KR102581188B1 - Gait analysis method and gait analysis system thereof - Google Patents

Abstract

A gait analysis method according to an embodiment of the present disclosure includes the steps of acquiring gait information from one of the test subject's ground walking and treadmill walking from a sensor mounted on an insole worn by the test subject, and the other of the test subject's ground walking and treadmill walking. Obtaining walking information from and calculating walking regularity during overground walking and treadmill walking from the walking information obtained from overground walking and treadmill walking, wherein overground walking is defined as the test subject walking on the ground, Treadmill walking can be defined as the test subject walking on a treadmill.

Description

Gait analysis method and gait analysis system {GAIT ANALYSIS METHOD AND GAIT ANALYSIS SYSTEM THEREOF}

The present disclosure relates to a gait analysis method and a gait analysis system, and more specifically, to analyze the gait regularity and variability of ground walking and treadmill walking in patients such as cancer patients undergoing chemotherapy, patients with peripheral neuropathy, and diabetic complications. It relates to a gait analysis method and gait analysis system that can identify one or more of patients and elderly people.

Cancer patients receiving chemotherapy often experience functional limitations, including balance deficits, lower extremity muscle strength impairment, mobility limitations, and increased frequency of falls, and these limitations may also be present in patients with peripheral neuropathy, patients with diabetic complications, and the elderly. . Treatment toxicities can limit cancer treatment efficacy and affect patients' health-related quality of life. Impairment of peripheral senses (e.g., proprioception) due to chemotherapy may reduce gait regularity, and neurotoxic changes in the central and peripheral nervous system may affect autonomic activities, including walking. You can.

Additionally, because existing motion analysis is limited to only a few steps, it is not suitable for measuring the walking stability of the test subject.

Recognizing that gait instability is a key indicator of fall risk and mobility limitations in one or more of patients, such as cancer patients undergoing chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and the elderly, the present disclosure provides a method for evaluating the use of overground walking and treadmill walking. The purpose is to provide a gait analysis method and gait analysis system that can identify one or more of patients, such as cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and the elderly, by analyzing gait regularity and variability. do.

To achieve this purpose, the gait analysis method according to an embodiment of the present disclosure includes the steps of acquiring gait information from one of the ground walking and treadmill walking of the test subject from one or more sensors mounted on the insole worn by the test subject, A step of obtaining gait information from the other of overground walking and treadmill walking, and calculating gait regularity during overground walking and treadmill walking from the gait information obtained from overground walking and treadmill walking, wherein ground walking is performed by the test subject. It is defined as walking on the ground, and treadmill walking can be defined as the test subject walking on a treadmill.

In addition, a step of comparing the walking regularity calculated during on-ground walking with the walking regularity calculated during treadmill walking may be further included.

Additionally, the method may further include determining whether the walking regularity calculated during treadmill walking is greater than the walking regularity calculated during ground walking.

Additionally, gait regularity can be defined as the pressure difference between steps calculated by the absolute value of the difference in peak pressure between adjacent steps (│steps (peak (n)-peak (n-1))│).

Additionally, the method may further include calculating gait variability during overground walking and treadmill walking from gait information obtained from overground walking and treadmill walking.

Additionally, gait variability may be defined as a coefficient of variation calculated by dividing the standard deviation for one or more of the test subject's stride length and step swing time by the average.

In addition, the one or more sensors include one or more of a pressure sensor, an accelerometer, and a gyroscope disposed on the insole to form a predetermined pattern, and the walking information is detected from one or more of the pressure sensor, accelerometer, and gyroscope disposed on the insole. It can be calculated based on information.

Additionally, the gait information may include stride length, number of steps, step speed, step swing time, cadence, and distribution of pressure applied to the insole according to the test subject's steps.

A gait analysis system according to an embodiment of the present disclosure includes an insole provided in a shoe of a test subject, one or more sensors mounted on the insole to detect walking information of the test subject, a treadmill that guides the test subject's walking, and one or more sensors mounted on the insole. A gait analysis device includes a gait analysis device that receives gait information from the test subject and analyzes the gait information of the test subject, and when the test subject performs overground walking and treadmill walking, the gait analysis device receives gait information for both overground walking and treadmill walking from one or more sensors. The walking regularity during ground walking and treadmill walking is calculated from the walking information obtained from ground walking and treadmill walking. Ground walking is defined as the test subject walking on the ground, and treadmill walking is defined as the test subject walking on a treadmill. It can be defined as

In one embodiment of the present disclosure, there is also a computer-readable recording medium that is readable by a computer and stores program instructions operable by the computer, wherein when the program instructions are executed by a processor of the computer, the processor A computer-readable recording medium for performing a method according to one of the above-described gait analysis methods is provided.

