CN109470502B - Exoskeleton comfort evaluation device and method based on multiple sensors - Google Patents

Exoskeleton comfort evaluation device and method based on multiple sensors Download PDF

Info

Publication number
CN109470502B
CN109470502B CN201811278695.4A CN201811278695A CN109470502B CN 109470502 B CN109470502 B CN 109470502B CN 201811278695 A CN201811278695 A CN 201811278695A CN 109470502 B CN109470502 B CN 109470502B
Authority
CN
China
Prior art keywords
exoskeleton
pressure
comfort
stance
sensor
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811278695.4A
Other languages
Chinese (zh)
Other versions
CN109470502A (en
Inventor
李候
刘昊
吕鑫
李冠呈
王道臣
常远
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Machinery Equipment Research Institute
Original Assignee
Beijing Machinery Equipment Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Machinery Equipment Research Institute filed Critical Beijing Machinery Equipment Research Institute
Priority to CN201811278695.4A priority Critical patent/CN109470502B/en
Publication of CN109470502A publication Critical patent/CN109470502A/en
Application granted granted Critical
Publication of CN109470502B publication Critical patent/CN109470502B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass

Abstract

The invention discloses an exoskeleton comfort evaluation device and an exoskeleton comfort evaluation method based on multiple sensors, belongs to the field of wearing equipment comfort evaluation, solves the problems of no qualification and no quantification of exoskeleton comfort evaluation, and provides scientific basis for the future fast, standard and efficient exoskeleton comfort design. An exoskeleton comfort evaluation device based on multiple sensors comprises a computer, a Bluetooth assembly, a binding band, a pressure sensor and a myoelectric sensor; the bandage is used for simulating wearing equipment; the pressure sensor collects the pressure of the bandage on the human body and the pressure of the sole; the electromyographic sensor is in direct contact with a human body and is used for acquiring electromyographic signals; data collected by the pressure sensor and the myoelectricity sensor are transmitted to a computer through the Bluetooth assembly. The evaluation method of the exoskeleton comfort evaluation device based on the multiple sensors qualitatively and quantitatively evaluates the comfort of the exoskeleton through comprehensive analysis of wearing comfort, motion balance and muscle fatigue.

