CN116687354B - Intelligent analysis feedback system for digital biomarkers of parkinsonism patient - Google Patents
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Abstract
The invention relates to a digital biomarker intelligent analysis feedback system for parkinsonism patients, which comprises the following components: the acquisition unit is used for acquiring one or more types of physiological signals of the patient; an analysis unit for processing the acquired physiological signals to obtain digital biomarkers related to frozen gait; a stimulation unit for providing a stimulation signal to the patient; the analysis unit can generate a control command for regulating the stimulation unit based on the acquired digital biomarker related to the frozen gait, and the stimulation signal provided by the stimulation unit responding to the control command can drive the patient to perform a motion matched with the stimulation purpose mapped by the control command, wherein the analysis unit generates a corresponding control command at least according to the current motion state of the patient, and the digital biomarker related to the frozen gait can be used for judging the type of the current motion state of the patient.
Description
Technical Field
The invention relates to the technical field of rehabilitation assisting medical equipment, in particular to a digital biomarker intelligent analysis feedback system for parkinsonism patients.
Background
The digital biomarker (Digital Biomarker) is objective characteristic data obtained by collecting various data such as physiological indexes and motion states of a user in an internet of things mode and the like and analyzing and sorting the data through artificial intelligence when the user uses a mobile phone, a computer, a tablet and intelligent wearing equipment. The characteristic data may be used to predict and evaluate a physiological state or health state of the user. On the one hand, the current development goal of digital biomarkers is an effective complement of traditional biomarkers, rather than replacing the latter; on the other hand, digital biomarkers can strongly drive the transition of healthy medical patterns from passive countering to active prevention. Digital biomarkers are expected to be effective means for deep understanding of human health and disease.
Parkinson's disease is a common nervous system degeneration disease, the elderly frequently see that the most important pathological change is degeneration death of midbrain substantia nigra dopaminergic neurons, thereby leading to significant reduction of striatal DA content and pathogenicity, and clinical manifestations mainly comprise resting tremor, bradykinesia, myotonia and postural gait disorder, and patients can be accompanied with non-motor symptoms such as depression, constipation and sleep disorder. Gait disorder is one of the characteristics of advanced parkinson's disease, and is mainly manifested as frozen gait, panic-open gait, and the like. Frozen gait is one of the pathological gaits common to parkinson's disease. Studies have shown that about 7.1% of early parkinsonian patients develop frozen gait, whereas late parkinsonian patients have frozen gait morbidity as high as 53%, which has a great impact on the life of the patient, and severe patients can fall to cause fractures, lose self-care ability, and even die.
Frozen gait is defined as "subjectively wanting to walk, but with a short swing or a significant lack of forward walking. This definition includes various behavioral characteristics such as transient walking initiation difficulties (initiation hysteresis), forward termination (turn or end-point hysteresis), and small steps of panic. In most cases, the frozen gait includes the following important features: (1) the foot does not leave the ground or the foot lifting amplitude is very small; (2) alternating trembling of the two legs in a mode of 3-8 Hz; (3) The gait is panic or the pace rhythm is obviously increased and the pace is obviously reduced; (4) subjectively perceiving that the biped sticks to the ground; (5) Internal or external factors may exacerbate or mitigate frozen gait; (6) The symptoms of the two lower limbs can be asymmetric, mainly involve the lower limbs on one side, and are more easy to induce when turning to one side. Frozen gait may not be a single constant symptom, but rather a syndrome caused by a variety of different pathogenesis, such as lack of motion freezing, tremor of the original lower extremities, panic-step.
CN115737386a discloses an intelligent device and method for improving the frozen gait of parkinson's disease, the intelligent device comprising a control box arranged on a hip joint assisting exoskeleton, and a special headset and a controller connected with the control box; the special headset comprises a headset and a laser, wherein the laser is arranged outside an earmuff of the headset through a rotating shaft and a knob and can rotate around the rotating shaft outside the earmuff so as to adjust the distance of the overground horizontal laser line; the left and right sides of the waist binding belt of the hip joint assisting exoskeleton are rotatably connected with thigh frameworks through hip joints, thigh binding belts are arranged at the other ends of the thigh frameworks, and the thigh frameworks are controlled to rotate around the hip joints through driving motors.
CN112617807a discloses a device and method for preventing and relieving frozen gait of parkinson's disease patient, the device comprises an acceleration sensor, a plantar pressure sensor, a mobile terminal, a vibration node and a vibration force-sensitive insole; the method comprises a method for constructing a frozen gait prediction detection model and a method for preventing and relieving frozen gait of a patient suffering from Parkinson's disease based on the frozen gait prediction detection model. The motion modes of a patient are monitored in real time through plantar pressure sensors arranged on the vibration force-sensitive insole, frozen gait prediction detection models of different motion modes are trained and verified based on acceleration signals, prediction is quickly made immediately before frozen gait occurs, and detection judgment is accurately made when frozen gait has occurred. The combination of vibration nodes placed on the vibratory force-sensitive insole provides rhythmic tactile cues that block further deterioration of the patient's gait, thereby helping the patient to resume normal walking.