The gait analysis method and gait analysis system according to embodiments of the present disclosure can identify one or more of patients, such as cancer patients receiving anticancer chemotherapy, peripheral neuropathy patients, patients with diabetes complications, and elderly people, and can identify patients receiving anticancer chemotherapy. It may provide preliminary data that can be used to develop efficient protocols that enable regular exercise or sensory stimulation-based rehabilitation for regular walking in cancer patients, patients with peripheral neuropathy, patients with diabetes complications, and the elderly. .

Figure 1 is a conceptual diagram of walking activity and performance according to disability in cancer patients receiving chemotherapy.
Figure 2 is a block diagram of the configuration of a gait analysis system according to an embodiment of the present disclosure.
Figure 3 is a conceptual diagram of an example of an insole.
FIG. 4 is a graph showing an example of pressure and acceleration detected by sensors provided in the insole shown in FIG. 3.
Figure 5 is a flowchart of the configuration of a gait analysis method according to an embodiment of the present disclosure.

In this specification, terms such as “unit,” “module,” “device,” “terminal,” “server,” or “system” are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device such as a mobile device or a PC that includes a CPU or other processor. Additionally, software driven by hardware may refer to a running process, object, executable, thread of execution, program, etc.

Hereinafter, embodiments of the gait analysis method and gait analysis system according to embodiments of the present invention will be described with reference to the drawings. The present invention has been described with reference to the embodiment shown in the drawings, but this is described as one embodiment, and the technical idea of the present invention and its core configuration and operation are not limited thereby.

Figure 1 is a conceptual diagram of walking activity and performance according to disability in cancer patients receiving chemotherapy.

Referring to Figure 1, gait activity is expressed in terms of walking speed, stride length, and swing/stance ratio, while gait instability is expressed as variability in spatiotemporal parameters such as walking speed and stride length, or movement parameters such as 'step-to-step pressure difference'. It appears as an irregularity. Studies have reported gait variability in patients with central nervous system disorders such as Parkinson's disease.

Patients, such as cancer patients receiving chemotherapy, may have problems with peripheral sensation and muscle weakness, which can lead to problems such as balance deficits, lower extremity muscle strength problems, limited mobility, and an increased frequency of falls. In the following, the present disclosure is described for cancer patients receiving chemotherapy. However, the gait analysis method and gait analysis system according to embodiments of the present disclosure are used to improve peripheral senses and The same can be applied to those suffering from muscle weakness problems.

For cancer patients receiving chemotherapy, gait variability and irregularity can be used to quantify changes in movement.

[Walking activity]

Stride length (cm) is defined as the distance between steps. Step count is determined based on acceleration peaks before and after heel strike using a Y-axis accelerometer (Accel Y) (see Figure 4). Cadence (steps/min) is calculated as the number of steps divided by the time (1/(stride time/60)). Speed (km/h) is calculated as stride length divided by step time.

[Gait Variability]

The coefficient of variation (CV) can be used as an indicator of gait variability, and the coefficient of variation is calculated as the standard deviation of the variable divided by the mean. For example, stride length variability and swing time variability can be determined.

[Gait regularity]

Gait regularity can be measured, for example, by the pressure difference between steps, where the pressure difference between steps is the absolute value of the difference in peak pressure between steps (│steps (peak (n)-peak (n-1))│) It is calculated as

Figure 2 is a block diagram of the configuration of a gait analysis system according to an embodiment of the present disclosure.

Referring to FIG. 2 , the gait analysis system 1 may include a gait analysis device 10, an insole 100, and a treadmill 200.

The gait analysis device 10 is a device for receiving and processing various information sensed from the insole 100 and/or the treadmill 200, and may be a device including, for example, a smartphone, tablet PC, PC, laptop, etc. You can.

The gait analysis device 10 may include a gait analysis unit 11, a control unit 12, a communication unit 13, an input unit 14, an output unit 15, and a memory 16.

The gait analysis unit 11 can obtain and analyze gait information of the test subject (cancer patient receiving chemotherapy and healthy control group). Gait information may include stride length, number of steps, step speed, step swing time, cadence, and distribution of pressure applied to the insole 100 according to the test subject's step. As will be described later, the gait analysis unit 11 may acquire the gait information of the examinee from the sensor unit 110 mounted on the insole 100 and analyze the gait information of the examinee through the Gilon gait analysis algorithm. For example, the gait analysis unit 11 may calculate gait activity, gait variability, gait regularity, etc. from the acquired gait information of the test subject.

