Gait intervention method for dyskinesia disease population, music generation method, device, system, equipment and medium

Abstract

The application discloses a gait intervention method for a dyskinesia disease population, a music generation method, device, system, equipment and medium, and relates to the technical field of intelligent medical treatment. The method comprises the following steps: in the process that a target object suffering from a dyskinesia disease is intervened and treated according to abnormal gait by rhythm music, collecting multi-modal physiological data of the target object in real time, and dynamically determining a plurality of motion symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data; determining music parameters at each moment in real time according to the plurality of motion symptom characteristic indexes; dynamically adjusting notes of the rhythm music at each moment according to the music parameters at each moment, and generating rhythm music adapted to the current gait condition of the target object. According to the scheme, the rhythm music can be more comprehensively and individually dynamically adjusted, and is more suitable for the current gait condition of the target object, so that the effect of abnormal gait rehabilitation treatment, intervention and correction can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computers, in particular to the technical field of smart medical treatment, and especially to a gait intervention method for a movement disorder disease population, a music generation method, device, system, equipment and medium. BACKGROUND

[0002] In the field of medical rehabilitation, gait disorders are common symptoms of various movement disorder diseases (such as Parkinson's disease, sequelae of stroke, etc.), which seriously affect the quality of life of patients. At present, the method of using music or rhythm intervention to improve gait disorders has been widely used.

[0003] In the prior art, there is a gait intervention method based on music rhythm. This method improves the gait rhythm and coordination of patients by making them follow the preset music or rhythm for gait intervention treatment. The specific implementation process is as follows: first, a doctor or a rehabilitation therapist selects appropriate rhythm music or rhythm as an intervention stimulus according to the basic condition and gait characteristics of the patient; then, the patient steps according to the heard music or rhythm signal in the gait intervention process, and tries to make his own pace synchronized with the rhythm. In the gait intervention process, the therapist will observe the gait performance of the patient, and adjust the rhythm of the music or rhythm in time according to experience to better guide the patient to complete the gait intervention treatment.

[0004] However, the existing gait intervention method has the following defects:

[0005] 1. The prior art solution mainly relies on the subjective observation and experience of the therapist to adjust the intervention music or rhythm signal, lacks real-time and accurate monitoring and evaluation of the patient's movement state, and leads to the optimization of the intervention music or rhythm signal being not scientific and timely enough, affecting the rehabilitation treatment effect.

[0006] 2. It is impossible to perform dynamic and continuous evaluation and timing analysis during the gait rhythm music intervention process, and it is impossible to timely capture the subtle changes in the patient's gait and the real-time state in the intervention process, and it is difficult to flexibly adjust the intervention measures of the gait rhythm music according to the actual reaction of the patient.

[0007] 3. It can only adjust the rhythm of the music or rhythm, and cannot realize comprehensive and personalized dynamic adjustment of the gait rhythm music. SUMMARY

[0008] In view of the technical problems of lack of scientificity, comprehensiveness and individualization in the music adjustment of the existing gait intervention based on music rhythm, a gait intervention method for a movement disorder disease population, a music generation method, device, system, equipment and medium are provided.

[0009] According to a first aspect, a music generation method based on physiological data of a movement disorder disease is provided, comprising:

[0010] In the process of abnormal gait intervention treatment of the target object with movement disorder disease according to rhythmic music, the multi-modal physiological data of the target object is collected in real time, and a plurality of movement symptom characteristic indexes related to gait of the target object are dynamically determined according to the multi-modal physiological data;

[0011] The music parameters of each time are determined in real time according to the plurality of movement symptom characteristic indexes;

[0012] The notes of each time of the rhythmic music are dynamically adjusted according to the music parameters of each time, and the rhythmic music adapted to the current gait condition of the target object is generated.

[0013] According to a second aspect, a gait intervention correction method for a movement disorder disease population is provided, comprising:

[0014] The rhythmic music required by the target object for abnormal gait intervention treatment is dynamically generated by the music generation method based on the physiological data of the movement disorder disease;

[0015] The rhythmic music is played to the target object for auditory stimulation to guide the target object to perform intervention treatment of abnormal gait based on the rhythmic music.

[0016] According to a third aspect, a music generation device based on physiological data of movement disorder disease is provided, comprising:

[0017] The data acquisition unit is configured to collect multi-modal physiological data of the target object in real time in the process of abnormal gait intervention treatment of the target object with movement disorder disease according to rhythmic music, and dynamically determine a plurality of movement symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data;

[0018] The music parameter determination unit is configured to determine music parameters of each time in real time according to a plurality of movement symptom characteristic indexes;

[0019] The music generation unit is configured to dynamically adjust the notes of each time of the rhythmic music according to the music parameters of each time, and generate the rhythmic music adapted to the current gait condition of the target object.

[0020] According to a fourth aspect, a gait intervention correction system for a movement disorder disease population is provided, comprising:

[0021] The above-mentioned music generation device based on the physiological data of the movement disorder disease is configured to generate the rhythmic music required by the target object for abnormal gait intervention treatment in real time;

[0022] The playing unit is configured to play the rhythmic music to the target object for auditory stimulation, so as to guide the target object to intervene in the abnormal gait based on the rhythmic music.

[0023] According to a fifth aspect, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the embodiments of the music generation method based on physiological data of movement disorder diseases or the gait intervention correction method for movement disorder disease population.

[0024] According to a sixth aspect, a computer readable storage medium is provided, which stores a computer program, when the program is executed by a processor, the method of any one of the embodiments of the music generation method based on physiological data of movement disorder diseases or the gait intervention correction method for movement disorder disease population is implemented.

[0025] According to the scheme of the present application, in the process of abnormal gait intervention treatment of the target object with movement disorder diseases based on rhythmic music, the movement symptom characteristic indexes related to gait of the target object are determined according to the real-time and dynamic multi-modal physiological data of the target object, and then the music parameters at each time are determined in real time based on the movement symptom characteristic indexes, and finally the notes at each time of the currently used rhythmic music are dynamically adjusted according to the music parameters at each time, so that the rhythmic music suitable for the current gait condition of the target object is generated, and the adjustment and generation of the rhythmic music is realized based on the movement symptom characteristic indexes of the current actual gait condition, so that the adjustment and generation process of the rhythmic music is more scientific and timely, avoiding the influence of human experience and subjective factors, and dynamic and continuous evaluation and time sequence analysis can be realized, so that the subtle changes of the gait of the target object and the real-time state in the gait intervention process can be captured in time, the intervention measures of the gait rhythm music can be flexibly adjusted according to the actual reaction of the target object, and the rhythmic music can be more comprehensively and individually dynamically adjusted, which is more suitable for the current gait condition of the target object, and is conducive to improving the effect of abnormal gait rehabilitation treatment, intervention and correction. BRIEF DESCRIPTION OF DRAWINGS