Gait training is an important content of rehabilitation training of Parkinson patients, and in the prior art, although the functions of laser guidance, beat reminding, touch reminding and the like can be realized for the patients in the rehabilitation training process, the triggering of each function is mostly manually selected by the patients, the training requirement of the patients cannot be intelligently judged in the rehabilitation training process of the patients, the optimal training guidance cannot be automatically provided for the patients, intervention cannot be effectively performed in time based on the real-time motion state of the patients, and the expected training effect is difficult to realize.
Furthermore, there are differences in one aspect due to understanding to those skilled in the art; on the other hand, since the applicant has studied a lot of documents and patents while making the present invention, the text is not limited to details and contents of all but it is by no means the present invention does not have these prior art features, but the present invention has all the prior art features, and the applicant remains in the background art to which the right of the related prior art is added.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a digital biomarker intelligent analysis feedback system to solve at least part of the technical problems.
Traditional biomarkers often require specialized personnel and equipment (e.g., medical imaging equipment, gauges, gait labs) and are often invasive in measurement (e.g., blood drawing assays), resulting in limited measurement range and high cost. Digital biomarkers are objective criteria for the development, interpretation, or prediction of disease progression that are quantifiable, clinically interpretable by digitally converting the "signal" released by humans. By means of popularization and application of the intelligent terminal and technical development of big data and artificial intelligence, the digital biomarker can effectively promote chronic disease management, full life cycle health management and personalized and accurate medical development, improve disease prevention and diagnosis quality, and further reduce influence of diseases on personal life and social economy.
The invention discloses a digital biomarker intelligent analysis feedback system, which comprises:
an acquisition unit for acquiring one or more types of physiological signals of a patient;
an analysis unit for processing the acquired physiological signals to obtain digital biomarkers related to frozen gait;
and the stimulation unit is used for providing a stimulation signal for the patient.
Preferably, the analysis unit is capable of generating control instructions for regulating the stimulation unit based on the obtained digital biomarkers related to the frozen gait, the stimulation signals provided by the stimulation unit in response to the control instructions are capable of driving the patient to perform actions matched with the stimulation purposes mapped by the control instructions, wherein the analysis unit is capable of generating corresponding control instructions at least according to the current motion state of the patient, and the digital biomarkers related to the frozen gait are capable of being used for judging the type of the current motion state of the patient.
The intelligent analytical feedback system of the present invention may be particularly useful in gait training of parkinson's patients to determine the type of motion state the patient is currently in based on digital biomarkers associated with frozen gait. The "digital biomarker related to frozen gait" may be used to characterize the likelihood of frozen gait occurring, which may be obtained by an analysis unit operating on one or more types of physiological signals acquired by an acquisition unit. The physiological signals acquired by the acquisition unit at least comprise one or more of electrocardiosignals, skin electric signals and acceleration signals.
For the electrocardiosignal, the analysis unit can calculate the average heart rate and heart rate variability from the original electrocardiographic data acquired by the acquisition unit to be used as a digital biomarker related to the frozen gait, wherein the calculation mode is that each acquisition time window is 3 seconds, the two acquisition time windows overlap for 0.5 seconds, and the average heart rate and heart rate variability of each acquisition time window are calculated through the original ECG data so as to predict the occurrence of the frozen gait. For skin electric signals, the analysis unit can calculate the average value (C) mean ) Median (C) median ) And standard deviation (C) std ) As the "and freeze stepStatus-related digital biomarkers.
For the acceleration signal, the analysis unit can calculate the lateral disturbance degree from the acceleration data of the arm and/or foot motion acquired by the acquisition unit to be used as a digital biomarker related to the frozen gait, wherein the lateral disturbance degree can be comprehensively calculated by a plurality of measurement indexes.
The intelligent analysis feedback system can monitor the motion state of the patient in the process of rehabilitation training of the patient, judge the motion type of the patient according to the monitoring data, and then adjust the working mode or the working parameters of the response unit according to the motion type of the patient so as to automatically provide training guidance for the patient.
According to a preferred embodiment, the analysis unit is capable of obtaining the digital biomarker related to the frozen gait according to the occurrence times of key features of the physiological signal in a preset period, wherein the key features are feature data, which are carried by the physiological signal and exceed a set threshold value after being analyzed and calculated, of the data information.
The purpose of the analysis unit to obtain the digital biomarker is to use the digital biomarker to determine the type of movement of the patient.
According to a preferred embodiment, the analysis unit can divide at least the type of the current movement state of the patient into a first movement state which can be intervened by the stimulation unit, a second movement state which can be pre-warned by the analysis unit and a third movement state which can be recorded by the analysis unit, wherein the analysis unit can drive the stimulation unit to intervene at least when the patient is in the first movement state and does not deviate within a set time limit.