The control unit 12 generally controls the operation of the gait analysis device 10. For example, the control unit 12 controls the communication unit 13 to receive information received from the insole 100, analyzes the information received from the insole 100 through the gait analysis unit 11, and processes to be performed subsequently. You can control them.

The control unit 12 includes at least one processor. The processor may be implemented as an array of multiple logic gates, or as a combination of a general-purpose microprocessor and a memory storing a program that can be executed on the microprocessor. Additionally, those skilled in the art can understand that the present embodiment may be implemented with other types of hardware.

The communication unit 13 may include one or more modules that enable the gait analysis device 10 to communicate with an external device through wired or wireless communication. For example, the external device may be an insole 100 worn by the test subject, or it may be a treadmill 200 that induces the test subject to walk.

The communication unit 13 may include an Internet module unit, a short-range communication module unit, etc. The Internet module part may refer to a module for Internet access. The Internet module unit may be built into or external to the gait analysis device 10. The short-range communication module part refers to a module for short-range communication. Short-distance communication technologies include Bluetooth, RFID (Radio Frequency Identification), IrDA (infrared Data Association), UWB (Ultra Wideband), and ZigBee.

For example, the communication unit 13 may receive the test subject's walking information from the insole 100 worn by the test subject, and may receive the test subject's walking information from the treadmill 200.

The input unit 14 is a device that receives input from a user using the gait analysis device 10 and may include, for example, a keyboard, mouse, microphone, touch sensor, etc.

The keyboard and mouse correspond to the input parts of a typical computer. A keyboard is a device that allows you to input the numbers, letters, and functions indicated by the keys by pressing them. The mouse is a device that moves the cursor and executes commands that appear on the display of the gait analysis device 10 according to the customer's operations (movement and clicking).

The microphone receives external sound signals in call mode, recording mode, voice recognition mode, etc., and processes them into electrical voice data. Various noise removal algorithms can be implemented in the microphone to remove noise generated in the process of receiving external acoustic signals. Additionally, various voice commands for driving the gait analysis device 10 and executing its functions may be input from the customer through the microphone.

The touch sensor may be configured to convert changes in pressure applied to a specific part of the display or capacitance occurring in a specific part of the display into an electrical input signal. The touch sensor can be configured to detect not only the location and area of the touch, but also the pressure at the time of touch. When there is a touch input to the touch sensor, a corresponding signal is sent to the touch controller. The touch controller processes the signal and then transmits the corresponding data to the control unit 12. The control unit 12 can determine which area of the display has been touched.

As described above, the touch sensor may be connected to the display, but the touch sensor may also be provided in other forms. For example, the touch sensor may have the form of a touch film, touch sheet, or touch pad.

The output unit 15 may include a display, an audio output unit, etc.

The display displays information processed by the gait analysis device 10. For example, a UI (User Interface) or GUI (Graphic User Interface) related to the gait analysis device 10 is displayed. Displays include liquid crystal display, thin film transistor-liquid crystal display, organic light-emitting diode, flexible display, 3D display, It may be at least one of transparent displays.

As described above, when the display and the touch sensor form a mutual layer structure, the display can be used as an input device in addition to an output device.

The audio output unit may output audio data. The sound output unit may output sound signals related to functions performed by the gait analysis device 10 (eg, call signal reception sound, message reception sound, etc.). This sound output unit may include a receiver, speaker, buzzer, etc.

The memory 16 is hardware that stores various data processed within the gait analysis device 10. The memory 16 includes data for analyzing gait information obtained from the gait analysis unit 11 and analyzed data, The control unit 12 may store processed data and data to be processed. The memory 16 is a variety of memory such as random access memory (RAM) such as dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and electrically erasable programmable read-only memory (EEPROM). It can be implemented in different types.

The memory 16 may store various information, such as a program for operating the gait analysis device 10, gait information obtained from the insole 100 and/or the treadmill 200, and a program for processing the obtained gait information. there is.

The insole 100 may be provided in the examinee's shoes, and contacts and supports the soles of the examinee's feet when the examinee wears the shoes. The insole 100 may include a sensor unit 110 and a communication unit 120. Hereinafter, with regard to the detailed configuration of the insole 100, detailed description to the extent that it overlaps with the configuration described in the gait analysis device 10 will be omitted.