Description

Exoskeleton comfort evaluation device and method based on multiple sensors
Technical Field
The invention belongs to the technical field of wearing equipment comfort evaluation, and particularly relates to an exoskeleton comfort evaluation device and method based on multiple sensors.
Background
Wearable devices have gained widespread attention in technology and industry in recent years. From Google Glass to Jowbone bracelet to domestic intelligent hardware combining various software and hardware, wearable equipment has begun to leave laboratories, and becomes an indispensable science and technology electronic product in people's daily life.
The wearable exoskeleton equipment combines physical strength of a machine and intelligence of a person at the same time, can be worn by the person, and receives instructions to complete tasks far beyond personal ability. From the introduction of concepts to the increasing market share today, exoskeleton wearing devices have played a great role in the fields of science and technology, military, medical treatment, energy augmentation, etc.
Exoskeleton equipment is always human-centered, and in the set of system, a robot and a human are in a symbiotic relationship. People and machines are interacting with each other all the time, and in the process, people need to safely and comfortably coexist with the machines harmoniously to achieve the working purpose of wearing the equipment. How to make the user have good use experience in physiology when wearing the equipment is a key problem for designing and using the exoskeleton equipment.
Here we propose the concept of comfort to express this physiologically good experience. This means that we want to measure the concept of comfort by objective comfort assessment. In order to meet the increasingly intelligent and complex design requirements and the more diversified market requirements, a referable comfort evaluation standard is urgently needed to help designers design products more efficiently and reasonably. Meanwhile, the comfort evaluation system can also help a user to more accurately and more humanized demand to obtain wearable equipment matched with the user.
Disclosure of Invention
In view of the above analysis, the invention aims to provide an exoskeleton comfort evaluation device and an exoskeleton comfort evaluation method based on multiple sensors, which solve the problems of uncertainty and no quantification of exoskeleton comfort evaluation and provide scientific basis for the future fast, standard and efficient exoskeleton comfort design.
The purpose of the invention is mainly realized by the following technical scheme:
an exoskeleton comfort evaluation device based on multiple sensors comprises a computer, a Bluetooth assembly, a binding band, a pressure sensor and a myoelectric sensor;
the binding bands comprise an exoskeleton thigh rod binding band, an exoskeleton shank rod binding band and an exoskeleton waist binding band and are used for simulating wearing equipment;
the pressure sensors comprise thigh pressure sensors, shank pressure sensors, waist pressure sensors and sole pressure sensors, the thigh pressure sensors, the shank pressure sensors and the waist pressure sensors are used for collecting the pressure of the binding bands on a human body, and the sole pressure sensors are used for collecting the pressure of soles;
the electromyographic sensor is in direct contact with a human body and is used for acquiring electromyographic signals;
data collected by the pressure sensor and the myoelectricity sensor are transmitted to a computer through the Bluetooth assembly.
Furthermore, the thigh pressure sensor, the shank pressure sensor and the waist pressure sensor are fixed on one side, close to the human body, of the exoskeleton thigh rod binding band, the exoskeleton shank rod binding band and the exoskeleton waist binding band in a corresponding manner in a sewing mode.
Furthermore, eight myoelectric sensors are arranged and respectively distributed in the areas of the left biceps brachii, the right biceps brachii, the left triceps brachii, the right triceps brachii, the left gluteus maximus, the right gluteus maximus, the left gastrocnemius and the right gastrocnemius.
Furthermore, three plantar pressure sensors are arranged and respectively distributed in the toe, forefoot and heel areas.
An evaluation method of an exoskeleton comfort evaluation device based on multiple sensors is characterized by comprising the following steps:
s1, collecting and processing data of a thigh pressure sensor, a shank pressure sensor and a waist pressure sensor to obtain a wearing comfort level index P1；
S2, collecting and processing data of the plantar pressure sensor to obtain a wearing comfort level index P2；