[0026] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0027] Figure 1 is a flow chart of an embodiment of the music generation method based on physiological data of movement disorder diseases according to the present application;

[0028] Figure 2is a schematic diagram of one application scenario of the music generation method based on physiological data of movement disorder diseases according to the present application;

[0029] Figure 3 is a flow chart of one embodiment of the gait intervention correction method for movement disorder disease population according to the present application;

[0030] Figure 4 is a structural schematic diagram of one embodiment of the music generation device based on physiological data of movement disorder diseases according to the present application;

[0031] Figure 5 is a structural schematic diagram of one embodiment of the gait intervention correction system for movement disorder disease population according to the present application;

[0032] Figure 6 is a block diagram of an electronic device for implementing the music generation method based on physiological data of movement disorder diseases or the gait intervention correction method for movement disorder disease population according to the embodiments of the present application. DETAILED DESCRIPTION

[0033] The exemplary embodiments of the present application are described below with reference to the accompanying drawings, which include various details of the embodiments of the present application to assist in understanding, and should be considered as merely exemplary. Thus, those of ordinary skill in the art will recognize various changes and modifications of the embodiments described herein, which will not depart from the scope and spirit of the present application. Also, for the sake of brevity and clarity, descriptions of well-known functions and constructions are omitted from the following description.

[0034] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0035] An exemplary system architecture to which the embodiments of the music generation method (device) based on physiological data of movement disorder diseases or the gait intervention correction method (system) for movement disorder disease population according to the present application can be applied can include a plurality of terminal devices, a network and a server. The network is a medium to provide a communication link between the plurality of terminal devices and the server. The network can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0036] The multi-modal physiological data can be collected using a plurality of terminal devices, and interacted with the server through the network. The plurality of terminal devices can be an electroencephalogram collection device, a heart rate collection device, an electromyogram signal collection device, etc.

[0037] The server can be a server providing various services, such as a background server providing support for the terminal device. The background server can analyze and process the received multi-modal physiological data and the like, and feed back the processing result (e.g., generated rhythmic music) to the terminal device for playing.

[0038] Reference Figure 1 , shows a flow of one embodiment of a music generation method based on physiological data of movement disorder diseases according to the present application. The music generation method based on physiological data of movement disorder diseases comprises the following steps:

[0039] Step 101, in the process of abnormal gait intervention treatment of the target object with movement disorder diseases according to rhythmic music, real-time acquisition of multi-modal physiological data of the target object, and dynamic determination of a plurality of movement symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data;

[0040] Step 102, real-time determination of music parameters at each time according to a plurality of movement symptom characteristic indexes;

[0041] Step 103, dynamic adjustment of notes of the rhythmic music at each time according to the music parameters at each time, and generation of rhythmic music adapted to the current gait condition of the target object.

[0042] In some optional implementation manners of the present embodiment, in order to determine the current gait condition in real time and accurately, so as to generate comprehensive, personalized and accurately adapted rhythmic music, it is proposed to determine movement symptom characteristic indexes based on multi-modal physiological data, and further generate music parameters based on the movement symptom characteristic indexes, for example, dynamic determination of a plurality of movement symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data, including: determination of a plurality of movement symptom characteristic indexes of the target object according to brain waves, heart rate and electromyographic signals, and the plurality of movement symptom characteristic indexes include movement retardation index, static tremor index, muscle rigidity index and posture balance disorder index.

[0043] In some optional implementations of the embodiment, in the process of the target object receiving abnormal gait intervention treatment according to the rhythmic music, the current alpha wave power, the minimum alpha wave power, the maximum alpha wave power, the beta wave power (Pβ), the minimum beta wave power, the maximum beta wave power, the theta wave power, the minimum theta wave power, the maximum theta wave power and the like can be collected by the existing electroencephalogram acquisition terminal device; the mean heart rate, the minimum heart rate, the maximum heart rate, the heart rate variance, the minimum heart rate variance, the maximum heart rate variance and the like can be collected by the heart rate sensor in the process of human body movement; the electromyographic activity intensity, the minimum electromyographic activity intensity, the maximum electromyographic activity intensity, the electromyographic activity frequency, the minimum electromyographic activity frequency, the maximum electromyographic activity frequency and the like can be collected by the electromyographic sensor in the process of human body movement.

[0044] It is found by the inventors of the present application that a plurality of movement symptom characteristic indexes of movement disorder diseases related to gait can be extracted from multi-modal physiological data such as brain waves, heart rates and electromyograms, for example, the following correlations exist between movement retardation and multi-modal physiological data such as brain waves, heart rates and electromyograms. Brain wave characteristics: in electroencephalogram (EEG), the motor cortex and related brain areas of patients with movement disorder diseases (for example, Parkinson's disease) often show reduced beta wave (13-30 Hz) activity and relatively increased theta wave (4-8 Hz) activity. This change reflects the decrease in excitability of the motor cortex of the brain, the decrease in the degree of neuron synchronization, the slowing down of the initiation and conduction speed of movement instructions, and thus causes the symptom of movement retardation of patients. Heart rate characteristics: heart rate variability (HRV) is an important indicator reflecting the function of the autonomic nervous system. Patients with movement disorder diseases (for example, Parkinson's disease) have impaired autonomic nervous function and reduced heart rate variability. Specifically, in time domain analysis, the standard deviation (SDNN) of adjacent RR intervals, root mean square deviation (RMSSD) and the like decrease; in frequency domain analysis, the low frequency (LF) and high frequency (HF) power components decrease, and the LF / HF ratio is abnormal. This decrease in heart rate variability reflects the imbalance of sympathetic and parasympathetic nerves in regulating the heart, which is closely related to the symptom of movement retardation, and indicates that the patient's cardiovascular system has decreased adaptability during movement. Electromyographic characteristics: through surface electromyography (sEMG), it can be observed that patients with movement disorder diseases (for example, Parkinson's disease) have decreased muscle explosive force, reduced electromyographic signal amplitude and prolonged muscle contraction duration during movement.