The sensor unit 110 is mounted on the insole 100 and can detect walking information of the test subject. The sensor unit 110 may include one or more sensors 111, 112, 113, 114, and 115, as will be described later. The sensor unit 110 may include a measuring element that measures a change in a predetermined physical quantity and a processor that converts the change in the measured physical quantity into a digital value and obtains predetermined information from the change in the measured physical quantity. The sensor unit 110 may include a pressure sensor, an acceleration sensor, a gyroscope, and a body worn sensor.

The communication unit 120 can communicate with external devices. Here, the external device may be the gait analysis device 10 described above. For example, the communication unit 120 may transmit the gait information of the test subject detected by the sensor unit 110 to the gait analysis device 10.

The treadmill 200 is a device that induces the test subject to walk. Hereinafter, with regard to the detailed configuration of the treadmill 200, detailed description to the extent that it overlaps with the configuration described for the gait analysis device 10 and the insole 100 will be omitted.

The treadmill 200 is a device that induces a test subject to walk using a belt that rotates in an infinite orbit, and is also commonly called a treadmill.

The control unit 210 generally controls the operation of the treadmill 200. For example, the control unit 210 may control the driving speed of the belt of the treadmill 200. In addition, the control unit 210 controls the operation of the sensor unit 220 to detect the biometric information of the test subject in the treadmill 200 and controls the communication unit 230 to transmit the detected biometric information to the gait analysis device 10. You can.

The sensor unit 220 can detect biometric information of a test subject using the treadmill 200. For example, when the test subject touches a part of the treadmill 200, the test subject's biometric information such as electrocardiogram and heart rate can be detected. Additionally, the weight of a patient placed on the treadmill 200 may be measured by a weight sensor. Sensors that can be provided in the treadmill 200 are not limited to the types described above, and may further include various sensors as needed. For example, the sensor unit 220 further includes sensors capable of detecting various biometric information such as blood oxygen saturation (SpO2), intravascular blood pressure (IBP), diastolic blood pressure (NIBP), and brain wave (qCON) of the test subject. can do.

The communication unit 230 can communicate with external devices. Here, the external device may be the gait analysis device 10 described above. For example, the communication unit 230 may transmit the biometric information of the test subject detected by the sensor unit 220 to the gait analysis device 10.

Figure 3 is a conceptual diagram of an example of an insole, and Figure 4 is a graph showing an example of pressure and acceleration detected by sensors provided in the insole shown in Figure 3.

Referring to FIG. 3, the insole 100 may include one or more sensors 111, 112, 113, 114, and 115 that detect walking information of the test subject.

For example, one or more sensors include four pressure sensors (FSR, force-sensing resistors) (111, 112, 113, 114) and a three-axis acceleration sensor (115) disposed on the insole 100 to form a predetermined pattern. may include. However, the type of one or more sensors is not limited by the above. For example, the one or more sensors may be one or more of a pressure sensor, an acceleration sensor, and a gyroscope.

The pressure applied to the insole 100 by each part of the sole of the examinee's foot can be measured through the four pressure sensors 111, 112, 113, and 114. This allows quantitative analysis of the test subject's gait. For example, as shown in FIG. 3, the first pressure sensor (FSR 1, 111) measures the pressure near the test subject's thumb, and the second pressure sensor (FSR 2, 112) measures the pressure near the first metatarsal head (head). of first metatarsal bone, the third pressure sensor (FSR 3, 113) measures the pressure near the head of the fifth metatarsal bone, and the fourth pressure sensor (FSR 4, 114) ) measures the pressure near the heel.

Additionally, a change in the position of the insole 100 may be measured through the acceleration sensor 115. Accordingly, the walking information of the test subject can be detected.

Referring to FIG. 4, an example is shown in which the walking information of the test subject is detected by four pressure sensors 111, 112, 113, and 114 and an acceleration sensor 115. For example, when the stance phase begins in the examinee's steps, the pressure of the heel increases first and then the pressure of the thumb increases, and the change in pressure between each step can be measured. Additionally, the stance and swing stages of walking can be distinguished based on the three-axis acceleration values measured by the acceleration sensor 115.

Figure 5 is a flowchart of the configuration of a gait analysis method according to an embodiment of the present disclosure.