The arrangement can grade the movement type of the patient through the analysis unit, when the patient is in the first movement state, the patient is indicated to have the possibility of frozen gait immediately, and the response unit is required to intervene, so that the action of dynamically guiding in walking to reduce the pause is achieved; when the patient is in the second motion state, the stride of the patient is gradually reduced, so that the possibility of frozen gait of the patient is indicated, and the response unit is required to perform early warning to remind the patient of adjusting the posture and the gait; when the patient is in the third movement state, the gait of the patient is in the normal category, and the analysis unit can use the monitoring data of the patient when the gait of the patient is normal as the basic data of machine learning, so that the grading accuracy of the analysis unit on the individual movement types of the patient is improved.
According to a preferred embodiment, the stimulation unit is capable of applying one or more of sensory electrical stimulation, auditory sound stimulation, visual light stimulation to the patient based on control instructions, wherein the control instructions to which the stimulation unit is responsive are capable of being derived from the analysis unit and/or the input unit.
After the stimulation unit provides the stimulation signal, the patient can make corresponding walking actions based on the stimulation signal, and the physiological signal of the patient when the patient makes the walking actions can be acquired again by one or more acquisition components configured by the acquisition unit, so that the analysis unit can update the digital biomarker.
According to a preferred embodiment, the input unit is capable of generating control commands after capturing and converting external commands at least when the patient is in the third movement state, wherein the external commands can comprise voice commands, operating commands and/or transmission commands.
The control command for driving the stimulation unit is generated after conversion, so that the stimulation unit can provide the control scheme matched with the external command.
According to a preferred embodiment, the analysis unit is capable of acquiring, at least based on the acceleration signal acquired by the acquisition unit, feature data corresponding to key features, which at least comprise information characterizing the first, second and/or third metric, wherein the analysis unit is capable of determining the degree of lateral involvement of the patient based on one or more metrics.
The intelligent analytical feedback system of the present invention can treat the degree of lateral involvement as a "digital biomarker associated with frozen gait" for assessing the patient's motor status. The intelligent analysis feedback system is used for obtaining the degree of the lateral disturbance based on at least comprehensive calculation of one or more measurement indexes when the degree of the lateral disturbance of the patient is evaluated, different measurement indexes can be obtained by different physiological signals through different calculation modes so as to reflect the current actual physiological state of the patient more comprehensively and accurately, so that the analysis unit can regulate and control the stimulation unit in time conveniently, and the intelligent analysis feedback system can realize effective intervention when the patient is in a first motion state and fails to separate in time.
According to a preferred embodiment, the analysis unit can take the synchronicity as a first metric, wherein the swing of any arm can be obtained as follows: and determining intersection points after intersection of straight lines formed along the extending directions of the arms respectively from the two acceleration direction switching points based on position information corresponding to two adjacent acceleration direction switching points, so as to calculate the included angle of the two straight lines at the intersection points.
The analysis unit may utilize the swing arm asymmetry index and/or the heterolateral synchronization index to characterize some or all of the first metric.
According to a preferred embodiment, the analysis unit is capable of taking the phase coordination as the second metric, wherein the average swing time of any one foot can be obtained by calculating the time required for the foot to complete any one swing based on at least the time information corresponding to the successive two adjacent acceleration direction switching points, and by taking the average of the times required for multiple swings of the same foot.
The analysis unit may utilize the gait asymmetry index and/or the phase coordination index to characterize some or all of the second metric.
According to a preferred embodiment, the analysis unit is able to take as a third measure the time difference between the point in time of the stimulus signal provision and the point in time of the patient's action performing the action matching the stimulus signal.
The analysis unit is capable of differencing the provided time point and the action time point in a manner that excludes false actions to obtain a time difference representing the third metric. Further, the third metric may be particularly useful in situations where the stimulation signal employs sensory electrical stimulation to obtain a time difference between the point in time of electrical stimulation occurrence and the point in time of patient action.
According to a preferred embodiment, the acquisition unit is configured with one or more acquisition components based on the type of signal and/or the acquisition site to be acquired, wherein the type of signal to be acquired comprises at least one or more of an electrocardiographic signal, an electromyographic signal, an electrical skin signal and an acceleration signal.
Drawings
Fig. 1 is a simplified schematic diagram of module connection relation of an intelligent analysis feedback system according to a preferred embodiment of the present invention.
List of reference numerals
100: an acquisition unit; 110: a first acquisition component; 120: a second acquisition component; 130: a third acquisition component; 140: fourth acquisition a component; 200: an analysis unit; 300: a stimulation unit; 400: an input unit.
Detailed Description
The following detailed description refers to the accompanying drawings.
FIG. 1 is a simplified schematic diagram of the module connection of a preferred intelligent analytical feedback system.
