CN113040791A - Vibration trigger equipment, finger lifting device and touch finger motion rehabilitation system - Google Patents
Vibration trigger equipment, finger lifting device and touch finger motion rehabilitation system Download PDFInfo
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- CN113040791A CN113040791A CN202110260917.5A CN202110260917A CN113040791A CN 113040791 A CN113040791 A CN 113040791A CN 202110260917 A CN202110260917 A CN 202110260917A CN 113040791 A CN113040791 A CN 113040791A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H1/00—Apparatus for passive exercising; Vibrating apparatus ; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
- A61H1/02—Stretching or bending or torsioning apparatus for exercising
- A61H1/0274—Stretching or bending or torsioning apparatus for exercising for the upper limbs
- A61H1/0285—Hand
- A61H1/0288—Fingers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2205/00—Devices for specific parts of the body
- A61H2205/06—Arms
- A61H2205/065—Hands
- A61H2205/067—Fingers
Abstract
The invention relates to a vibration trigger device, a finger lifting device and a touch finger motion rehabilitation system. The vibration trigger device comprises a P300 inducing device for inducing a P300 signal related to the sense of touch by generating vibrations using a micromotor, wherein said device is divided into 2 sets of 5 vibration units each, and in use, 10 vibration units are fixed in sequence on 10 fingers of a user, respectively. The finger lifting device is provided with 5 lifting brackets which are respectively connected with 5 fingers through respective stretching devices, wherein the lifting brackets are used for lifting the fingers of a user upwards, and the finger lifting device can control a certain finger to do lifting action according to a command sent by an upper computer.
Description
The application is a divisional application entitled "system and method for recovering motion of tactile finger based on brain-computer interface" filed 2016, 25/11/2016 and application number 201611061234.2.
Technical Field
The invention belongs to the technical field of motion rehabilitation, and relates to a motion rehabilitation system, in particular to a vibration trigger device, a finger lifting device and a touch finger motion rehabilitation system based on a brain-computer interface.
Background
The auxiliary device relates to Brain-Computer Interface (Brain Computer Interface) technology. The technology was drawn open by the study of Jacques Vidal in the 1970 s, and the following definitions were made at the first international conference on brain-computer interface in 1999: the brain-computer interface is a communication system (translation) that does not rely on the normal output pathway consisting of peripheral nerves and muscles, and in short, controls peripheral devices by mind. The brain-computer interface of today usually has the following components, visual induction device (optional), brain activity signal extraction device, signal processing device, peripheral controlled device.
The invention is applied to a brain-computer interface based on P300 potential. The P300 Potential is one of Event Related Potentials (ERP). Approximately 300ms after the human being is stimulated by the external stimulus, positive potential fluctuation is generated in the relevant area of the brain. The potential can be evoked by various sensory stimuli such as visual, auditory, tactile, etc. The stimulus needs to be a small probability event that if the stimulus is too frequent, it will affect the quality of P300. A brain-machine interface typewriter system based on visual P300 was first proposed in 1988. In this type of brain-computer interface, the pattern of visual stimuli, the flashing sequence of stimuli, and the interface layout all have an effect on the performance of the system.
Motor imagery is also a common paradigm in brain-computer interfaces, and when a person imagines that the person operates a certain limb to move, (the limb does not really act), matched electrical activity is detected in the relevant area of the brain, and the limb imagined by the user is judged by detecting brain waves.
The brain activity signals commonly used are eeg (electroencephalography), which is a non-invasive way of collecting outside the brain scalp via electrodes. The acquisition mode has advantages in signal resolution, cost and practicality.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the vibration trigger equipment, the finger lifting device and the touch-sensing finger motion rehabilitation system comprising the vibration trigger equipment and the finger lifting device are provided, and a brain-computer interface technology is used, so that a user can directly control the lifting of the finger through imagination; the intuitive design is beneficial to the user to participate in the design better; the brain is helped to form a control loop, and the rapid recovery of the brain is helped.
According to a first aspect of the present invention there is provided a vibration trigger device comprising: a P300 induction device for inducing a P300 signal associated with the sense of touch using a micromotor to generate vibrations, wherein said device is divided into 2 sets of 5 vibration units each, and in use, 10 vibration units are affixed to 10 fingers of a user in sequence.
According to a second aspect of the present invention, there is provided a finger lifting device, wherein the finger lifting device has 5 lifting brackets, each of which is connected to 5 fingers through a respective stretching device, wherein the lifting brackets are used to lift up a finger of a user, and the finger lifting device controls a certain finger to perform a lifting action according to a command sent by an upper computer.