Referring to FIG. 5, the gait analysis device 10 acquires gait information during the ground walking (GW) of the test subject from one or more sensors provided in the insole 100 worn by the test subject (S1). In addition, the gait analysis device 10 acquires gait information during the treadmill walking (TW) of the test subject from one or more sensors provided in the insole 100 worn by the test subject (S2). However, the order of steps S1 and S2 is not limited to the order shown in FIG. 5. For example, step S1 of acquiring walking information during on-ground walking may be performed after step S2 of acquiring walking information during treadmill walking is performed. At this time, ground walking means that the test subject walks on the ground, and treadmill walking means that the test subject walks on a treadmill.

In the gait information acquisition step (S1) during ground walking and the gait information acquisition step (S2) during treadmill walking, each test subject stands up wearing the insole 100 equipped with a pressure sensor and an acceleration sensor. Subjects are allowed to practice walking twice before measurements are performed. For example, overground walking could be measured by having participants walk a 9 m walkway three times. Additionally, treadmill walking can be measured by participants walking on the treadmill 200 for 3 minutes at their most comfortable speed.

The gait analysis device 10 calculates gait regularity during overground walking and treadmill walking from gait information obtained from overground walking and treadmill walking (S3). As described above, gait regularity can be defined as the pressure difference between steps calculated by the absolute value of the difference in peak pressure between adjacent steps (│steps (peak (n)-peak (n-1))│) .

Next, the gait analysis device 10 compares the gait regularity calculated during overground walking with the gait regularity calculated during treadmill walking, and the gait regularity calculated during treadmill walking is greater than the gait regularity calculated during overground walking. You can determine whether it is large (S4).

If the gait regularity calculated during treadmill walking in step S4 is greater than the gait regularity calculated during ground walking, the gait analysis device 10 is used in cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and the elderly. One or more of the following, preferably, can be determined from the walking pattern of a cancer patient receiving chemotherapy (S5). In addition, in step S4, the gait analysis device 10 detects cases where the gait regularity calculated during treadmill walking is smaller than the gait regularity calculated during ground walking, such as cancer patients receiving chemotherapy, peripheral neuropathy patients, and diabetic complications patients. and the gait pattern of at least one of the elderly, preferably a cancer patient receiving chemotherapy (S6).

Cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and the elderly show more regular walking patterns during treadmill walking compared to overground walking. Therefore, gait irregularity during overground walking is a sensitive parameter to detect gait instability in one or more of the following: cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetic complications, and elderly people, preferably in cancer patients receiving chemotherapy. It can be.

Gait variability during the overground walking and the treadmill walking can be calculated from the gait information obtained from the overground walking and the treadmill walking (S7). As described above, gait variability may be defined as a coefficient of variation calculated by dividing the standard deviation for one or more of the test subject's stride length and gait swing time by the average.

However, the sequence of steps S3 to S7 is not limited to the sequence shown in FIG. 5. For example, after step S7 for calculating gait variability is performed, gait irregularity is calculated and compared to determine at least one of cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and elderly people, preferably anti-cancer patients. Steps S3 to S6 may be performed, which are determined by the gait pattern of a cancer patient receiving chemotherapy.

Hereinafter, gait analysis performed by the gait analysis method according to the present disclosure will be described. In statistical analysis, t-test and ^χ2 -test were used to compare clinical and demographic variables between groups and significantly different variables were controlled in subsequent analyses. Gait variables for overground walking and treadmill walking are compared using paired t-tests. The outcomes of the groups were examined using logistic regression and linear regression models. Linear regression coefficients and odds ratios (95% confidence intervals, CIs) were used to quantify the effect sizes of linear regression and logistic regression models, respectively. A value of p<0.05 was considered statistically significant. Data were analyzed using STATA software (ver. 14.0; Stata Corp., College Station, TX, USA). Results are expressed as mean ± standard deviation unless otherwise specified.

Eligibility criteria for cancer patients in this disclosure include cancer diagnosis, ongoing or recently completed (i.e., within the past 12 months) chemotherapy, and Eastern Cooperative Oncology Group (ECOG) performance score of 0-2. Exclusion criteria for all subjects were bone or brain metastases, as well as pre-existing conditions or medication use that may affect gait, balance, or muscle strength. All healthy controls correspond to people without tumor history.

The demographic characteristics of cancer patients receiving chemotherapy and healthy control participants are summarized in Table 1 below. The two groups were similar in body mass index (BMI) but different in other demographic characteristics.