[0045] For example, resting tremor is associated with the following multi-modal physiological data: EEG features: Parkinson's disease patients often exhibit 4-6 Hz rhythmic theta wave activity in the resting state, which has a certain correlation with tremor frequency. In electroencephalogram, this rhythmic theta wave mainly appears in brain areas related to movement and sensation, such as the precentral gyrus and postcentral gyrus. Its mechanism may be related to the dysfunction of the basal ganglia-thalamus-cortex loop. When the neurons in this loop abnormally discharge, synchronized electrical activity at a specific frequency band is generated, triggering resting tremor. Heart rate features: The fluctuation of heart rate and tremor frequency is a significant heart rate feature of Parkinson's disease resting tremor. Specifically, in electrocardiogram (ECG), the fluctuation frequency of heart rate and tremor frequency have a certain harmonic relationship. For example, when the tremor frequency is about 5 Hz, the heart rate fluctuation may have frequency components that are integer multiples or fractional multiples of the tremor frequency. This synchronization reflects the abnormal regulation of the autonomic nervous system, which may be caused by the periodic changes in blood pressure due to muscle contraction and relaxation during tremor, affecting the heart rhythm. In addition, the baseline level of heart rate may also increase, and the heart rate variability further decreases, indicating increased sympathetic nervous excitability. Electromyography features: In patients with resting tremor, the tremor-related muscles exhibit synchronous rhythmic electrical activity. Through electromyography, it can be observed that the antagonist and synergist muscles exhibit regular alternating discharge at the tremor frequency.

[0046] For example, muscle rigidity is associated with the following multi-modal physiological data: EEG features: Parkinson's disease patients exhibit elevated alpha wave (8-13 Hz) and beta wave amplitude in electroencephalogram. This change mainly occurs in brain areas related to muscle movement control, such as the motor cortex and supplementary motor area. The increase in alpha and beta wave amplitude reflects the overexcitation and synchronization enhancement of cerebral cortical neurons, leading to weakened inhibitory control of muscles, resulting in continuous muscle tension. Heart rate features: The resting heart rate of patients with muscle rigidity is usually elevated, and the heart rate variability is reduced. The increase in heart rate baseline may be related to increased sympathetic nervous excitability, while the decrease in heart rate variability further reflects the dysfunction of autonomic nervous function. During muscle relaxation training, the heart rate decreases less and recovers more slowly, indicating decreased adaptability of the heart to muscle state changes. Electromyography features: The muscles of patients with muscle rigidity exhibit continuous high-amplitude electrical activity without obvious relaxation period. Through electromyography, it can be observed that the electromyography signal amplitude of the relevant muscles of patients is high in both resting and exercise states, and the muscle contraction duration is prolonged.

[0047] For example, the following correlations exist between posture balance disorders and multi-modal physiological data such as brain waves, heart rate, and muscle electricity: brain wave features: posture balance disorders in Parkinson's disease patients are related to the abnormality of theta waves and alpha waves in the vestibular-related brain regions. In electroencephalogram, the theta wave power of these brain regions increases, and the alpha wave power decreases, reflecting the functional disorder of the vestibular nucleus and related cortical areas. This abnormal brain activity affects the brain's ability to process and integrate balance information, making it difficult for patients to maintain a stable posture when standing and walking. Heart rate features: the resting heart rate of patients with posture balance disorders increases, and the heart rate is sluggish in response to posture changes. When normal people stand up from a sitting position or sit down from a standing position, their heart rate quickly adjusts to adapt to the changes in blood dynamics caused by body position changes, while the heart rate adjustment ability of Parkinson's disease patients decreases significantly. Specifically, during the posture change process, the heart rate has a smaller rise or fall amplitude, and the time to reach a steady state is prolonged. Muscle electricity features: patients with posture balance disorders have poor coordination of lower limb and trunk muscles, and the muscle electricity signals are out of sync. During walking, the contraction and relaxation of the lower limb muscles of normal people have a certain rhythmicity and coordination, while the muscle contraction timing of Parkinson's disease patients is disordered, and the synergy between different muscle groups is weakened.

[0048] In some optional implementations of the present embodiment, the plurality of gait-related movement symptom feature indexes of the target object can be determined by the following steps:

[0049] According to the decrease of the theta wave power in the brain waves, the decrease of the heart rate variability, and the decrease of the muscle electricity burst force, the movement retardation index is determined;

[0050] According to the tremor-related theta wave power in the brain waves, the synchronicity of the heart rate fluctuation and the tremor frequency, and the muscle electricity tremor amplitude, the static tremor index is determined;

[0051] According to the increase of the alpha wave amplitude and the beta wave amplitude in the brain waves, the synchronicity of the heart rate baseline, and the muscle electricity sustained activity, the muscle rigidity index is determined;

[0052] According to the abnormality degree of the theta wave and the alpha wave in the vestibular-related brain regions in the brain waves, the reactivity of the heart rate to the posture change, and the muscle electricity coordination, the posture balance disorder index is determined.

[0053] In some optional implementations of the present embodiment, according to the decrease of the theta wave power in the brain waves, the decrease of the heart rate variability, and the decrease of the muscle electricity burst force, the movement retardation index (reflecting the degree of movement retardation of the target object) is determined, including:

[0054] The movement retardation index is calculated by the following formula:

[0055]

[0056] wherein, , , are the weight coefficients of the electroencephalogram, heart rate and electromyogram, respectively, the θ wave relative power is the ratio of the power of the θ wave to the power of the full frequency band in the electroencephalogram, and the ratio is used to reflect the degree of decrease in the excitability of the motor cortex of the brain, the θ wave relative power min and the θ wave relative power max are the minimum and maximum values of the difference in the θ wave relative power between the normal population and the target object, the heart rate variability is the degree of fluctuation of the heart rate, and can be represented by a time domain or frequency domain analysis index, such as the SDNN, RMSSD or LF / HF ratio, etc., the heart rate variability min and the heart rate variability max are the minimum and maximum values of the difference in the heart rate between the normal population and the target object, the electromyogram burst force represents the ability of the muscle to produce the maximum force in a unit of time, and can be calculated by the amplitude and rising rate of the electromyogram, the electromyogram burst force min and the electromyogram burst force max are the minimum and maximum values of the difference in the electromyogram burst force between the normal population and the target object.

[0057] According to the tremor-related θ wave power in the electroencephalogram, the synchronism of the heart rate fluctuation and the tremor frequency, and the electromyogram tremor amplitude, the static tremor index (reflecting the degree of static tremor of the target object) is determined, comprising:

[0058] The static tremor index is calculated by the following formula:

[0059]

[0060] wherein, the tremor-related θ wave power is the θ wave power in the electroencephalogram related to the tremor frequency, and is used to reflect the degree of functional disorder of the basal ganglia-thalamus-cortex loop, the tremor-related θ wave power min and the tremor-related θ wave power max are the minimum and maximum values of the difference in the tremor-related θ wave power between the normal population and the target object, the heart rate and tremor synchronism is the correlation between the heart rate fluctuation and the tremor frequency, and can be calculated by the cross-correlation analysis or harmonic analysis method, the heart rate and tremor synchronism min is the minimum value of the difference in the heart rate and tremor synchronism between the normal population and the target object, the heart rate and tremor synchronism max is the maximum value of the difference in the heart rate and tremor synchronism between the normal population and the target object, the electromyogram tremor amplitude is the amplitude of the electromyogram of the tremor-related muscle, and is used to evaluate the intensity of the tremor, the electromyogram tremor amplitude min and the electromyogram tremor amplitude maxThese are the minimum and maximum values ​​of the difference in electromyographic tremor amplitude between the normal population and the target subject, respectively;

[0061] The myotonia index (reflecting the degree of myotonia in the target subject) is determined based on the increase in alpha and beta wave amplitudes in the electroencephalogram (EEG), the synchronicity of the heart rate baseline, and the sustained electromyographic activity. This includes:

[0062] The muscle rigidity index is calculated using the following formula:

[0063]

[0064] Among them, the amplitudes of alpha and beta waves are the sum of the amplitudes of alpha and beta waves in the electroencephalogram (EEG), used to reflect the degree of overexcitation of neurons in the cerebral cortex. min The minimum difference in alpha and beta wave amplitudes between the normal population and the target population; alpha and beta wave amplitudes. max The maximum difference in alpha and beta wave amplitudes between the normal population and the target subject is defined as the baseline heart rate, which is the average heart rate of the target subject at rest and reflects the degree of overexcitation of neurons in the cerebral cortex. min and baseline heart rate max These represent the minimum and maximum differences in baseline heart rate between the normal population and the target subjects, respectively. Continuous electromyography (EMG) activity is the amplitude of the EMG signal at rest, used to assess the sustained tension of the muscle. min and continuous electromyographic activity max These represent the minimum and maximum values ​​of the difference in sustained electromyographic activity between the normal population and the target subject, respectively.

[0065] The postural balance disorder index (reflecting the degree of postural balance disorder in the target subject) is determined based on the degree of abnormality in theta and alpha waves in vestibular-related brain regions during electroencephalography (EEG), the responsiveness of heart rate to postural changes, and electromyographic coordination. This includes:

[0066] The postural balance obstacle index is calculated using the following formula:

[0067]

[0068] Among them, the degree of abnormality of theta and alpha waves in vestibular-related brain regions refers to the degree of power change of theta and alpha waves in vestibular-related brain regions in the electroencephalogram (EEG), which is used to reflect the decline in balance control ability and the degree of abnormality of theta and alpha waves in vestibular-related brain regions. min The minimum difference in the degree of abnormality of theta and alpha waves in vestibular-related brain regions between normal individuals and the target subjects; the degree of abnormality of theta and alpha waves in vestibular-related brain regions. maxThe maximum value of the difference between the θ wave and the α wave of the vestibular-related brain area of the normal population and the target object, the reactivity of the heart rate to the posture change is the adjustment ability of the heart rate of the target object when the posture changes, the reactivity of the heart rate to the posture change can be calculated by the amplitude and the speed of the heart rate change, and the reactivity of the heart rate to the posture change min and the reactivity of the heart rate to the posture change max The minimum value and the maximum value of the difference between the reactivity of the heart rate to the posture change of the normal population and the target object respectively, the electromyographic coordination is the coordination degree of the lower limb and the trunk muscle when moving and maintaining the posture, the electromyographic coordination can be calculated by the time sequence analysis and the correlation analysis of the electromyographic signal, and the electromyographic coordination min and the electromyographic coordination max The minimum value and the maximum value of the difference between the electromyographic coordination of the normal population and the target object respectively.

[0069] In some optional implementations of the embodiment, in order to realize that the music parameters at each moment can be determined in real time according to the motion symptom characteristic indexes, the following method for determining the music parameters is proposed, for example, determining the music parameters at each moment in real time according to a plurality of motion symptom characteristic indexes, comprising:

[0070] determining the rhythm speed at each moment in real time according to the motion retardation index and the rhythm speed of the rhythmic music;

[0071] determining the rhythm change complexity at each moment in real time according to the posture balance disorder index and the rhythm change complexity of the rhythmic music;

[0072] determining the beat regularity at each moment in real time according to the static tremor index and the beat regularity of the rhythmic music;

[0073] determining the melody fluency at each moment in real time according to the motion retardation index and the melody fluency of the rhythmic music;

[0074] determining the melody fluctuation at each moment in real time according to the static tremor index and the melody fluctuation of the rhythmic music;

[0075] determining the harmonic tension at each moment in real time according to the muscle rigidity index and the harmonic tension of the rhythmic music;

[0076] determining the harmonic richness at each moment in real time according to the posture balance disorder index and the harmonic richness of the rhythmic music.

[0077] In some optional implementations of the embodiment, in order to accurately determine the music parameters at each moment in real time according to the movement symptom characteristic index, the rhythm speed at each moment is determined in real time according to the movement retardation index and the rhythm speed of the rhythmic music, including:

[0078] The rhythm speed is calculated by the following formula:

[0079]

[0080] wherein the basic rhythm speed is the rhythm speed of the rhythmic music, for example, 120 BPM, and the maximum movement retardation index is the maximum movement retardation index observed in clinical data; when the movement retardation index is higher, the rhythm speed is slower.

[0081] The rhythm change complexity at each moment is determined in real time according to the posture balance disorder index and the rhythm change complexity of the rhythmic music, including:

[0082] The rhythm change complexity is calculated by the following formula:

[0083]

[0084] wherein the basic rhythm change complexity is the rhythm change complexity of the rhythmic music, for example, taking a value of 1, the maximum posture balance disorder index is the maximum posture balance disorder index observed in clinical data, the maximum rhythm change complexity is the maximum value in the allowed values of the rhythm change complexity (for example, the existing conventional allowed values of the rhythm change complexity), for example, taking a value of 3; the higher the posture balance disorder index, the more complex the rhythm change.

[0085] The beat regularity at each moment is determined in real time according to the static tremor index and the beat regularity of the rhythmic music, including:

[0086] The beat regularity is calculated by the following formula:

[0087]

[0088] wherein the basic beat regularity is the beat regularity of the rhythmic music, for example, taking a value of 0.5, the maximum static tremor index is the maximum static tremor index observed in clinical data, and the maximum beat regularity is the maximum value in the allowed values of the beat regularity, which can take a value of 1; the higher the static tremor index, the stronger the beat regularity.

[0089] The melody fluency at each moment is determined in real time according to the movement retardation index and the melody fluency of the rhythmic music, including:

[0090] The melody fluency is calculated by the following formula:

[0091]

[0092] Wherein, the basic melody fluency is the melody fluency of the rhythmic music, for example, the basic melody fluency can take the value of 0.5, the maximum melody fluency is the maximum value in the allowed value of the melody fluency, for example, the maximum melody fluency can take the value of 1; the higher the movement retardation index, the more fluent and slow the melody is;

[0093] The melody fluctuation at each time point is determined in real time according to the static tremor index and the melody fluctuation of the rhythmic music, including:

[0094] The melody fluctuation is calculated by the following formula:

[0095]

[0096] Wherein, the basic melody fluctuation is the melody fluctuation of the rhythmic music, for example, the basic melody fluctuation can take the value of 0.3, the maximum melody fluctuation is the maximum value in the allowed value of the melody fluctuation, for example, the maximum melody fluctuation can take the value of 0.7; the higher the static tremor index, the more obvious the melody fluctuation is;

[0097] The harmonic tension at each time point is determined in real time according to the muscle rigidity index and the harmonic tension of the rhythmic music, including:

[0098] The harmonic tension is calculated by the following formula:

[0099]

[0100] Wherein, the basic harmonic tension is the harmonic tension of the rhythmic music, for example, the basic harmonic tension can take the value of 0.6; the higher the muscle rigidity index, the lower the harmonic tension is;

[0101] The harmonic richness at each time point is determined in real time according to the posture balance disorder index and the harmonic richness of the rhythmic music, including:

[0102] The harmonic richness is calculated by the following formula:

[0103]

[0104] Wherein, the basic harmonic richness is the harmonic richness of the rhythmic music, for example, the basic harmonic richness can take the value of 1, the maximum harmonic richness is the maximum value of the harmonic richness, and the maximum harmonic richness can take the value of 3; the higher the posture balance disorder index, the richer the harmony is.

[0105] In some optional implementations of the embodiment, after determining the music parameters at each time point, the notes at each time point of the rhythmic music can be dynamically adjusted in real time according to the music parameters at each time point. For example, the note duration at each time point is determined according to the rhythm speed and the beat regularity at the time point; the pitch variation range at each time point is determined according to the melody fluency and the melody volatility at the time point; the chord at each time point is determined according to the harmony richness and the harmony tension at the time point; the rhythm pattern at each time point is determined according to the rhythm change complexity at the time point; and the notes at the time point are composed according to the determined note duration, pitch variation range, chord and rhythm pattern at the time point.

[0106] In implementation, the note duration can be mainly composed of quarter notes and eighth notes, and a few half notes can be added to adapt to slower rhythms.

[0107] The formula for calculating the note duration is as follows:

[0108] ;

[0109] Parameter explanation:

[0110] Rhythm speed: rhythm speed obtained by mapping the movement retardation index, with the unit of BPM.

[0111] Beat regularity: beat regularity obtained by mapping the static tremor index, with the value range of 0-1.

[0112] For example, the rhythm speed is 72 BPM and the beat regularity is 0.8, then the duration of each quarter note is:

[0113] Quarter note duration = 1 BPM × 0.666 seconds × 0.8 = 0.666 seconds.

[0114] In implementation, the formula for calculating the pitch variation range is as follows:

[0115] Pitch variation range = base pitch × (1 + melody fluency × melody volatility)

[0116] Parameter explanation:

[0117] Base pitch: the value can be C4, indicating the base pitch of the notes.

[0118] Melody fluency: melody fluency obtained by mapping the movement retardation index, with the value range of 0-1.

[0119] Melody volatility: melody volatility obtained by mapping the static tremor index, with the value range of 0-1.

[0120] For example, if the melody fluency is 0.6 and the volatility is 0.4, the pitch change range is: pitch change range = base pitch × (1 + 0.6 × 0.4) = base pitch × 1.24.

[0121] In implementation, the formula for calculating the chord is as follows:

[0122]

[0123] Parameter interpretation:

[0124] Base chord: The default value is C major chord, representing the base selection of the chord.

[0125] Harmonic richness: The harmonic richness obtained by mapping the posture balance disorder index, with a value range of 0-3.

[0126] Harmonic tension: The harmonic tension obtained by mapping the muscle rigidity index, with a value range of 0-1.

[0127] Maximum chord range: Represents the maximum range of available chords, usually G major chord.

[0128] In implementation, the melody construction rule is: according to the melody fluency, construct smooth melody lines with small pitch changes to adapt to the patient's movement retardation symptoms.

[0129] The formula for calculating the rhythm pattern is as follows:

[0130]

[0131] Parameter interpretation:

[0132] Base rhythm pattern: The default value is a combination of quarter notes and eighth notes, representing the base type of rhythm.

[0133] Rhythm change complexity: The rhythm change complexity obtained by mapping the posture balance disorder index, with a value range of 0-3.

[0134] Maximum rhythm change complexity: Represents the maximum value of rhythm change complexity, usually 3.

[0135] Maximum rhythm pattern change: Represents the maximum change range of rhythm pattern, usually including complex rhythm patterns such as sixteenth notes and dotted notes.

[0136] In implementation, the calculated note duration, pitch, chord and rhythm pattern of each time are combined into the note of that time, and the notes of each time form a music sequence according to the time sequence.

[0137] For example, the music sequence is {(0.666 seconds, C4 x 1.378, C major chord), (0.333 seconds, E4 x 1.378, G major chord), …}

[0138] The first note has a note duration of 0.666 seconds, a pitch of C4 multiplied by 1.378, and a chord of C major chord. The second note has a note duration of 0.333 seconds, a pitch of E4 multiplied by 1.378, and a chord of G major chord. And so on.

[0139] In implementation, after dynamically adjusting the notes of each time of the rhythmic music according to the music parameters of each time, the rhythmic music that adapts to the current gait condition of the target object is generated to provide the target object with rhythmic auditory stimulation to guide the gait movement.

[0140] Referring back to Figure 2 , Figure 2 is a schematic diagram of an application scenario of the music generation method based on physiological data of movement disorder diseases according to the present embodiment. In Figure 2 application scenario, the execution subject 201 collects multi-modal physiological data 202 of the target object in real time in the process of abnormal gait intervention treatment of the target object with movement disorder diseases according to rhythmic music, and dynamically determines a plurality of movement symptom characteristic indexes 203 related to gait of the target object according to the multi-modal physiological data. The execution subject 201 determines music parameters 204 of each time in real time according to a plurality of movement symptom characteristic indexes. The execution subject 201 dynamically adjusts the notes of each time of the rhythmic music according to the music parameters of each time, and generates rhythmic music 205 that adapts to the current gait condition of the target object.

[0141] Referring back to Figure 3 , Figure 3 is a flowchart of one embodiment of the gait intervention correction method for movement disorder disease population according to the present embodiment, comprising the following steps:

[0142] Step 301: dynamically generate the rhythmic music required by the target object for abnormal gait intervention treatment by any of the above-mentioned music generation methods based on physiological data of movement disorder diseases;

[0143] Step 302: play the rhythmic music to the target object for auditory stimulation to guide the target object to perform abnormal gait intervention treatment based on the rhythmic music.

[0144] Further reference is made to Figure 4As an implementation of the method shown in the above figures, the present application provides an embodiment of a music generation device based on physiological data of movement disorder diseases, which corresponds to the method embodiment shown in Figure 1 In addition to the features described below, the device embodiment can also include the same or corresponding features or effects as the method embodiment shown in Figure 1 The device can be specifically applied to various electronic devices.

[0145] As shown in Figure 4 The music generation device based on physiological data of movement disorder diseases 400 of the present embodiment includes a data acquisition unit 401, a music parameter determination unit 402, and a music generation unit 403. The data acquisition unit 401 is configured to acquire multi-modal physiological data of a target object with a movement disorder disease in real time during the process of abnormal gait intervention treatment of the target object according to rhythmic music, and dynamically determine a plurality of movement symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data. The music parameter determination unit 402 is configured to determine music parameters at each time in real time according to a plurality of movement symptom characteristic indexes. The music generation unit 403 is configured to dynamically adjust notes at each time of the rhythmic music according to the music parameters at each time, and generate rhythmic music adapted to the current gait condition of the target object.

[0146] In the present embodiment, the specific processing of the data acquisition unit 401, the music parameter determination unit 402, and the music generation unit 403 of the music generation device based on physiological data of movement disorder diseases 400 and the technical effects brought by them can be respectively referred to the corresponding embodiments of the steps 101, 102, and 103. Figure 1 The related descriptions of the steps 101, 102, and 103 in the corresponding embodiments will not be repeated here.

[0147] Further referring to Figure 5 As an implementation of the method shown in the above figures, the present application provides an embodiment of a gait intervention correction system 500 for movement disorder disease population, which corresponds to the method embodiment shown in Figure 3 In addition to the features described below, the system embodiment can also include the same or corresponding features or effects as the method embodiment shown in Figure 3 The system can be specifically applied to various electronic devices.

[0148] As shown in Figure 5As shown, the gait intervention correction system 500 for the gait intervention of the gait disorder disease population of the embodiment includes a music generation device 400 based on physiological data of the gait disorder disease and a playing unit 501. The music generation device 400 based on physiological data of the gait disorder disease is configured to generate rhythm music required for the target object to perform abnormal gait intervention treatment in real time; the playing unit 501 is configured to play the rhythm music to the target object for auditory stimulation to guide the target object to perform abnormal gait intervention treatment based on the rhythm music.

[0149] According to the embodiments of the present application, the present application also provides an electronic device and a readable storage medium.

[0150] As Figure 6 shown, it is a block diagram of an electronic device for a music generation method based on physiological data of a gait disorder disease or a gait intervention correction method for a gait disorder disease population according to the embodiments of the present application. The electronic device is intended to represent a variety of forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent a variety of forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown in the figure, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0151] As Figure 6 shown, the electronic device includes one or more processors 601, memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected using different buses, and can be mounted on a common motherboard or otherwise installed as desired. The processor can process instructions for execution within the electronic device, including instructions stored in the memory or on the memory to display graphical information for a GUI on an external input / output device, such as a display device coupled to the interface. In other implementations, multiple processors and / or multiple buses can be employed as desired to improve performance, for example, as can be needed for certain implementations of the present application. Also, various other peripheral devices can be connected to the electronic device, such as an input device and a power supply unit. Figure 6 The processor 601 is taken as an example in the figure.

[0152] The memory 602 is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the processor to perform the music generation method based on physiological data of movement disorders or the gait intervention and correction method for people with movement disorders provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the music generation method based on physiological data of movement disorders or the gait intervention and correction method for people with movement disorders provided in this application.

[0153] Memory 602, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the music generation method based on physiological data of movement disorders or the gait intervention and correction method for people with movement disorders in the embodiments of this application (e.g., attached). Figure 4 The data acquisition unit 401, music parameter determination unit 402, and music generation unit 403 shown, or attached Figure 5 The playback unit 501 shown above. The processor 601 executes various functional applications and data processing of the server by running non-transient software programs, instructions and modules stored in the memory 602, that is, to implement the music generation method based on physiological data of movement disorders or the gait intervention and correction method for people with movement disorders in the above method embodiments.

[0154] The memory 602 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function. The data storage area may store data created by using an electronic device based on physiological data of movement disorders or gait intervention and correction methods for people with movement disorders. Furthermore, the memory 602 may include high-speed random access memory and may also include non-transient memory, such as at least one disk storage device, flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 602 may optionally include memory remotely located relative to the processor 601. This remote memory can be connected via a network to an electronic device using a music generation method based on physiological data of movement disorders or gait intervention and correction methods for people with movement disorders. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0155] The electronic device for the music generation method based on physiological data of movement disorder diseases or the gait intervention correction method for movement disorder disease population can further include an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603, and the output device 604 can be connected through a bus or other means, Figure 6 The connection through the bus is taken as an example.

[0156] The input device 603 can receive inputted digital or character information, and generate key signal input related to user settings and function control of the processing electronic device of the music generation method based on physiological data of movement disorder diseases or the gait intervention correction method for movement disorder disease population, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 604 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor), etc. The display device can include, but is not limited to, a liquid crystal display (LCD), a light-emitting diode (LED) display, and a plasma display. In some embodiments, the display device can be a touch screen.

[0157] Various embodiments of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0158] These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus and / or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0159] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0160] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0161] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0162] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and

[0163] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware. The described units can also be arranged in a processor, for example, a processor can be described as including a data acquisition unit, a music parameter determination unit and a music generation unit. Among them, the name of these units does not constitute a limitation to the unit itself in some cases, for example, the data acquisition unit can also be described as "a unit for acquiring data".

[0164] As another aspect, the present application also provides a computer readable medium, which can be included in the apparatus or system described in the above embodiments, or can exist independently without being assembled into the apparatus or system. The above computer readable medium carries one or more programs, when the one or more programs are executed by the apparatus, the apparatus is caused to: in the process of abnormal gait intervention treatment of a target object with a movement disorder disease according to rhythmic music, real-time acquisition of multi-modal physiological data of the target object, and dynamic determination of a plurality of movement symptom characteristic indexes related to gait of the target object according to the multi-modal physiological data; real-time determination of music parameters at each time according to a plurality of movement symptom characteristic indexes; dynamic adjustment of notes of the rhythmic music at each time according to the music parameters at each time, and generation of rhythmic music adapted to the current gait condition of the target object.

[0165] The above description is only the preferred embodiments of the present application and the explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A method for music generation based on physiological data of movement disorder disease, the method comprising: acquiring multi-modal physiological data of a target subject suffering from movement disorder disease in real time during an abnormal gait intervention treatment of the target subject according to rhythmic music, and dynamically determining a plurality of movement symptom characteristic indexes of the target subject related to gait according to the multi-modal physiological data; determining music parameters at each time instant in real time according to the plurality of movement symptom characteristic indexes; dynamically adjusting notes of the rhythmic music at each time instant according to the music parameters at each time instant, and generating rhythmic music adapted to a current gait condition of the target subject; dynamically determining a plurality of movement symptom characteristic indexes of the target subject related to gait according to multi-modal physiological data, comprising: determining a plurality of movement symptom characteristic indexes of the target subject according to electroencephalogram, heart rate and electromyogram, wherein the plurality of movement symptom characteristic indexes comprise movement retardation index, static tremor index, muscle rigidity index and posture balance disorder index; determining a plurality of movement symptom characteristic indexes of the target subject according to electroencephalogram, heart rate and electromyogram, comprising: determining the movement retardation index according to a decrease in θ wave power in electroencephalogram, a decrease in heart rate variability and a decrease in electromyogram burst force; determining the static tremor index according to tremor-related θ wave power in electroencephalogram, synchronism of heart rate fluctuation and tremor frequency and electromyogram tremor amplitude; determining the muscle rigidity index according to an increase in α wave amplitude and β wave amplitude in electroencephalogram, synchronism of heart rate baseline and electromyogram sustained activity; determining the posture balance disorder index according to abnormality degree of θ wave and α wave in vestibular-related brain area in electroencephalogram, reactivity of heart rate to posture change and electromyogram coordination; determining music parameters at each time instant in real time according to the plurality of movement symptom characteristic indexes, comprising: determining rhythm speed at each time instant in real time according to the movement retardation index and rhythm speed of the rhythmic music; determining rhythm change complexity at each time instant in real time according to the posture balance disorder index and rhythm change complexity of the rhythmic music; determining beat regularity at each time instant in real time according to the static tremor index and beat regularity of the rhythmic music; determining melody fluency at each time instant in real time according to the movement retardation index and melody fluency of the rhythmic music; determining melody fluctuation at each time instant in real time according to the static tremor index and melody fluctuation of the rhythmic music; determining harmony tension at each time instant in real time according to the muscle rigidity index and harmony tension of the rhythmic music; determining harmony richness at each time instant in real time according to the posture balance disorder index and harmony richness of the rhythmic music.

2. The method of claim 1, wherein, determining the movement retardation index according to a decrease in θ wave power in electroencephalogram, a decrease in heart rate variability and a decrease in electromyogram burst force, comprising: calculating the movement retardation index by the following formula: wherein, , , are the weight coefficients of the brain waves, the heart rate and the muscle electrical signals respectively, the θ wave relative power is the ratio of the θ wave power to the full-band power in the brain waves, the θ wave relative power min and the θ wave relative power max are the minimum and maximum values of the θ wave relative power difference between the normal population and the target object, the heart rate variability is the degree of heart rate fluctuation, the heart rate variability min and the heart rate variability max are the minimum and maximum values of the heart rate difference between the normal population and the target object, the muscle electrical burst force represents the ability of the muscle to produce the maximum force in a unit of time, the muscle electrical burst force min and the muscle electrical burst force max are the minimum and maximum values of the muscle electrical burst force difference between the normal population and the target object. determining the resting tremor index according to the tremor-related theta wave power in the brain waves, the synchronism of heart rate fluctuation and tremor frequency, and the amplitude of electromyographic tremor, including: calculating the resting tremor index by the following formula: Wherein, the tremor-related theta wave power is the power of the theta wave in the brain waves related to the tremor frequency, the tremor-related theta wave power min and the tremor-related theta wave power max are respectively the minimum and maximum values of the difference in the tremor-related theta wave power between the normal population and the target object, the heart rate and tremor synchronicity is the correlation between the heart rate fluctuation and the tremor frequency, the heart rate and tremor synchronicity min is the minimum value of the difference in the heart rate and tremor synchronicity between the normal population and the target object, the heart rate and tremor synchronicity max is the maximum value of the difference in the heart rate and tremor synchronicity between the normal population and the target object, the electromyogram tremor amplitude is the amplitude of the electromyogram signal of the tremor-related muscle, the electromyogram tremor amplitude min and the electromyogram tremor amplitude max are respectively the minimum and maximum values of the difference in the electromyogram tremor amplitude between the normal population and the target object. determining the muscle rigidity index according to the rise of alpha wave amplitude and beta wave amplitude in the brain waves, the synchronism of heart rate baseline, and the sustained activity of electromyography, including: calculating the muscle rigidity index by the following formula: Wherein, the alpha and beta amplitude is the sum of the alpha amplitude and the beta amplitude in the brain waves, the alpha and beta amplitude min is the minimum value of the alpha and beta amplitude difference between the normal population and the target object, the alpha and beta amplitude max is the maximum value of the alpha and beta amplitude difference between the normal population and the target object, the heart rate baseline is the average heart rate of the target object in the resting state, the heart rate baseline min and the heart rate baseline max are respectively the minimum value and the maximum value of the heart rate baseline difference between the normal population and the target object, the electromyogram sustained activity is the electromyogram amplitude of the muscle in the resting state, the electromyogram sustained activity min and the electromyogram sustained activity max are respectively the minimum value and the maximum value of the electromyogram sustained activity difference between the normal population and the target object; determining the postural balance disorder index according to the abnormality degree of theta wave and alpha wave of the vestibular-related brain area in the brain waves, the reactivity of heart rate to posture change, and the coordination of electromyography, including: calculating the postural balance disorder index by the following formula: The abnormality degree of the vestibular-related brain area theta wave and alpha wave is a power change degree of the vestibular-related brain area theta wave and alpha wave in the brain wave, and the abnormality degree of the vestibular-related brain area theta wave and alpha wave min is a minimum value of the difference between the normal population and the target object in the abnormality degree of the vestibular-related brain area theta wave and alpha wave, the abnormality degree of the vestibular-related brain area theta wave and alpha wave max is a maximum value of the difference between the normal population and the target object in the abnormality degree of the vestibular-related brain area theta wave and alpha wave, the heart rate response to posture change is an adjustment ability of the heart rate of the target object when the posture changes, and the heart rate response to posture change min and the heart rate response to posture change max are respectively a minimum value and a maximum value of the difference between the normal population and the target object in the heart rate response to posture change, the electromyographic coordination is a coordination degree of the lower limb and trunk muscles when moving and maintaining posture, and the electromyographic coordination min and the electromyographic coordination max are respectively a minimum value and a maximum value of the difference between the normal population and the target object in the electromyographic coordination.