S3, collecting and processing data of the electromyographic sensor to obtain a wearing moderate index P3；
S4, taking P1、P2、P3The average value of (a) obtains the comfort level of the comprehensive quantification of the wearable device.
Further, comfort index P1Is obtained by the following formula,
Figure BDA0001847594410000031
and F is the pressure generated by the bandage on the human body when the bandage is fixed on the human body, and data is acquired by the pressure sensor.
Further, comfort index P2Is obtained by the following formula,
P2＝0.5*100*Ssingle_stance+0.5*100*(1-ASI)
in the formula, Ssingle_stanceThe ratio of the left and right single-leg support time is shown, and the ASI is the index of the absolute symmetry of the left and right sides.
Further, Ssingle_stanceThe closer to 1, the higher the gait similarity of walking on both legs, and the better the gait balance; ssingle_stanceIs obtained by the following formula,
Figure BDA0001847594410000032
in the formula, Tsingle_stance_rightFor right leg single leg support time, Tsingle_stance_leftThe left leg is supported for one leg.
Further, the smaller the ASI value is, the similar walking gaits of the two legs are, and the better the gait balance is; the ASI is obtained from the following formula,
Figure BDA0001847594410000041
in the formula, Tsingle_stance_rightFor right leg single leg support time, Tsingle_stance_leftThe left leg is supported for one leg.
Further, comfort index P3Is obtained by the following formula,
Figure BDA0001847594410000042
in the formula, RMDFIs the median frequency slope value, R, in the electromyographic signalMNFIs the mean frequency slope value in the electromyographic signal.
Compared with the prior art, the invention has the following beneficial effects:
1) according to the exoskeleton comfort evaluation device based on the multiple sensors, the comfort index of the exoskeleton robot can be given qualitatively and quantitatively, a set of standard comfort evaluation system is established, and the blank of the exoskeleton comfort evaluation system is filled; the comfort of the sample can be comprehensively predicted before the exoskeleton is designed and manufactured for mass production, so that the production design and improvement can be guided.
2) The data are collected and processed through the matching of the binding band and the pressure sensor, and the comfort level index of the wearing equipment fixed through the binding band can be obtained quantitatively; the data of the plantar pressure sensor are collected and processed, and quantitative comfort indexes of the foot wearing equipment can be obtained; collecting and processing data of the electromyographic sensor to obtain a quantitative human muscle comfort level index; get P1、P2、P3The average value of (a) obtains the comfort level of the comprehensive quantification of the wearable device. The invention carries out comprehensive analysis on wearing comfort, motion balance and muscle fatigue of exoskeleton wearing equipment, and carries out accurate comprehensive evaluation on the exoskeleton comfort.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic view of an exoskeleton comfort assessment device;
FIG. 2 is a flow chart of exoskeleton comfort level assessment indicator quantification;
FIG. 3 is a graph of pressure values in the toe, forefoot and heel regions during walking;
FIG. 4 is a flow chart of electromyographic signal preprocessing;
FIG. 5 left foot pressure chart of example 1;
fig. 6 pressure diagram of the right foot of example 1.
Reference numerals:
1-a computer; 2-a bluetooth component; 3-exoskeleton thigh bar strap; 4-exoskeleton shank rod strap; 5-exoskeleton waist strap; 6-thigh pressure sensor; 7-calf pressure sensor; 8-lumbar pressure sensor; 9-plantar pressure sensors; 10-electromyographic sensors; 11-heel; 12-forefoot; 13-toe.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
An exoskeleton comfort evaluation device based on multiple sensors is shown in fig. 1 and comprises a computer 1, a Bluetooth assembly 2, an exoskeleton thigh rod binding band 3, an exoskeleton shank rod binding band 4, an exoskeleton waist binding band 5, a thigh pressure sensor 6, a shank pressure sensor 7, a waist pressure sensor 8, a sole pressure sensor 9 and a myoelectricity sensor 10.
The thigh pressure sensor 6, the shank pressure sensor 7 and the waist pressure sensor 8 are fixed on one side, close to the human body, of the exoskeleton thigh rod binding band 3, the exoskeleton shank rod binding band 4 and the exoskeleton waist binding band 5 in a corresponding manner in a sewing manner; the sole pressure sensors are made into insole soft board patterns and are arranged in the shoes, and the three sole pressure sensors are respectively distributed in the areas of toes 13, forefeet 12 and heels 11; the eight myoelectric sensors are respectively distributed in the areas of the left biceps brachii, the right biceps brachii, the left triceps brachii, the right triceps brachii, the left gluteus maximus, the right gluteus maximus, the left gastrocnemius and the right gastrocnemius.