Patient demographics and treatment characteristics Patients receiving chemotherapy healthy control group p-values* Gender (F/M) 21/1 8/16 < 0.001 Age (years) 55.4 ± 10.1 44.5 ± 11.0 0.001 Mass (kg) 55.8 ± 8.3 64.1 ± 13.7 0.018 Height (cm) 159.1 ± 5.2 166.5 ± 10.2 0.004 BMI (kg/m ² ) 22.1 ± 3.7 22.9 ± 3.0 0.413 Diabetes, number of patients (%) 1 (4.2%) 0 cancer breast cancer 14 (58.3%) uterine cancer 4 (16.7%) ovarian cancer 2 (8.3%) colon cancer 4 (16.7%) Cancer stage at diagnosis, number of patients (%) Ⅰ 7 (29.2%) Ⅱ 9 (37.5%) Ⅲ 4 (16.7%) Ⅳ 4 (16.7%) cancer chemotherapy Paclitaxel 11 (45.8%) Docetaxel 9 (37.5%) Others 4 (16.7%)

BMI, body mass index.

The absolute values of gait stability measures and spatiotemporal gait variables are summarized in Table 2 below. Compared with healthy controls, peak-to-peak pressure differences were larger and walking speed and stride length were lower in cancer patients during overground walking.

Comparison of gait parameters between cancer patients and healthy controls Patients receiving chemotherapy (n = 24) healthy control group
(n = 23) p-values Walking speed (km/h) 3.68 ± 0.64 4.05 ± 0.57 0.040 Stride length (cm) 113.77 ± 13.83 127.36 ± 21.02 0.012 Cadence (stride/min) 53.72 ± 5.07 54.76 ± 3.61 0.422 Swing time (s) 0.41 ± 0.04 0.40 ± 0.04 0.533 Stride length CV (%) 38.91 ± 16.31 57.32 ± 58.82 0.147 Swing Time CV (%) 23.24 ± 19.96 32.87 ± 32.29 0.223 ISPD FSR1 79.77 ± 48.62 56.87 ± 16.03 0.034 ISPD FSR2 73.65 ± 42.49 52.38 ± 13.37 0.024 ISPD FSR3 79.71 ± 28.09 63.55 ± 15.66 0.019 ISPDFSR4 48.62 ± 32.17 27.82 ± 9.94 0.004

CV, coefficient of variation; ISPD, inter-step pressure difference.

Table 3 below shows regression coefficients and 95% CIs for the comparison of gait parameters between cancer patients receiving chemotherapy and healthy controls during overground walking. Cancer patients receiving chemotherapy had significantly lower cadence over the heel region (β = -3.73, 95% CI, -6.96-0.49) and higher step-to-step pressure differential (β) after adjustment for age, sex, and BM. = 13.96, 95% CI, 3.64-24.27). Gait irregularity given by step-to-step pressure difference (indexed by cadence) is significantly greater in cancer patients (β = 16.84, 95% CI, 5.89-27.78) even after adjusting for gait activity.

Regression coefficients and 95% CI of gait parameters: comparison between cancer patients receiving chemotherapy and healthy controls during overground walking. Model 1 Model 2 Walking speed (km/h) 0.15 (-0.62 to 0.32) Stride length (cm) -2.49 (-16.12 to 11.14) Cadence (stride/min) -3.73 (-6.96 to 0.49) 0.80 (-0.23 to 1.82) swing time 0.02 (-0.01 to 0.04) Stride length CV (%) -0.24 (-0.58 to 0.11) Swing time CV (%) -0.11 (-0.32 to .09) ISPD FSR1 11.70 (-3.95 to 27.35) ISPD FSR2 10.90 (-2.57 to 24.36) ISPD FSR3 5.74 (-7.38 to 18.87) ISPDFSR4 13.96 (3.64 to 24.27) 16.84 (5.89 to 27.78)

Model 1 adjusted for age, gender, and BMI; Model 2 was further adjusted for key gait parameters in Model 1. ISPD, inter-step pressure difference.

Table 4 below shows the results of some cancer patients (n = 14) and healthy controls (n = 13) completing treadmill walking. In both groups, the average walking speed for treadmill walking was lower compared to overground walking due to the reduction in stride length. However, treadmill walking affected gait regularity differently in cancer patients and healthy participants. Irregular walking patterns during overground walking tend to become more regular during treadmill walking in cancer patients than in healthy controls.