According to a third aspect of the present invention, there is provided a finger lifting device, wherein the finger lifting device has 5 lifting brackets, and respectively controls 5 sets of stretching devices, each set of stretching devices is connected with a finger, wherein when the lifting device receives an upper computer command, the corresponding lifting bracket is controlled to perform a lifting action, and the lifting bracket lifts the corresponding finger upwards through the stretching device; after the lifting action is finished, the lifting support and the stretching device return to the original position, and the fingers of the user naturally return to the original position.
According to a fourth aspect of the present invention, there is provided a brain-computer interface based haptic finger motion rehabilitation system, characterized in that the system comprises: the vibration trigger device described above; the signal acquisition equipment acquires the brain electrical activity at the scalp, converts the acquired analog signals into digital signals and transmits the digital signals to the signal processing module through the USB port; a signal processing module to: analyzing the acquired data in real time, wherein electroencephalogram data are input into a signal processing module from signal acquisition equipment, the signal processing module carries out Butterworth band-pass filtering and feature extraction on the data, and finally a Bayesian linear classifier is used for classification; the signal processing module is simultaneously used as an upper computer to control the whole experimental process, collect electroencephalogram signals, output a command for controlling vibration and output a command for controlling the finger lifting device; and two sets of the finger lifting devices are respectively fixed on the two hand backs of the user.
Preferably, the tactile finger movement rehabilitation system is configured to perform the steps of:
step S1, a vibration stimulation control step;
when vibration stimulation is needed, the data processing module sends instructions in the sequence one by one according to a fixed time interval, and after receiving the command, the vibration stimulation inducing module starts a vibration device on the corresponding finger;
step S2, a motor imagery signal recognition step;
the user starts imagining to lift a certain hand for movement; recording the electroencephalogram signals within a set time from the beginning; preprocessing the data; the classifier classifies the imagined hand of the user according to the model made by the off-line data; and
step S3, online P300 signal processing step;
after each vibration stimulus appears, the data processing program stores the received data in a cache; when the data next to a certain stimulation setting time is recorded, the program starts to process the data; preprocessing the data; the classifier calculates according to a model obtained by using the off-line data to obtain a classification coefficient corresponding to each vibration stimulus;
after 5 fingers receive vibration stimulation, each stimulation has a corresponding classification coefficient, the largest classification coefficient is selected, and the represented finger is the target finger; if the fingers obtained in two consecutive times are the same, the fingers are output as control signals, and the vibration stimulation is suspended at the same time.
Preferably, in step S1, before the system starts to work, the data processing module will calculate the order of occurrence of vibration stimulation according to the following rules; (1) the vibration stimulation cannot continuously occur for 2 times at the same position; (2) the stimulus must appear on all 5 fingers, and only once, completing one group; after which the next group starts.
Preferably, the haptic finger motion rehabilitation system is further configured to perform between step S1 and step S2:
step A, a motor imagery off-line data acquisition step, which comprises the following steps:
step a1, the user receiving an instruction, imagine left or right hand;
step a2, the user imagines a left or right hand lift for several seconds;
step A3, imagine the end, if the acquisition is not finished, go to step a1 until enough data are acquired;
step B, P300 vibration data acquisition step, comprising:
step B1, the user receiving an instruction as to which finger to vibrate;
step B2, the motor on 10 fingers will vibrate in a certain order;
step B3, the user defaults the number of vibrations in mind when the requested motor vibrates;
step B4, the user is not concerned when the motor vibrates on the other fingers;
-step B5, stopping the vibration after a certain number of vibrations of the motor;
step B6, return to step B1 until enough data has been collected; and
the offline data of step C, motor imagery and P300 are used to train the respective classification models for the identification of the brain electrical signals in online use.
Preferably, the signal acquisition device transmits the signal to the signal processing module through a USB port;
the signal processing module performs Butterworth band-pass filtering and feature extraction on the data, and finally classifies the data by using a Bayesian linear classifier.
The invention has the beneficial effects that: the invention provides a system and a method for recovering the motion of a touch finger based on a brain-computer interface, which use the brain-computer interface technology to ensure that a user can directly control the lifting of the finger by imagination. This intuitive design facilitates better user engagement. The brain is helped to form a control loop, and the rapid recovery of the brain is helped.
The invention combines the motor imagery and the P300 paradigm, utilizes the motor imagery to determine the left hand and the right hand, and P300 to determine specific fingers, and finds a balance between accuracy and speed. The invention utilizes the vibration unit to generate the P300 signal related to the touch sense, does not need visual participation and can help the user with visual difficulty to recover.