The thigh pressure sensor, the shank pressure sensor and the waist pressure sensor are used for collecting the pressure of the bandage on a human body, and the sole pressure sensor is used for collecting the pressure of a sole; the electromyographic sensor is in direct contact with a human body and collects electromyographic signals; the data collected by the sensors 6, 7, 8, 9 and 10 are transmitted to a computer through the Bluetooth module.
An evaluation method of an exoskeleton comfort evaluation device based on multiple sensors is shown in fig. 2.
S1, collecting and processing data of a thigh pressure sensor, a shank pressure sensor and a waist pressure sensor to obtain a wearing comfort level index P1；
The thigh pressure sensor 6, the shank pressure sensor 7 and the waist pressure sensor 8 measure the pressure value F given to the human body by the bandage, and the wearing comfort level index P is obtained according to the algorithm1The comfort of the exoskeleton wearing, which is fixed by the binding bands, can be reflected by this value. The human body and the exoskeleton are fixedly interacted through the binding bands, the tightness degree of the binding bands directly influences the effect of a human body interaction system, the binding bands are too loose, and the human body and the exoskeleton cannot be interacted; if the binding is too tight, the wearing comfort is affected. It is necessary to select a binding pressure range that is both practical and comfortable. Under normal conditions, the binding belt is in contact with human muscle, when the pressure value is less than 10N, the binding belt is loose, and the up-and-down sliding is obvious; when the pressure value is larger than 30N, the binding feeling is generated, so that the selected pressure value range is judged to be comfortable between 10 and 30N, the score is 100, and the scoring rule is as follows:
Figure BDA0001847594410000071
when more than 1 piece of wearing equipment fixed through the binding band is arranged, the wearing comfort degree is obtained through the method respectively, and then the average value is taken to obtain the wearing comfort degree index P1。
S2, collecting and processing data of the plantar pressure sensor to obtain a wearing comfort level index P2。
After the exoskeleton equipment is worn, the change of pressure on a place contacting with the wearable equipment can be caused, the walking balance of a human body can be influenced, and the change of pressure is also an index for evaluating the comfort level.
The plantar pressure sensor 9 measures the plantar pressure value of the human body and obtains the wearing comfort index P according to an algorithm2The walking balance of the exoskeleton can be judged according to the value; the sole pressure sensors are three in number and are respectively distributed in the toe area, the forefoot area and the heel area.
FIG. 3 is a partial enlarged view of the pressure change curves in the three regions of the heel, forefoot and toes during two complete gait cycles, FheelPressure in the heel area, FsolePressure in the forefoot region, FtoeIs the pressure in the toe area. As can be seen from fig. 2, at the beginning of the gait cycle, the heel touches down first, the heel pressure increases rapidly, then the forefoot pressure and the toe pressure increase in turn, the heel pressure, the forefoot pressure and the toe pressure decrease rapidly in turn when the sole lifts off, and the swing phase is entered when the toe pressure decreases to a minimum.
In a gait cycle, the heel pressure and the toe pressure are in a single wave peak state, and the ascending stage and the descending stage are in monotone curves. Setting a threshold Fheel_limitWhen heel pressure is less than Fheel_limitThe state becomes larger than Fheel_limitThe time of the state, the key frame T for judging heel landing is extractedheel(ii) a Setting a threshold Ftoe_limitWhen the toe pressure is higher than Ftoe_limitThe state becomes less than Ftoe_limitThe time of the state is extracted as the time T for judging the toe offtoe。
Suppose that the right heel touches the ground at the moment Theel_rightThe time of leaving the ground of the right toe is Ttoe_rightThe landing time of the left heel is Theel_leftThe toe-off time of the left foot is Ttoe_leftThen, it is known that in the ith gait cycle, the right leg single leg support time T is obtained as the start of one gait cycle from the right heel landingsingle_stance_rightAnd left leg single leg support time Tsingle_stance_leftThe calculation formulas are respectively as follows.