Comparison of absolute values of the means of gait stability and spatiotemporal gait variables between overground walking (GW) and treadmill walking (TW) in cancer patients receiving chemotherapy and healthy controls. Patients receiving chemotherapy (n = 14) Healthy control group (n = 13) G.W. TW t p-value G.W. TW t p-value Walking speed (km/h) 3.60 ± 0.64 3.11 ± 0.78 3.522 0.003 4.17 ± 0.64 3.51 ± 0.45 3.709 0.004 Stride length (cm) 111.89 ± 14.58 101.58 ± 18.67 2.823 0.014 130.62 ± 24.41 111.06 ± 13.52 2.515 0.031 Cadence (stride/min) 53.59 ± 4.48 50.84 ± 5.07 2.315 0.038 55.07 ± 4.22 52.93 ± 4.91 1.679 0.124 Stride length CV (%) 41.08 ± 19.53 15.54 ± 9.39 8.395 < 0.001 64.42 ± 70.94 12.47 ± 2.66 2.453 0.034 Swing Time CV (%) 27.61 ± 24.62 8.42 ± 5.29 3.355 0.005 32.03 ± 33.38 5.46 ± 1.93 2.726 0.021 ISPD FSR1 70.14 ± 17.55 44.41 ± 18.98 4.016 0.002 39.89 ± 16.16 52.98 ± 13.07 2.073 0.065 ISPD FSR2 63.33 ± 18.55 38.96 ± 16.21 3.659 0.003 51.37 ± 12.77 40.29 ± 11.79 2.061 0.066 ISPD FSR3 73.29 ± 17.85 50.99 ± 21.48 3.216 0.007 53.41 ± 17.31 47.16 ± 18.22 0.935 0.374 ISPDFSR4 41.94 ± 15.24 28.34 ± 19.51 2.341 0.036 26.63 ± 10.51 14.42 ± 6.78 3.716 0.004

Absolute values are expressed as mean ± standard deviation. Bold indicates significant difference between GW and TW (paired t-test; p < 0.05). CV, coefficient of variation; ISPD, inter-step pressure difference.

The present disclosure compared the gait activity and stability of patients receiving anti-cancer chemotherapy with healthy controls. As a result, gait performance (e.g., walking speed and stride length, etc.) was lower and gait irregularity was higher in cancer patients receiving chemotherapy compared to healthy controls during overground walking. However, the peak-to-peak pressure difference between steps is greater during overground walking in cancer patients receiving chemotherapy compared with healthy controls. Cancer patients receiving chemotherapy show a more regular walking pattern during treadmill walking compared to overground walking. Therefore, gait irregularity during overground walking may be a sensitive parameter to detect instability in cancer patients undergoing chemotherapy.

In this disclosure, the pressure difference between steps was used as a parameter to evaluate gait regularity. An important dose-limiting side effect of anticancer chemotherapy agents is peripheral sensory impairment. Gait irregularities may occur due to sensory impairment in cancer patients receiving chemotherapy before decreased gait speed, reduced stride length, and increased TUG time due to motor impairment.

In this disclosure, gait parameters are grouped into regularity, stability and activity parameters, the gait parameters of activity and stability are distinguished, and the parameters are divided into volatility and regularity parameters. The present disclosure postulates that fluctuations in gait variability may be influenced by central regulatory centers and that gait regularity may be disrupted by impaired peripheral proprioception. Anticancer chemotherapy agents cause non-specific damage to nervous system cells, resulting in neurotoxicity. Neurotoxic changes cause disruption of specific channels in the sensory terminals of muscle proprioceptors, affecting sensory encoding and motor function. As a result, the reduction in perceptual information quality may further affect gait by promoting sensorimotor deficits.

Gait control may be modulated by physical constraints (e.g., treadmill walking) that reduce freedom of movement in cancer patients receiving chemotherapy. A more regular gait pattern was observed in cancer patients during treadmill walking, while healthy controls maintained a consistent gait pattern regardless of treadmill use. The improvement in the regularity of cancer patients' gait patterns during treadmill walking may be due to their high dependence on environmental conditions. Regulatory input from the environment can restore efficient and stable movement patterns. The use of regulatory input from the environment can overcome faulty irregular peripheral feedback. Treadmill walking can provide the stimulus needed to maintain a certain level of gait rhythm in a manner similar to external signals. These external signals can promote motor activity by effectively bypassing the internal signaling pathways through alternative pathways.