Drawings
Fig. 1 is a schematic diagram of the exercise rehabilitation system of the finger exercise rehabilitation system according to the present invention.
Fig. 2 is a flowchart of a motor rehabilitation method of the finger motor rehabilitation system according to the present invention.
Fig. 3 is a schematic diagram of the finger exercise rehabilitation system according to the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Example one
Referring to fig. 1 and 3, the present invention discloses a tactile finger exercise rehabilitation system based on brain-computer interface, the exercise rehabilitation system includes: the device comprises a vibration trigger device 1, a signal acquisition device 2, a signal processing module 3 and a finger lifting device 4.
The vibration inducing device is a P300 inducing device, a micromotor is adopted to generate vibration, and a P300 signal related to touch is induced; the equipment is divided into 2 sets of 5 vibration units; in use, 10 units are attached to 10 fingers of a user in sequence.
The signal acquisition equipment acquires the brain electrical activity of the scalp, converts the acquired analog signals into digital signals and transmits the digital signals to the signal processing module through the USB port.
The signal processing module is used for analyzing the acquired data in real time; the electroencephalogram data are input into the signal processing module from the signal acquisition equipment, the signal processing module carries out Butterworth band-pass filtering and feature extraction on the data, and finally, a Bayesian linear classifier is used for classification.
The signal processing module is used for controlling an experimental process at the same time; the signal processing module is simultaneously used as an upper computer to control the whole experimental process, collect motor imagery signals, output commands for controlling vibration and output commands for controlling the finger lifting device.
The finger lifting devices comprise two sets which are respectively fixed on two hand backs of a user, and each set is provided with 5 lifting brackets which are respectively connected with 5 fingers through respective stretching devices; the lifting bracket is used for lifting the fingers of the user upwards; the finger lifting device can control a certain finger to do lifting action according to a command sent by the upper computer.
Referring to fig. 2, an exercise rehabilitation method using the above-mentioned tactile finger exercise rehabilitation system based on brain-computer interface includes the following steps:
step S1, a vibration stimulation control step;
before the system starts to work, the data processing module calculates the occurrence sequence of the vibration stimulation according to the following rules; (1) the vibration stimulation cannot continuously occur for 2 times at the same position; (2) the stimulus must appear on all 5 fingers, and only once, completing one group; then the next group starts;
when vibration stimulation is needed, the data processing module sends instructions in the sequence one by one according to a fixed time interval, and after receiving the command, the vibration stimulation inducing module starts a vibration device on the corresponding finger;
step A, an off-line data acquisition step of the motor imagery, comprising the following steps:
step a1, the user receiving an instruction, imagine left or right hand;
step a2, the user imagines a left or right hand lift for several seconds;
step A3, imagine the end, if the acquisition is not finished, go to step a1 until enough data are acquired;
step B, P300 vibration data acquisition step, which comprises the following steps:
step B1, the user receiving an instruction as to which finger to vibrate;
step B2, the motor on 10 fingers will vibrate in a certain order;
step B3, the user defaults the number of vibrations in mind when the requested motor vibrates;
step B4, the user is not concerned when the motor vibrates on the other fingers;
-step B5, stopping the vibration after a certain number of vibrations of the motor;
step B6, return to step B1 until enough data has been collected.
Step C, the motor imagery and the offline data of P300 are used to train respective classification models for recognizing the brain electrical signals in online use.
Step S2, a motor imagery signal identification step;
the user starts imagining to lift a certain hand for movement; from the beginning, the electroencephalogram signals within a period of time are recorded; preprocessing the data; the classifier classifies the user's imagined hand according to a model made from the offline data.
Step S3, an online P300 signal processing step;
after each vibration stimulus appears, the data processing program stores the received data in a cache; when 800ms of data immediately following a stimulus is recorded, the program begins processing the data; preprocessing the data; the classifier calculates according to a model obtained by using the off-line data to obtain a classification coefficient corresponding to each vibration stimulus;
after 5 fingers receive vibration stimulation, each stimulation has a corresponding classification coefficient, the largest classification coefficient is selected, and the represented finger is the target finger; if the fingers obtained in two consecutive times are the same, outputting the fingers as control signals, and simultaneously suspending vibration stimulation;
the use steps of the tactile finger movement rehabilitation system based on the brain-computer interface are shown in fig. 2: the system starts, the user receives the instruction (such as the index finger of the left hand) and imagines that the left hand of the user is lifted (actually, all parts of the body do not move); the system analyzes the collected EEG signals; the system recognizes that the user imagines a left hand; the system controls five stimulation devices on the left hand to vibrate at corresponding time according to a certain sequence; the frequency of the user's silent vibration stimulation appearing on the index finger of the left hand in the heart; the system analyzes the acquired electroencephalogram signals and identifies that the user pays attention to the index finger; the system sends a command to control the lifting device to lift the left index finger.