Tsingle_stance_right(i)＝Theel_left(i+1)-Ttoe_left(i) (II)
Tsingle_stance_left(i)＝Theel_right(i+1)-Ttoe_right(i) (III)
Plantar pressure index 1: ratio S of left-right single-leg supporting timesingle_stanceThe formula is shown as follows, wherein Tsingle_stance_left、Tsingle_stance_rightRespectively, the left and right single-leg support times. Ssingle_stanceThe closer to 1, the higher the gait similarity of the two-leg walking, and the better the gait balance. T issingle_stance_left、Tsingle_stance_rightWhen they are completely equal, Ssingle_stanceEqual to 1, the walking gaits of the two legs are completely the same, and the gaits are balanced best.
Figure BDA0001847594410000081
Plantar pressure index 2: the left and right absolute symmetry indexes ASI, shown below, where T issingle_stance_left、Tsingle_stance_rightRespectively, the left and right single-leg support times. The smaller the ASI value is, the walking gait of both legs is similar, the better the gait balance is, generally, the ASI value is considered to be<10% can indicate better gait balance.
Figure BDA0001847594410000082
The wearing comfort index P2 is full score of 100, and the calculation formula is as follows:
P2＝0.5*100*Ssingle_stance+0.5*100*(1-ASI) (VI)
s3, collecting and processing data of the electromyographic sensor to obtain a wearing moderate index P3；
The wearing equipment can also affect the muscle fatigue of the body, which is also an index P for evaluating the comfort3。
Firstly, the electromyographic signals collected by the electromyographic sensor 10 are preprocessed, and as shown in fig. 4, the whole process of electromyographic signal preprocessing is shown.
1) Deaveraging
In the process of collecting the electromyographic signals, the recording instrument can cause drift to the electromyographic signals with the original zero mean value. The removal method comprises the following steps:
mean value removal of surface electromyography time series:
s1(n)＝s(n)-mean(s(n)) (VII)
2) then passed through a high pass filter with a cut-off frequency of 10-20 Hz. In this embodiment, a non-causal Butterworth (Butterworth) high-pass filter with a cut-off frequency of 16Hz, a 7 th order, and zero phase change is selected.
s2(n)＝Filterhighpass,cutoff＝16(s1(n)) (VIII)
Wherein: s (n) is a discrete time sequence of the raw electromyographic signal,
s1(n) is the electromyographic signal discrete time sequence after mean value removal,
s2and (n) is a discrete time sequence of the filtered electromyographic signals.
The high pass filter can remove at least 3 different sources of noise. They are respectively signal drifts caused by the recording instrument; motion noise; partial electrocardio interference.
In the process of collecting myoelectricity, the frequency of noise generated by the relative movement of muscles and joints, the change of joint angles and the relative movement of electrodes to detected muscle fibers is generally lower than 5-10 Hz, and almost most of the frequency components of myoelectricity signals are above the range. Therefore, the high-pass filter can remove most of the motion noise in the electromyographic signals with little influence on the useful electromyographic signals. In addition, the frequency component of 0-20Hz in the electromyographic signal is unstable because the burst frequency of the movement unit has non-stationary characteristics, and in most cases, the burst frequency is in this frequency band. Due to the instability of these components in the electromyographic signals, they should be treated as noise to be removed.
3) Removing power frequency interference
The main component of the environmental noise is power frequency interference, and the frequency component of the environmental noise is 50 Hz. The power frequency interference has great influence on the electromyographic signals, sometimes the amplitude of the power frequency interference is much larger than that of the electromyographic signals, the amplitude can reach 1-3 orders of magnitude, and a 50Hz wave trap is required to be used for removing the power frequency interference.
Secondly, feature extraction is carried out on the preprocessed electromyographic signals. The frequency domain analysis methods commonly used include 2 kinds of median frequency (MDF) and mean power frequency (MNF).
The median frequency (MDF) is the frequency that divides the power spectrum into upper and lower equal area regions, and is defined as:
Figure BDA0001847594410000101
the mean frequency (MNF) is defined as
Figure BDA0001847594410000102