In the present disclosure, both walking stability and walking activity such as walking speed and stride length of cancer patients are reduced. Compared to healthy controls, gait speed and stride length are significantly reduced and TUG time is increased in cancer patients. Decreased balance is associated with pain, fatigue, and physical dysfunction during treatment. Treadmills can regulate and stabilize the walking patterns of cancer patients undergoing chemotherapy. The gait analysis method and gait analysis system according to the present disclosure can provide preliminary data that can be used to develop efficient protocols that enable regular exercise or sensory stimulation-based rehabilitation for regular walking in cancer patients.

The system or device above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices and components described in the embodiments include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), and a programmable logic unit (PLU). It may be implemented using one or more general-purpose or special-purpose computers, such as a logic unit, microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. The software and/or data may be embodied in any type of machine, component, physical device, computer storage medium or device for the purpose of being interpreted by or providing instructions or data to the processing device. there is. Software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. At this time, the medium may continuously store a computer-executable program, or temporarily store it for execution or download. In addition, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. , and ROM, RAM, flash memory, etc. may be configured to store program instructions. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

Although embodiments of the present invention have been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and may be implemented in various modifications without departing from the technical spirit of the present invention. there is. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. The scope of protection of the present invention should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be construed as being included in the scope of rights of the present invention.

1: Gait analysis system
10: Gait analysis device
11: Gait analysis unit
12: control unit
13: Department of Communications
14: input unit
15: output unit
16: memory
100: Insole
110: sensor unit
111, 112, 113, 114: pressure sensor
115: acceleration sensor
120: Department of Communications
200: Treadmill
210: control unit
220: sensor unit
230: Department of Communications

Claims (9)

Obtaining walking information from one of ground walking and treadmill walking of the test subject from one or more sensors mounted on an insole worn by the test subject;
Obtaining walking information from one of the test subject's ground walking and treadmill walking;
Calculating walking regularity during the overground walking and the treadmill walking from walking information obtained from the overground walking and the treadmill walking;
Comparing the walking regularity calculated during the overground walking with the walking regularity calculated during the treadmill walking;
determining whether the walking regularity calculated during the treadmill walking is greater than the walking regularity calculated during the ground walking; and
If the gait regularity calculated during the treadmill walking is greater than the gait regularity calculated during the ground walking, the gait pattern is at least one of cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and elderly people. Including the step of determining,
The ground walking is defined as the test subject walking on the ground, and the treadmill walking is defined as the test subject walking on a treadmill,
The gait regularity is defined as the pressure difference between steps calculated by the absolute value of the difference in peak pressure between adjacent steps (│steps (peak (n)-peak (n-1))│).
delete delete delete According to paragraph 1,
A gait analysis method further comprising calculating gait variability during the overground walking and the treadmill walking from gait information obtained from the overground walking and the treadmill walking.
According to clause 5,
The gait variability is defined as a coefficient of variation calculated by dividing the standard deviation for one or more of the test subject's stride length and gait swing time by the average.
According to paragraph 1,
The one or more sensors include one or more of a pressure sensor, an accelerometer, and a gyroscope arranged to form a predetermined pattern on the insole,
The gait information is calculated by information sensed from one or more of the pressure sensor, the accelerometer, and the gyroscope disposed on the insole.
In clause 7,
The gait information includes step length, number of steps, step speed, step swing time, cadence, and pressure distribution applied to the insole according to the test subject's step.
Insoles provided in the examinee's shoes;
One or more sensors mounted on the insole to detect walking information of the test subject;
A treadmill that induces the test subject to walk; and
A gait analysis device that receives the gait information from the one or more sensors mounted on the insole and analyzes the gait information of the test subject,
The gait analysis device,
When the test subject performs overground walking and treadmill walking, gait information for both the overground walking and the treadmill walking is obtained from the one or more sensors,
Calculating walking regularity during the overground walking and the treadmill walking from walking information obtained from the overground walking and the treadmill walking,
Compare the walking regularity calculated during the overground walking with the walking regularity calculated during the treadmill walking,
Determine whether the walking regularity calculated during the treadmill walking is greater than the walking regularity calculated during the ground walking,
If the gait regularity calculated during the treadmill walking is greater than the gait regularity calculated during the ground walking, the gait pattern is at least one of cancer patients receiving chemotherapy, patients with peripheral neuropathy, patients with diabetes complications, and elderly people. decide,
The ground walking is defined as the test subject walking on the ground, and the treadmill walking is defined as the test subject walking on a treadmill,
The gait regularity is defined as the pressure difference between steps calculated by the absolute value of the difference in peak pressure between adjacent steps (│steps (peak (n)-peak (n-1))│).

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