the maximum pressure value, average pressure value, average contact area, and maximum contact area from the body pressure distribution data. It also performs correlation analysis and quadratic linear regression analysis on the processed EMG data and body pressure distribution data. The hardness generation module generates the target hardness of the seat material based on the analysis results. These modules can utilize the equipment used in the above embodiments. It is understood that the optimization system also includes a main control unit, a storage unit, etc.

[0119] The present invention also proposes a storage medium storing a computer program, the storage medium including computer program instructions, wherein when the computer program instructions are executed by a computer device, the computer device performs the above-described optimization method.

[0120] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0121] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for optimizing the firmness of a seat cushion for the elderly, characterized in that, Includes the following steps: S1. Collect lower limb electromyography data and seat pressure distribution data of elderly people during STS exercise; S2. Perform root mean square and median frequency analysis on the electromyography data; S3. Extract the maximum pressure value, average pressure value, average contact area, and maximum contact area from the body pressure distribution data; S4. Perform correlation analysis and quadratic linear regression analysis on the processed electromyography data and body pressure distribution data; S5. Based on the analysis results, select the target hardness of the cushion material and design the seat cushion for the elderly.

2. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, The step S1, which involves collecting lower limb electromyography data and seat pressure distribution data of elderly individuals during STS exercise, includes having the subjects provide subjective evaluations after each data collection session.

3. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, In step S1, which involves collecting lower limb electromyographic data and seat pressure distribution data of elderly individuals during STS exercise, the lower limb electromyographic data includes electromyographic signals of the biceps femoris and rectus femoris muscles.

4. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, The step S2, performing root mean square and median frequency analysis on the electromyographic data, includes: Time-domain analysis was performed on the electromyography data to calculate the electromyography integral value and root mean square value; Frequency domain analysis was performed on the electromyographic data to calculate the median frequency and average power frequency.

5. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, In step S1, which involves collecting lower limb electromyography data and seat pressure distribution data of elderly individuals during STS exercise, the seat pressure distribution data is collected through a body pressure distribution measurement system and used to calculate the pressure and area parameters of the contact area between the seat and the human body.

6. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, In step S4, which involves performing correlation analysis and quadratic linear regression analysis on the processed electromyography data and body pressure distribution data, the correlation analysis is used to determine the association between the cushion firmness and subjective evaluation and objective indicators, respectively, and the quadratic linear regression analysis is used to establish a model between the cushion firmness and muscle mechanical response.

7. The method for optimizing the firmness of a seat cushion for the elderly according to claim 1, characterized in that, In step S5, which involves selecting a cushion material with appropriate hardness based on the analysis results and designing a seat cushion for the elderly, the hardness of the cushion material is selected to be 60D~70D based on the analysis results.

8. A chair for the elderly, characterized in that, The seat cushion is made of a material with a hardness of 60D to 70D, and the hardness of the cushion material is determined by the optimization method based on any one of claims 1 to 7.

9. A system for optimizing the firmness of a seat cushion for the elderly, used to perform the optimization method as described in any one of claims 1-7, characterized in that, The optimization system includes: The data acquisition module is used to collect lower limb electromyography data and seat pressure distribution data of elderly people during STS exercise. The data analysis module is used to perform root mean square and median frequency analysis on the electromyography data, and to extract the maximum pressure value, average pressure value, average contact area and maximum contact area from the body pressure distribution data. It also performs correlation analysis and quadratic linear regression analysis on the processed electromyography data and body pressure distribution data. The hardness generation module is used to generate the target hardness of the cushion material based on the analysis results.

10. A storage medium storing a computer program, characterized in that, It includes computer program instructions, which, when executed by a computer device, perform the optimization method as described in any one of claims 1-7.

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