The invention discloses a digital biomarker intelligent analysis feedback system for parkinsonism patients, which can also be an intelligent rehabilitation training device for relieving frozen gait of parkinsonism patients, and is particularly suitable for rehabilitation training process of parkinsonism patients, and comprises the following steps: the system comprises an acquisition unit 100 for acquiring physiological signals related to patient movement, an analysis unit 200 for processing the acquired physiological signals to obtain digital biomarkers related to frozen gait, and a stimulation unit 300 for responding to control instructions to provide corresponding stimulation signals, wherein a patient who feels the stimulation signals provided by the stimulation unit 300 can perform actions matched with the purpose of stimulation, and further the acquisition unit 100 acquires the corresponding physiological signals again and sends the corresponding physiological signals to the analysis unit 200, so that the analysis unit 200 can perform iterative analysis on the digital biomarkers to realize prediction and assessment of the physiological state or health state of the patient, and the purpose of the stimulation is to drive the patient to perform certain actions by applying the stimulation signals. Preferably, the digital biomarker of the invention particularly refers to objective characteristic data related to frozen gait, which is obtained by collecting various data such as physiological indexes, movement states and the like of a parkinsonism patient through various intelligent devices and then analyzing and sorting the data, wherein the intelligent devices can comprise one or more of mobile phones, computers, tablets and intelligent wearable devices.
Preferably, the physiological signals acquired by the acquisition unit 100 may comprise electrocardiographic signals, electromyographic signals, skin electrical signals, and/or acceleration signals, wherein the acquisition unit 100 may be configured with one or more acquisition components based on the type of signals desired to be acquired and/or the location of the acquisition.
Preferably, the acquisition unit 100 may be configured with the first acquisition component 110 in a chest region of the patient, wherein the first acquisition component 110 may be configured to at least acquire an electrocardiographic signal of the patient, the electrocardiographic signal being bioelectrical in a varying state accompanying sequential excitation of the pacing point, atrium, ventricle of the heart of the patient in each cardiac cycle.
Preferably, the acquisition unit 100 may be provided with a second acquisition member 120 in the leg region of the patient, wherein the second acquisition member 120 may be used at least for acquiring an electromyographic signal of the patient, which is a superposition of the action potentials of the movement units in the plurality of muscle fibers in time and space, and/or a skin electrical signal representing the change in skin electrical conduction when the body is stimulated, typically expressed in terms of the resistance value and its logarithm or conductance and its square root.
Preferably, the acquisition unit 100 may be configured with a third acquisition component 130 in the arm and/or wrist region of the patient, wherein the third acquisition component 130 may be used at least for acquiring acceleration signals of the patient's hand, which may be used at least for characterizing the swing amplitude and swing speed of the patient's arm. Further, the acceleration signal is accompanied with at least position information and time information to acquire at least position information at which acceleration is maximum and minimum for a certain period of time. Preferably, when the third acquisition members 130 are disposed, a plurality of the third acquisition members 130 may be disposed along an arm extending direction, wherein the arm extending direction is a direction in which the wrist is substantially connected to the arm when the arm is not bent.
Preferably, the acquisition unit 100 may be configured with a fourth acquisition component 140 in the ankle region of the patient, wherein the fourth acquisition component 140 may be used at least for acquiring acceleration signals of the foot of the patient, which may be used at least for characterizing the stride and the pace of the patient. Further, the acceleration signal is accompanied with at least position information and time information to acquire at least position information at which acceleration is maximum and minimum for a certain period of time.
Preferably, at least one acquisition component in the acquisition unit 100 in the activated state may send the acquired physiological signal to the analysis unit 200, so that the analysis unit 200 may obtain a digital biomarker related to the frozen gait according to the occurrence number of the key feature corresponding to the physiological signal in the preset period, where the digital biomarker may be at least used to determine the movement type of the patient, and the key feature may be feature data that the data information attached to the physiological signal exceeds the set threshold after being calculated through analysis. Preferably, the type of movement of the patient comprises at least: a first motion state in which a high probability of a frozen gait occurs, a second motion state in which a frozen gait is likely to occur later, and a third motion state in which a normal gait is occurring. Preferably, the patient in the first state of motion is in need of intervention; the patient in the second movement state is in need of early warning; the patient in the third movement state is in need of recording, wherein intervention, pre-warning and/or recording operations may be initiated by the analysis unit 200 and the initiation timing may not be exactly the same as the patient state switching timing, the intervention may be by driving the stimulation unit 300 to provide a stimulation signal to act as a dynamic guide and reduce pauses during patient walking; the early warning can be to prevent the frozen gait from occurring in the subsequent development by reminding the patient to adjust the posture and/or gait as soon as possible and autonomously; the record may be that the analysis unit 200 stores the training record. Further, the analysis unit 200 may determine a generation manner and/or a generation timing of the control instruction for the stimulation unit 300 based on the determination result of the patient movement type, wherein the analysis unit 200 generates the control instruction for driving the stimulation unit 300 at least in a case where it is determined that the current patient is in the first movement state and is not out of the state in time.
Preferably, the analysis unit 200 may generate a control command for driving the stimulation unit 300 to provide the stimulation signal based on the digital biomarker related to the frozen gait obtained by processing the physiological signal acquired by the acquisition unit 100, wherein the control command is generated at least if the analysis unit 200 determines that the frozen gait of the patient is likely to occur immediately and the hidden danger is not eliminated in time.
Preferably, after providing the stimulation signal, the stimulation unit 300 may make a corresponding walking motion based on the stimulation signal, and the physiological signal of the patient when making the walking motion may be acquired again by one or more acquisition components configured by the acquisition unit 100, so that the analysis unit 200 can update the digital biomarker.
Preferably, the stimulation signal provided to the patient by the stimulation unit 300 in response to the control instructions generated by the analysis unit 200 may comprise sensory electrical stimulation, auditory sound stimulation, visual light stimulation.
Further, the sensory electrical stimulation provided by stimulation unit 300 may be generated using a voltage-controlled dual-channel stimulator, wherein a series of consecutive biphasic electrical stimulations may be generated using a fixed-rhythm cue strategy. Preferably, the stimulation unit 300 is capable of providing sensory electrical stimulation in the form of: which includes a 100ms rise time, a 500ms on time, a 100ms fall time, and a 0ms off time. Further, the intensity of the sensory electric stimulus provided by the stimulation unit 300 may be adjusted based on the sensory response of the patient, wherein the target of the adjustment of the stimulus intensity is to induce a sensory response, but not a motor response, wherein the sensory response refers to the response of the human being to receive the peripheral stimulus through the receptors, the corresponding compound sensations are combined, the external stimulus is sensed through the connection of various nerve fibers and the nerve synapses of the conductive bundle, the nerve center is conducted through the synaptic connection, the nerve center is reduced and then reacted through the effector, and the secretion of glands or the contraction of muscles are promoted to generate the response. The motor response may be a complex muscle action potential recorded on the muscle it innervates by stimulating the nerve; and/or muscle contraction responses seen upon stimulation of the nerve; and/or muscle contraction caused by the myotonic reflex. Preferably, the stimulation unit 300 may be delivered to the quadriceps muscle belly through a 5×5cm skin surface electrode using a stimulator.
Further, the auditory sound stimulus provided by the stimulation unit 300 may comprise different types of auditory stimulus patterns, wherein the auditory sound stimulus may be a beat, music, a specific sound source, etc. Illustratively, the auditory sound stimulus may be configured to: the sound of the metronome and can adjust different rhythms; music with drumming points, and can provide rhythms of various representative types as recommended music; the footsteps of walking may take place and the interval time of adjacent footsteps and/or the associated step duration may be adjusted, wherein an interval time of 50ms may be set for a footstep of walking and the associated step duration may be set to 500, 550, 600, 650, 700, 750, 800, 850 or 900ms. Preferably, the type of the acoustic source of the auditory sound stimulation provided by the stimulation unit 300 may be stored in a database, and the auditory stimulation mode with the highest matching degree may be selected from the database as the stimulation signal based on the control instruction of the analysis unit 200.
Further, the visual ray stimulus provided by the stimulation unit 300 may comprise different modes of visual stimulus forms, wherein the visual ray stimulus may be a laser. Illustratively, based on the difference in trigger forms, the visual stimulus form may have the following pattern: continuous stimulation or frozen gait trigger stimulation; based on the difference in laser forms, the visual stimulus form may have the following pattern: single line stimulation or double line stimulation. Preferably, the light source pattern of the visual ray stimulus provided by the stimulus unit 300 may be stored in a database, and the visual stimulus form having the highest matching degree may be selected from the database as the stimulus signal based on the control instruction of the analysis unit 200.
Preferably, the stimulation unit 300 is also capable of being driven based on other control instructions, i.e. control instructions not generated by the analysis unit 200. Further, other control instructions may be generated after capturing and converting the external instruction by the input unit 400, that is, the input unit 400 may extract information of a modulation scheme attached to the external instruction after capturing the external instruction, and generate a control instruction for driving the stimulation unit 300 after converting the information, so that the stimulation unit 300 may provide a modulation scheme matched to the external instruction, where the external instruction may be one or more of a voice instruction, an operation instruction, and a transmission instruction.
Preferably, when the input unit 400 captures the voice command, if the voice command includes information related to visual ray stimulation such as "laser", the input unit 400 can generate a control command for driving the stimulation unit 300 to provide visual ray stimulation; if the voice command includes information related to auditory sound stimulation such as "music", the input unit 400 can generate a control command for driving the stimulation unit 300 to provide auditory sound stimulation; if the voice command includes information related to sensory electric stimulation such as "vibration", the input unit 400 can generate a control command for driving the stimulation unit 300 to provide sensory electric stimulation. Further, the input unit 400 may be used to record the voice information of the current patient, so that the input unit 400 may distinguish the voice of the patient from the environmental sound, and capture the information of the regulation scheme attached in the voice, so as to avoid the situation that the input unit 400 captures all the voices and causes the stimulation unit 300 to be started by other voices by mistake.
Preferably, when the input unit 400 captures an operation instruction, if the operation instruction is input by pressing an identification button related to visual ray stimulation such as "laser", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide visual ray stimulation; if the operation instruction is input by pressing an identification button related to auditory sound stimulation such as "music", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide auditory sound stimulation; if an operation instruction is input by pressing an identification button associated with the tactile electric stimulus such as "shake", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide the tactile electric stimulus. Further, the pressing of the associated identification button is typically done by the patient on his own will, wherein the associated identification button may be provided in several numbers to match specific different stimulation types.
Preferably, when the input unit 400 captures the transmission instruction, if the transmission instruction is accompanied by information related to visual ray stimulation such as "laser", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide visual ray stimulation; if the transmission instruction is accompanied by information related to visual ray stimulation such as "laser", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide auditory sound stimulation; if the transmission instruction is accompanied by information related to visual ray stimulation such as "laser", the input unit 400 can generate a control instruction for driving the stimulation unit 300 to provide tactile electrical stimulation. Preferably, the transmission instruction may be generally generated and transmitted by a terminal device communicatively connected to the input unit 400, wherein the terminal device may be a mobile phone, a tablet, a computer, or the like, and the terminal device may be operated by a patient, a caregiver, and/or a medical care provider.
Preferably, the patient may choose the intervention occasion of the stimulation unit 300 when performing the rehabilitation training, i.e. the stimulation unit 300 may actively intervene in the rehabilitation training based on the subjective wishes of the user (which may comprise the patient, the accompanying person and/or the healthcare person) and/or passively intervene in the rehabilitation training based on the digital biomarkers related to frozen gait obtained by the analysis unit 200. Further, the stimulation unit 300 can at least drive the stimulation unit 300 to passively intervene in the rehabilitation training when the analysis unit 200 determines that the patient is in the first motion state and fails to be separated in time, so as to improve the training effect and accuracy.
Preferably, the stimulation unit 300 is capable of determining an initial operation scheme based on the external instructions captured by the input unit 400 and/or the control instructions generated by the analysis unit 200 based on the current patient condition when actively involved in the rehabilitation training, and the initial operation scheme may include the stimulation type provided by the stimulation unit 300 and the setting parameters corresponding to the respective stimulation type, etc. Further, as the progress of the rehabilitation training progresses, the analysis unit 200 can acquire the digital biomarker related to the frozen gait of the current patient based on the physiological signal acquired by the acquisition unit 100, so as to adjust the operation scheme of the stimulation unit 300 through the control instruction.
Preferably, during rehabilitation training, if the gait of the patient is normal, the analysis unit 200 may determine that the patient in the third movement state is in the third movement state, that is, the patient in the third movement state is not easy to generate frozen gait or the situation that frozen gait is generated is very low in a certain time range, so that the physiological signal of the patient in the third movement state acquired by the acquisition unit 100 may be used as basic data for machine learning by the analysis unit 200, thereby improving the classification accuracy of the analysis unit 200 on the movement type of the patient. Further, the analysis unit 200 only needs to record when the patient is in the third movement state, wherein the input unit 400 can capture the external instructions only when the patient is in the third movement state in general. Further, if the analysis unit 200 cannot provide the correct control instruction in time, the input unit 400 may convert the captured external instruction to generate the control instruction in a permission transferring manner, where the permission is preferentially transferred to the terminal device operated by the medical staff, that is, in the above case, the medical staff may transmit the external instruction for generating the control instruction to the input unit 400 through the terminal device thereof.
Preferably, during rehabilitation training, the analysis unit 200 may set a plurality of numerical thresholds and/or a number of times thresholds for the data information attached to the physiological signal, for judging the non-third movement state (i.e., the first movement state and the second movement state), wherein the numerical threshold and/or the number of times threshold for judging the second movement state is smaller than the numerical threshold and/or the number of times threshold for judging the first movement state. Typically, after the analysis unit 200 gives an early warning to the patient in the second movement state, the patient can switch to the third movement state by adjusting the posture and/or gait, but may also develop into the first movement state. Further, the analysis unit 200 starts timing when it is determined that the patient is in the first movement state, and in the case that the duration of the timing exceeds a set time limit, the analysis unit 200, i.e. the driving stimulation unit 300, generates a stimulation signal for intervention, wherein the set time limit may be 3 seconds.
Preferably, the analysis unit 200 may have different key feature discriminants based on different types of physiological signals acquired by the acquisition unit 100 to acquire different types of "digital biomarkers related to frozen gait".
Preferably, for the electrocardiographic signals, the characteristic data corresponding to the key characteristics may include an average heart rate and a heart rate variability, and may be calculated from raw electrocardiographic data acquired by the first acquisition component 110, where each acquisition time window is 3 seconds, two acquisition windows overlap by 0.5 seconds, and the average heart rate and heart rate variability of each acquisition time window is calculated from the raw ECG data to predict occurrence of a frozen gait.
Preferably, for skin electrical signals, the feature data corresponding to the key features may comprise a mean value of skin conductance (C mean ) Median (C) median ) And standard deviation (C) std ) The acquisition data that can be acquired from the second acquisition component 120 is calculated.
Preferably, for the acceleration signal, the feature data corresponding to the key feature may include a plurality of metrics, and the analysis unit 200 may determine a lateral involvement degree of the patient based on a comprehensive calculation of the plurality of metrics, wherein the lateral involvement degree may represent a severity of gait disturbance of the parkinson patient.
Preferably, the analysis unit 200 may take the synchronicity as a first metric, wherein the synchronicity index may be characterized by one or both of the swing arm asymmetry index and the heterolateral synchronicity index.
Preferably, the swing of either arm can be obtained as follows: and determining intersection points after intersection of straight lines formed along the extending directions of the arms respectively from the two acceleration direction switching points based on position information corresponding to two adjacent acceleration direction switching points, so as to calculate the included angle of the two straight lines at the intersection points. Further, the position information corresponding to two consecutive adjacent acceleration direction switching points may be acquired by the third acquisition component 130. Further, the analysis unit 200 may be set as a main arm and a sub-arm for the left and right arms of the patient, respectively, wherein the setting manner is: the swing amplitudes of the left arm and the right arm calculated based on the acceleration information acquired by the third acquisition unit 130 are set to the main arm on the side arm with the relatively larger swing amplitude, and the sub-arm on the side arm with the relatively smaller swing amplitude.
Preferably, the analysis unit 200 may calculate the swing arm asymmetry index by dividing the difference value obtained by subtracting 45 ° from the arctangent function value of the quotient of the main arm swing and the auxiliary arm swing by the absolute value of the quotient obtained by dividing 90 ° by the difference value, which is the swing arm asymmetry index. In other words, the swing arm asymmetry index obtained by the above-described calculation method may represent part or all of the first metric.
Preferably, the analysis unit 200 calculates the ipsilateral synchronization index, that is, the degree of synchronization between the motion mode of one arm and the motion mode of the foot opposite to the other arm in the natural walking process of the human being, if the synchronization is lacking or is low, it indicates that gait disorder may be caused, that is, the average time interval between the occurrence moments of the acceleration direction switching points of the two acceleration direction switching points is calculated by comparing the acceleration signals of the ipsilateral arm and the foot acquired by the third acquisition component 130 and the fourth acquisition component 140. In other words, the heterolateral synchronization index obtained by the above calculation may represent part or all of the first metric.
Preferably, the analysis unit 200 may serve as a second metric of phase coordination, wherein phase coordination may be characterized by one or both of the gait asymmetry index and the phase coordination index.
Preferably, the average swing time of any one foot can be obtained by calculating the time required for the foot to complete any one swing based on at least the time information corresponding to the successive two adjacent acceleration direction switching points acquired by the fourth acquisition unit 140, and by taking an average of the times required for a plurality of swings of the same foot. Further, the analysis unit 200 may be configured as a main foot and a sub foot for the left and right feet of the patient, respectively, wherein the configuration is as follows: the average swing time of the left and right feet calculated based on the acceleration information acquired by the fourth acquisition unit 140 is set to the main foot on the side of the relatively longer average swing time, and the auxiliary foot on the side of the relatively shorter swing.
Preferably, the analysis unit 200 may calculate the gait asymmetry index by calculating an absolute value of the natural logarithm of the quotient of the average swing time of the subsidiary foot and the average swing time of the main foot, which can be amplified by several times (i.e., multiplied by a set constant coefficient) to obtain the gait asymmetry index. In other words, the gait asymmetry index obtained by the above-described calculation may represent part or all of the second metric.
Preferably, the analysis unit 200 calculates the phase coordination index by calculating the phase value of the auxiliary foot using the main foot as the reference standard of the gait cycle, and further calculating the phase coordination index in all the strides of the patient, wherein the phase value is the phase of the ith stride length obtained by normalizing the stride length with respect to the stride length. Further, the phase value may be obtained by multiplying 360 ° by a quotient obtained by dividing a time difference between the main foot and the sub foot landed at the i-th step by a time difference between the sub foot landed at the i+1th step and the i-th step. Further preferably, based on the calculation of the phase value, a phase coordination index, i.e. the absolute value of the difference between the phase and the flat angle is obtained after amplification by several times (i.e. multiplication by a set constant coefficient). In other words, the phase coordination index obtained by the above calculation may represent part or all of the second metric.
Preferably, the analysis unit 200 may take the time difference between the stimulus signal providing time point and the action time point of the patient performing the action matching the stimulus signal as the third metric. Further, the stimulation unit 300 can feed back the supply time point of the stimulation signal to the analysis unit 200 when any one of the stimulation signals is supplied, so that the analysis unit 200 can perform recording. Further, the acquisition unit 100 can send an action time point at which the patient performs an action matching the stimulus signal to the analysis unit 200, wherein the analysis unit 200 can make a difference between the providing time point and the action time point in such a way that malfunction is excluded, to obtain a time difference characterizing the third metric.