In the formula: p (f) is a power spectral density function of the signal, and P (f) is estimated by a classical power spectrum estimation technology based on Fourier analysis; f. of0Is the upper frequency of the power spectral density, i.e. half the sampling frequency.
The mean and median frequencies are generally considered to be stable indicators of muscle fatigue, and exhibit similar time courses during voluntary or electrically induced muscle contraction: that is, as the degree of fatigue increases, the mean frequency and the median frequency of the energy spectrum of the electromyographic signal decrease. The median frequency and the average power frequency are slowly increased in the normal movement of the human body, and if the muscles are bound and the muscles are moved, the increasing rates of the two frequencies become faster. The slope of the two frequencies can reflect the change in the rate of increase of the two frequencies, RMDF、RMNFRepresenting the slope value of the median frequency and the slope value of the average frequency respectively, selecting two frequency slope thresholds as 0.8 and 0.6 respectively through experiments, judging that the part is uncomfortable if the frequency slope thresholds exceed the threshold, and judging that the wearing comfort index P is not comfortable3The full score is 100, and the specific calculation formula is as follows:
Figure BDA0001847594410000111
the myoelectric sensor is provided with eight myoelectric sensors, comfort levels of eight parts are obtained by the method respectively, and then an average value is taken to obtain a wearing comfort level index P3。
S4, taking P1、P2、P3The average value of (A) obtains the wearable device healdsComfort of the desired amount.
Finally, the exoskeleton comfort level index P is used as a basis for judging the exoskeleton comfort level, as shown in the following formula:
P＝(P1+P2+P3)/3 (XII)
and judging that the exoskeleton comfort index P is comfortable when the comprehensive score is more than 80 minutes.
Example 1
Wearing comfort index P1
Experimenter bandage pressure data: left thigh strap pressure: 20N; left calf strap pressure: 27N; right thigh strap pressure: 37N; pressure of the strap of the right calf: 25N; waist belt pressure: 40N.
P1＝(PLeft thigh+PLeft shank+PRight thigh+PRight crus+PWaist part)/5
＝{100+100+[100-3*(37-30)]+100+[100-3*(40-30)]}/5
＝100+100+71+100+70＝88.2
Wearing comfort index P2
As can be seen from fig. 5 and 6:
Theel_right(i+1)＝{1.061、2.15、3.45、4.81}
Ttoe_right(i)＝{0.61、1.73、2.9、4.4}
Theel_left(i+1)＝{1.42、2.55、3.81、5.12}
Ttoe_left(i)＝{0.96、2.04、3.31、4.59}
Tsingle_stance_left(i)＝Theel_right(i+1)-Ttoe_right(i) t is obtained by averaging {0.45, 0.42, 0.55, 0.41}single_stance_leftIs 0.4575.
Tsingle_stance_right(i)＝Theel_left(i+1)-Ttoe_left(i) T is obtained by averaging {0.5, 0.46, 0.50, 0.53}, where T is obtainedsingle_stance_rightIs 0.4975.
Tsingle_stance_left＜Tsingle_stance_rightSo that Ssingle_stance＝0.4575/0.4975＝0.92。
Figure BDA0001847594410000121
The ratio S of the supporting time of the left and right single legs is calculatedsingle_stanceThe average value is 0.92; the left and right absolute symmetry index ASI value is 8.4%.
P2＝0.5*100*Ssingle_stance+0.5*100*(1-ASI)
＝0.5*100*0.92+0.5*100*(1-0.084)＝46+45.8＝91.8
Wearing comfort index P3
Myoelectric data of experimenters: left biceps brachii RMNF、RMNF0.6 and 0.3 respectively; right biceps brachii muscle RMNF、RMNF0.7 and 0.5 respectively; left brachial triceps RMNF、RMNF0.9 and 0.4 respectively; right brachial triceps muscle RMNF、RMNF1.2 and 0.6 respectively; left gluteus maximus RMNF、RMNF0.9 and 0.7 respectively; right gluteus maximus RMNF、RMNF0.75 and 0.71, respectively; left gastrocnemius RMNF、RMNF0.68 and 0.46, respectively; right gastrocnemius muscle RMNF、RMNF1.1 and 0.4 respectively.
P3＝(PLeft biceps brachii+PRight biceps brachii+PLeft brachial triceps+PRight brachial triceps muscle+PLeft gluteus maximus+PRight gluteus maximus+
PLeft gastrocnemius+PRight gastrocnemius muscle)/8＝{100+100+[0.5*100*(1.8-0.9)+50]+
[0.5*100*(1.8-1.2)+50]+[0.5*100*(1.8-0.9)+0.5*100*
(1.6-0.7)]+[0.5*100*(1.6-0.71)+50]+100+[0.5*100*(1.8-1.1)
+50]}/8＝(100+100+95+80+90+94.5+100+85)/8＝93.0625
Exoskeleton comfort index P ═ P (P)1+P2+P3) The total score of (88.2+91.8+ 93.0625)/3-91.02 is greater than 80, and the skeletal equipment has better comfort.
The invention provides an exoskeleton comfort evaluation device based on multiple sensors, which can qualitatively and quantitatively give comfort indexes of an exoskeleton robot, establish a set of standard comfort evaluation system and fill the blank of the exoskeleton comfort evaluation system; a comprehensive prediction of comfort can be made prior to designing and manufacturing the exoskeleton, thereby guiding the production design.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (4)