3. The method of claim 2, wherein, determining the rhythm speed at each moment in real time according to the movement retardation index and the rhythm speed of the rhythmic music, including: calculating the rhythm speed by the following formula: wherein the basic rhythm speed is the rhythm speed of the rhythmic music, and the maximum movement retardation index is the maximum movement retardation index observed in the clinical data; determining the rhythm change complexity at each moment in real time according to the postural balance disorder index and the rhythm change complexity of the rhythmic music, including: calculating the rhythm change complexity by the following formula: wherein the basic rhythm change complexity is the rhythm change complexity of the rhythmic music, the maximum postural balance disorder index is the maximum postural balance disorder index observed in the clinical data, and the maximum rhythm change complexity is the maximum value in the allowed values of the rhythm change complexity; determining the beat regularity at each moment in real time according to the resting tremor index and the beat regularity of the rhythmic music, including: calculating the beat regularity by the following formula: wherein the basic beat regularity is the beat regularity of the rhythmic music, the maximum resting tremor index is the maximum resting tremor index observed in the clinical data, and the maximum beat regularity is the maximum value in the allowed values of the beat regularity; determining the melody fluency at each moment in real time according to the movement retardation index and the melody fluency of the rhythmic music, including: calculating the melody fluency by the following formula: wherein the basic melody fluency is the melody fluency of the rhythmic music, and the maximum melody fluency is the maximum value in the allowed values of the melody fluency; determining the melody fluctuation at each moment in real time according to the resting tremor index and the melody fluctuation of the rhythmic music, including: calculating the melody fluctuation by the following formula: wherein the basic melody fluctuation is the melody fluctuation of the rhythmic music, and the maximum melody fluctuation is the maximum value in the allowed values of the melody fluctuation; determining the harmony tension at each moment in real time according to the muscle rigidity index and the harmony tension of the rhythmic music, including: calculating the harmony tension by the following formula: wherein the basic harmony tension is the harmony tension of the rhythmic music; determining the harmony richness at each moment in real time according to the postural balance disorder index and the harmony richness of the rhythmic music, including: calculating the harmony richness by the following formula: The base and the chord richness are the chord richness of the rhythmic music, and the maximum chord richness is the maximum value in the allowed value of the chord richness.

4. The method of claim 1, wherein, According to the music parameters at each time, dynamically adjusting the notes of the rhythmic music at each time, including: According to the rhythm speed and the beat regularity at each time, determining the note duration at that time; According to the melody fluctuation and the melody fluency at each time, determining the pitch change range at that time; According to the chord richness and the chord tension at each time, determining the chord at that time; According to the rhythm change complexity at each time, determining the rhythm pattern at that time; According to the determined note duration, pitch change range, chord and rhythm pattern at that time, combining the notes at that time.

5. A gait intervention correction method for a dyskinesia patient, the method comprising: dynamically generating rhythmic music required for the target object to intervene in abnormal gait treatment by the method of any one of claims 1 to 4; playing the rhythmic music to the target object for auditory stimulation to guide the target object to intervene in abnormal gait treatment based on the rhythmic music.

6. A music generation device based on physiological data of dyskinesia, the device comprising: a data acquisition unit for acquiring multi-modal physiological data of a target object suffering from dyskinesia in real time during the process of the target object according to rhythmic music to intervene in abnormal gait treatment, and dynamically determining a plurality of motor symptom characteristic indexes of dyskinesia related to gait of the target object according to the multi-modal physiological data; a music parameter determination unit for determining music parameters at each time in real time according to a plurality of motor symptom characteristic indexes; a music generation unit for dynamically adjusting the notes of the rhythmic music at each time according to the music parameters at each time, and generating rhythmic music adapted to the current gait condition of the target object; the data acquisition unit is configured to determine a plurality of motor symptom characteristic indexes of the target object according to electroencephalogram, heart rate and electromyogram signals, and the plurality of motor symptom characteristic indexes include motor retardation index, static tremor index, muscle rigidity index and posture balance disorder index; the data acquisition unit is configured to determine the motor retardation index according to the reduction of θ wave power in electroencephalogram, the reduction of heart rate variability and the reduction of electromyogram burst force, determine the static tremor index according to the tremor-related θ wave power in electroencephalogram, the synchronicity of heart rate fluctuation and tremor frequency and the electromyogram tremor amplitude, determine the muscle rigidity index according to the increase of α wave amplitude and β wave amplitude in electroencephalogram, the synchronicity of heart rate baseline and the electromyogram sustained activity, and determine the posture balance disorder index according to the abnormality degree of θ wave and α wave in the vestibular-related brain area in electroencephalogram, the reactivity of heart rate to posture change and the electromyogram coordination. The music parameter determination unit is configured to determine, in real time, a rhythm speed at each time point according to the movement retardation index and a rhythm speed of the rhythmic music; determine, in real time, a rhythm change complexity at each time point according to the posture balance obstacle index and a rhythm change complexity of the rhythmic music; determine, in real time, a beat regularity at each time point according to the static tremor index and a beat regularity of the rhythmic music; determine, in real time, a melody fluency at each time point according to the movement retardation index and a melody fluency of the rhythmic music; determine, in real time, a melody fluctuation at each time point according to the static tremor index and a melody fluctuation of the rhythmic music; determine, in real time, a harmony tension at each time point according to the muscle rigidity index and a harmony tension of the rhythmic music; and determine, in real time, a harmony richness at each time point according to the posture balance obstacle index and a harmony richness of the rhythmic music.

7. A gait intervention correction system for a movement disorder patient, the system comprising: The device of claim 6 is configured to generate, in real time, rhythmic music required by the target object for abnormal gait intervention treatment; A playing unit is configured to play the rhythmic music to the target object for auditory stimulation, so as to guide the target object to perform the intervention treatment of abnormal gait based on the rhythmic music.

8. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-4 or the method of claim 5.

9. A computer readable storage medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method of any one of claims 1-4 or the method of claim 5. The program is executed by the processor to implement the method of any one of claims 1-4 or the method of claim 5.

Images