Further, the third metric is particularly useful in cases where the stimulation signal employs sensory electrical stimulation to obtain a time difference between the point in time of occurrence of the electrical stimulation and the point in time of patient action.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents. The description of the invention includes various inventive concepts such as "preferably," "according to a preferred embodiment," or "optionally," all means that the corresponding paragraph discloses a separate concept, and the applicant reserves the right to filed a divisional application according to each inventive concept. Throughout this document, the word "preferably" is used in a generic sense to mean only one alternative, and not to be construed as necessarily required, so that the applicant reserves the right to forego or delete the relevant preferred feature at any time.
Claims (8)
1. A digital biomarker intelligent analysis feedback system for parkinson's disease patients, comprising:
an acquisition unit (100) for acquiring one or more types of physiological signals of a patient;
an analysis unit (200) for processing the acquired physiological signals to obtain digital biomarkers related to frozen gait;
a stimulation unit (300) for providing a stimulation signal to the patient;
it is characterized in that the method comprises the steps of,
the analysis unit (200) can generate a control instruction for regulating the stimulation unit (300) based on the obtained digital biomarker related to frozen gait, the physiological signals acquired by the acquisition unit (100) are acceleration signals of arms and feet, the analysis unit (200) can take the synchronism represented by the swing arm asymmetry index and/or the heterolateral synchronism index as a first metric index, the phase coordination represented by the gait asymmetry index and/or the phase coordination index as a second metric index, the time difference between the time point of providing the stimulation signal and the action time point of the patient executing the action matched with the stimulation signal as a third metric index, the degree of lateral involvement is obtained through the first metric index, the second metric index and/or the third metric index to be used as the digital biomarker related to frozen gait,
The stimulation signals provided by the stimulation unit (300) in response to the control instructions can drive the patient to perform actions matching the stimulation purposes of the control instruction map, wherein the analysis unit (200) generates corresponding control instructions at least according to the current motion state of the patient, and the digital biomarkers related to frozen gait can be used for judging the type of the current motion state of the patient.
2. The intelligent analysis feedback system according to claim 1, wherein the analysis unit (200) is capable of obtaining the digital biomarker related to the frozen gait according to the occurrence number of key features of the physiological signal in a preset period, wherein the key features are feature data of which the data information attached to the physiological signal exceeds a set threshold after being analyzed and calculated.
3. The intelligent analysis feedback system according to claim 1, characterized in that the analysis unit (200) is capable of dividing at least the type of the current movement state of the patient into a first movement state which can be intervened by the stimulation unit (300), a second movement state which can be pre-warned by the analysis unit (200) and a third movement state which can be recorded by the analysis unit (200), wherein the analysis unit (200) is capable of driving the stimulation unit (300) to intervene at least when the patient is in the first movement state and does not deviate within a set time limit.
4. The intelligent analysis feedback system of claim 1, wherein the stimulation unit (300) is configured to apply one or more of sensory electrical stimulation, auditory sound stimulation, visual light stimulation to the patient based on control instructions, wherein the control instructions to which the stimulation unit (300) is responsive are configured to be sourced from the analysis unit (200) and/or input unit (400).
5. The intelligent analytical feedback system according to claim 4, wherein the input unit (400) is capable of capturing and converting external instructions, which may include voice instructions, operating instructions and/or transmission instructions, at least when the patient is in the third motion state, to generate control instructions.
6. The intelligent analytical feedback system of claim 1 wherein the swing of either arm is obtainable in the following manner: determining intersection points after intersection of straight lines formed along respective arm extending directions from the two acceleration direction switching points respectively based on position information corresponding to two adjacent acceleration direction switching points, so as to calculate an included angle of the two straight lines at the intersection points; and respectively calculating swing amplitude of the left arm and the right arm, setting one side arm with a relatively larger swing amplitude as a main arm, setting one side arm with a relatively smaller swing amplitude as an auxiliary arm, wherein the swing arm asymmetry index is an absolute value of a quotient obtained by dividing a difference value obtained by subtracting a quotient arctangent function value of the swing amplitude of the main arm and the swing amplitude of the auxiliary arm by 45 degrees by 90 degrees.
7. The intelligent analysis feedback system according to claim 1, wherein the average swing time of any one foot can be obtained by calculating the time required for the foot to complete any one swing based on at least the time information corresponding to the successive two adjacent acceleration direction switching points, and by taking the average of the times required for a plurality of swings of the same foot; and respectively calculating the average swing time of the left foot and the right foot, setting one side foot with relatively longer average swing time as a main foot, setting one side foot with relatively shorter swing amplitude as an auxiliary foot, and obtaining the gait asymmetry index by multiplying the absolute value of the natural logarithm of the quotient of the average swing time of the auxiliary foot and the average swing time of the main foot by the set constant coefficient.
8. The intelligent analysis feedback system according to claim 1, characterized in that the acquisition unit (100) is configured with one or more acquisition components based on the type of signal required to be acquired and/or the difference in acquisition sites.
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