1. The evaluation method of the exoskeleton comfort evaluation device based on the multiple sensors is characterized in that the exoskeleton comfort evaluation device based on the multiple sensors comprises a computer, a Bluetooth component, a binding band, a pressure sensor and a myoelectric sensor;
the binding bands comprise an exoskeleton thigh rod binding band, an exoskeleton shank rod binding band and an exoskeleton waist binding band and are used for simulating wearing equipment;
the pressure sensors comprise thigh pressure sensors, shank pressure sensors, waist pressure sensors and sole pressure sensors, the thigh pressure sensors, the shank pressure sensors and the waist pressure sensors are used for collecting the pressure of the binding bands on a human body, and the sole pressure sensors are used for collecting the pressure of soles;
the electromyographic sensor is in direct contact with a human body and is used for acquiring an electromyographic signal;
the data collected by the pressure sensor and the myoelectricity sensor are transmitted to a computer through a Bluetooth assembly;
the evaluation method comprises the following steps:
s1, collecting and processing data of a thigh pressure sensor, a shank pressure sensor and a waist pressure sensor to obtain a wearing comfort level index P1；
Selecting a pressure value rangeThe comfort level is judged to be comfortable within the range of 10-30N, the full score is 100 points, and the comfort level index P1Is obtained by the following formula,
Figure FDA0002493471340000011
when more than 1 piece of wearing equipment fixed through the binding band is arranged, the wearing comfort degree is obtained through the method respectively, and then the average value is taken to obtain the wearing comfort degree index P1(ii) a F is the pressure generated by the bandage on the human body when the bandage is fixed on the human body, and data are collected by the pressure sensor;
s2, collecting and processing data of the plantar pressure sensor to obtain a wearing comfort level index P2；
The plantar pressure sensor measures the plantar pressure value of the human body, and the wearing comfort level index P is obtained according to the algorithm2，
Right leg single leg support time Tsingle_stance_rightAnd left leg single leg support time Tsingle_stance_leftThe calculation formulas are respectively as follows:
Tsingle_stance_right(i)＝Theel_left(i+1)-Ttoe_left(i) (II)
Tsingle_stance_left(i)＝Theel_right(i+1)-Ttoe_right(i) (III)
suppose that the right heel touches the ground at the moment Theel_rightThe time of leaving the ground of the right toe is Ttoe_rightThe landing time of the left heel is Theel_leftThe toe-off time of the left foot is Ttoe_left；
Plantar pressure index 1: ratio S of left-right single-leg supporting timesingle_stanceThe formula is shown below, Ssingle_stanceThe closer to 1, the higher the gait similarity of walking on both legs is, and the better the gait balance is; t issingle_stance_left、Tsingle_stance_rightWhen they are completely equal, Ssingle_stanceThe walking gait is equal to 1, and the walking gait of the two legs is completely the same, so the gait balance is the best;
Figure FDA0002493471340000021
plantar pressure index 2: the absolute symmetry indexes of the left side and the right side are ASI, the formula is shown as follows, the smaller the ASI value is, the walking gait of the two legs is similar, the better the gait balance is, and generally, the gait balance can be represented to be better if the ASI is less than 10%;
Figure FDA0002493471340000022
the wearing comfort index P2 is full score of 100, and the calculation formula is as follows:
P2＝0.5*100*Ssingle_stance+0.5*100*(1-ASI) (VI)
s3, collecting and processing data of the electromyographic sensor to obtain a wearing moderate index P3；
Wearing comfort index P3The full score is 100, and the specific calculation formula is as follows:
Figure FDA0002493471340000031
in the formula, RMDFIs the median frequency slope value, R, in the electromyographic signalMNFIs the mean frequency slope value in the electromyographic signal; eight myoelectric sensors are arranged, comfort levels of eight parts are obtained by the method respectively, and then the average value is taken to obtain a wearing comfort level index P3；
S4, taking P1、P2、P3The average value of (a) obtains the comfort level of the comprehensive quantification of the wearable device.
2. The method of evaluating a multi-sensor based exoskeleton comfort evaluation device of claim 1, wherein the thigh pressure sensor, the shank pressure sensor and the waist pressure sensor are fixed on the sides of the exoskeleton thigh rod strap, the exoskeleton shank rod strap and the exoskeleton waist strap close to the human body by sewing.
3. The method of claim 1, wherein eight electromyographic sensors are distributed in the region of the left biceps brachii, right biceps brachii, left triceps brachii, right triceps brachii, left gluteus maximus, right gluteus maximus, left gastrocnemius, and right gastrocnemius, respectively.
4. An assessment method for a multi-sensor based exoskeleton comfort assessment device according to any one of claims 1 to 3 wherein there are three said plantar pressure sensors distributed in the toe, forefoot and heel areas respectively.
CN201811278695.4A 2018-10-30 2018-10-30 Exoskeleton comfort evaluation device and method based on multiple sensors Active CN109470502B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811278695.4A CN109470502B (en) 2018-10-30 2018-10-30 Exoskeleton comfort evaluation device and method based on multiple sensors