Example two
A brain-machine interface based tactile finger motor rehabilitation system, the motor rehabilitation system comprising: the device comprises vibration trigger equipment, signal acquisition equipment, a signal processing module and a finger lifting device.
The vibration inducing device comprises a number of vibration units for triggering a vibration signal. The signal acquisition equipment acquires the brain electrical activity of the scalp, converts the acquired analog signals into digital signals and transmits the digital signals to the signal processing module.
The signal processing module is used for analyzing the acquired data in real time; the electroencephalogram data are input into a signal processing module from signal acquisition equipment, and the signal processing module carries out filtering, feature extraction and classification on the data; the signal processing module is used for controlling an experimental process at the same time; the signal processing module is simultaneously used as an upper computer to control the whole experimental process, collect motor imagery signals, output commands for controlling vibration and output commands for controlling the finger lifting device.
The finger lifting devices are respectively fixed on two hand backs of a user, and each set of device is provided with a plurality of lifting brackets which are respectively connected with corresponding fingers through respective stretching devices; the lifting bracket is used for lifting the fingers of the user upwards; the finger lifting device can control a certain finger to do lifting action according to a command sent by the upper computer.
The invention also discloses a motion rehabilitation method using the touch finger motion rehabilitation system based on the brain-computer interface, which comprises the following steps:
step S1, a vibration stimulation control step;
when vibration stimulation is needed, the data processing module sends instructions in the sequence one by one according to a fixed time interval, and after the vibration stimulation inducing module receives the command, the vibration device on the corresponding finger is started.
Step S2, a motor imagery signal recognition step;
the user starts imagining to lift a certain hand for movement; recording the electroencephalogram signals within a set time from the beginning; preprocessing the data; the classifier classifies the user's imagined hand according to a model made from the offline data.
Step S3, online P300 signal processing step;
after each vibration stimulus appears, the data processing program stores the received data in a cache; when the data next to a certain stimulation setting time is recorded, the program starts to process the data; preprocessing the data; the classifier calculates according to a model obtained by using the off-line data to obtain a classification coefficient corresponding to each vibration stimulus;
after 5 fingers receive vibration stimulation, each stimulation has a corresponding classification coefficient, the largest classification coefficient is selected, and the represented finger is the target finger; if the fingers obtained in two consecutive times are the same, the fingers are output as control signals, and the vibration stimulation is suspended at the same time.
In summary, the system and the method for recovering the finger movement based on the tactile sensation of the brain-computer interface provided by the invention use the brain-computer interface technology, so that a user can directly control the finger to be lifted through imagination. This intuitive design facilitates better user engagement. The brain is helped to form a control loop, and the rapid recovery of the brain is helped.
The invention combines the motor imagery and the P300 paradigm, utilizes the motor imagery to determine the left hand and the right hand, and P300 to determine specific fingers, and finds a balance between accuracy and speed. The invention utilizes the vibration unit to generate the P300 signal related to the touch sense, does not need visual participation and can help the user with visual difficulty to recover.
The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (8)
1. A vibration trigger device comprising:
a P300 induction device for inducing a P300 signal associated with the sense of touch using a micromotor to generate vibrations, wherein said device is divided into 2 sets of 5 vibration units each, and in use, 10 vibration units are affixed to 10 fingers of a user in sequence.
2. A finger lifting device is provided, wherein the finger lifting device is provided with 5 lifting brackets which are respectively connected with 5 fingers through respective stretching devices, the lifting brackets are used for lifting the fingers of a user upwards, and the finger lifting device can control a certain finger to perform lifting action according to a command sent by an upper computer.
3. A finger lifting device is provided with 5 lifting supports, 5 sets of stretching devices are controlled respectively, each set of stretching devices is connected with a finger, when the lifting devices receive an upper computer command, the corresponding lifting supports are controlled to perform lifting actions, and the lifting supports lift the corresponding fingers upwards through the stretching devices; after the lifting action is finished, the lifting support and the stretching device return to the original position, and the fingers of the user naturally return to the original position.
4. A tactile finger motor rehabilitation system based on a brain-computer interface, the system comprising:
the vibration trigger device of claim 1;
the signal acquisition equipment acquires the brain electrical activity at the scalp, converts the acquired analog signals into digital signals and transmits the digital signals to the signal processing module through the USB port;
a signal processing module to:
analyzing the acquired data in real time, wherein electroencephalogram data are input into a signal processing module from signal acquisition equipment, the signal processing module carries out Butterworth band-pass filtering and feature extraction on the data, and finally a Bayesian linear classifier is used for classification; and is
Controlling an experiment process, wherein the signal processing module is simultaneously used as an upper computer to control the whole experiment process, collect electroencephalogram signals, output a command for controlling vibration and output a command for controlling the finger lifting device; and
two sets of finger lift devices according to claim 2 or 3, respectively attached to the backs of the user's hands.
5. A tactile finger motor rehabilitation system according to claim 4, wherein the tactile finger motor rehabilitation system is configured to perform the following steps:
step S1, a vibration stimulation control step;
when vibration stimulation is needed, the data processing module sends instructions in the sequence one by one according to a fixed time interval, and after receiving the command, the vibration stimulation inducing module starts a vibration device on the corresponding finger;
step S2, a motor imagery signal recognition step;
the user starts imagining to lift a certain hand for movement; recording the electroencephalogram signals within a set time from the beginning; preprocessing the data; the classifier classifies the imagined hand of the user according to the model made by the off-line data; and
step S3, online P300 signal processing step;
after each vibration stimulus appears, the data processing program stores the received data in a cache; when the data next to a certain stimulation setting time is recorded, the program starts to process the data; preprocessing the data; the classifier calculates according to a model obtained by using the off-line data to obtain a classification coefficient corresponding to each vibration stimulus;
after 5 fingers receive vibration stimulation, each stimulation has a corresponding classification coefficient, the largest classification coefficient is selected, and the represented finger is the target finger; if the fingers obtained in two consecutive times are the same, the fingers are output as control signals, and the vibration stimulation is suspended at the same time.
6. The haptic finger motion rehabilitation system according to claim 5, wherein in step S1, before the system starts to operate, the data processing module calculates the order of occurrence of vibration stimuli according to the following rules; (1) the vibration stimulation cannot continuously occur for 2 times at the same position; (2) the stimulus must appear on all 5 fingers, and only once, completing one group; after which the next group starts.
7. A tactile finger movement rehabilitation system according to claim 5, wherein the tactile finger movement rehabilitation system is further configured to perform between steps S1 and S2:
step A, a motor imagery off-line data acquisition step, which comprises the following steps:
step a1, the user receiving an instruction, imagine left or right hand;
step a2, the user imagines a left or right hand lift for several seconds;
step A3, imagine the end, if the acquisition is not finished, go to step a1 until enough data are acquired;
step B, P300 vibration data acquisition step, comprising:
step B1, the user receiving an instruction as to which finger to vibrate;
step B2, the motor on 10 fingers will vibrate in a certain order;
step B3, the user defaults the number of vibrations in mind when the requested motor vibrates;
step B4, the user is not concerned when the motor vibrates on the other fingers;
-step B5, stopping the vibration after a certain number of vibrations of the motor;
step B6, return to step B1 until enough data has been collected; and
the offline data of step C, motor imagery and P300 are used to train the respective classification models for the identification of the brain electrical signals in online use.
8. The tactile finger motor rehabilitation system according to claim 4, wherein:
the signal acquisition equipment is transmitted to the signal processing module through the USB port;
the signal processing module performs Butterworth band-pass filtering and feature extraction on the data, and finally classifies the data by using a Bayesian linear classifier.
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| CN201611061234.2A CN106774851A (en) | 2016-11-25 | 2016-11-25 | Sense of touch finger motion rehabilitation system and method based on brain-computer interface |
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| CN109011098A (en) * | 2018-08-01 | 2018-12-18 | 龚映清 | It is a kind of to be actively intended to vision and kinesthetic feedback training system and its operating method |
| CN109077895B (en) * | 2018-08-01 | 2020-06-19 | 深圳睿瀚医疗科技有限公司 | Active reinforcement type training system with multiple feedback modes and operation method thereof |
| CN109656365B (en) * | 2018-12-19 | 2021-03-30 | 东南大学 | Brain-computer interface method and system based on real-time closed-loop vibration stimulation enhancement |
| CN112085052A (en) * | 2020-07-28 | 2020-12-15 | 中国科学院深圳先进技术研究院 | Training method of motor imagery classification model, motor imagery method and related equipment |
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| CN104799984A (en) * | 2015-05-14 | 2015-07-29 | 华东理工大学 | Assistance system for disabled people based on brain control mobile eye and control method for assistance system |
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