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811278695.4A CN109470502B (en) 2018-10-30 2018-10-30 Exoskeleton comfort evaluation device and method based on multiple sensors

Publications (2)

Publication Number Publication Date
CN109470502A CN109470502A (en) 2019-03-15
CN109470502B true CN109470502B (en) 2020-07-10

Family

ID=65666462

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811278695.4A Active CN109470502B (en) 2018-10-30 2018-10-30 Exoskeleton comfort evaluation device and method based on multiple sensors

Country Status (1)

Country Link
CN (1) CN109470502B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111110519B (en) * 2019-12-25 2022-06-24 南京理工大学 Multi-sensing intelligent wearable lower limb exoskeleton robot
CN113063411A (en) * 2020-06-29 2021-07-02 河北工业大学 Exoskeleton evaluation system and method of use thereof
CN113520688A (en) * 2021-06-17 2021-10-22 四川护理职业学院 Intelligent ankle and foot correction device and method for children with cerebral palsy

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8140154B2 (en) * 2007-06-13 2012-03-20 Zoll Medical Corporation Wearable medical treatment device
CN101980228A (en) * 2010-09-01 2011-02-23 张辉 Human body information monitoring and processing system and method
US9345414B1 (en) * 2013-09-25 2016-05-24 Bardy Diagnostics, Inc. Method for providing dynamic gain over electrocardiographic data with the aid of a digital computer
CN204033339U (en) * 2014-06-09 2014-12-24 鸿讃科技有限公司 Wearable type electronics sensing apparatus
CN104082905B (en) * 2014-06-18 2016-02-17 杭州华亭科技有限公司 Multifunctional intellectual shoe-pad and gait similarity detection method
CN105286804B (en) * 2015-12-04 2018-11-30 重庆大学 A kind of wearable knee climbs physiology of exercise parameter detection device
CN106037753B (en) * 2016-07-06 2019-08-13 电子科技大学 A kind of wearable data collection system and its method based on multi-sensor fusion
NL2017506B1 (en) * 2016-09-21 2018-03-29 Bambi Belt B V Wearable device, method and system for monitoring one or more vital signs of a human body.
CN106648089A (en) * 2016-12-14 2017-05-10 中国电子产品可靠性与环境试验研究所 Grade evaluation method and system for intelligent wearable product
CN107296306A (en) * 2017-06-21 2017-10-27 北京机械设备研究所 A kind of portable cold storage individual temperature barrier
CN107788991A (en) * 2017-10-26 2018-03-13 复旦大学 Wearable lower limb rehabilitation assessment system
CN107874763A (en) * 2017-12-11 2018-04-06 无锡智开医疗机器人有限公司 A kind of lower limb data collecting system

Also Published As

Publication number Publication date
CN109470502A (en) 2019-03-15

Similar Documents

Publication Publication Date Title
Pizzolato et al. CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks
CN109470502B (en) Exoskeleton comfort evaluation device and method based on multiple sensors
Schwartz et al. The effect of walking speed on the gait of typically developing children
Ivanenko et al. Temporal components of the motor patterns expressed by the human spinal cord reflect foot kinematics
Lee et al. Biomechanics of overground vs. treadmill walking in healthy individuals
Yungher et al. Surface muscle pressure as a measure of active and passive behavior of muscles during gait
Miller et al. Optimal footfall patterns for cost minimization in running
Gandolla et al. Automatic setting procedure for exoskeleton-assisted overground gait: proof of concept on stroke population
Shin et al. EMG activity of low back extensor muscles during cyclic flexion/extension
Gueugnon et al. Age-related adaptations of lower limb intersegmental coordination during walking
Arones et al. Musculoskeletal model personalization affects metabolic cost estimates for walking
Chen et al. Estimating muscle forces and knee joint torque using surface electromyography: a musculoskeletal biomechanical model
Andrade et al. Functional data analyses for the assessment of joint power profiles during gait of stroke subjects
Luis et al. Evaluation of musculoskeletal models, scaling methods, and performance criteria for estimating muscle excitations and fiber lengths across walking speeds
Dinovitzer et al. Accurate real-time joint torque estimation for dynamic prediction of human locomotion
Deschamps et al. A novel approach for the detection and exploration of joint coupling patterns in the lower limb kinetic chain
Jeleń et al. Expressing gait-line symmetry in able-bodied gait
Tong et al. BP-AR-based human joint angle estimation using multi-channel sEMG
US20230330835A1 (en) System and process for estimation of gait phase for use with a wearable robot
Wibawa et al. A validation study on muscle activity prediction of a lower limb musculoskeletal model using EMG during normal walking
Hashish et al. The influence of a bout of exertion on novice barefoot running dynamics
KR20170053353A (en) System for evaluating functionality of footwear
Cruz et al. Joint gait kinematic and kinetic analysis using inertial measurement units and plantar pressure sensor system
Vellucci et al. A need for speed: Objectively identifying full-body kinematic and neuromuscular features associated with faster sprint velocities
Atkinson Computational modelling techniques to determine patellofemoral joint loads.

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant