Device and method for reducing symptoms of neurological movement disorders using wearable device

Abstract

To provide a medical wearable device for alleviating symptoms of neurological movement disorders.SOLUTION: The multimodal wearable band 11 stimulates sensory neurons of the wrist or ankle using mechanical vibration to reduce the severity of tremor, rigidity, involuntary muscle contractions, and bradykinesia caused by neurological movement disorders. The device reduces the severity of the user's symptoms using the sensor 1107 that provides an output that is used by the processing unit 1101 to determine the user's stimulation pattern and to determine when the stimulation 13 is needed, and then using the one or more mechanical transducers 1105 to stimulate the user's neural pathways 1203 correspondingly. The device may also be adapted to integrate with third party devices.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a medical wearable device, and more particularly to a medical wearable device for alleviating the symptoms of neurological movement disorders.

Background Art

[0002] There are several neurological movement disorders that exhibit a range of somewhat similar symptoms, examples of which are shown below. Essential tremor is characterized by tremors in the limbs. Parkinson's disease (PD) can cause tremors, rigidity, bradykinesia, and sometimes freezing or an inability to start moving. Restless legs syndrome does not cause tremors, but causes a strong urge to move and shake the patient's legs. Tremors can also exist as a side effect of certain medications.

[0003] Today, there are approximately 10 million people worldwide suffering from Parkinson's disease (PD), and more than 70% of these patients experience tremors, that is, involuntary shaking or trembling of the limbs. Other symptoms of PD include muscle stiffness or rigidity, bradykinesia (defined as slowness of movement), and freezing (defined as a temporary and involuntary inability to move).

[0004] There is no cure for Parkinson's disease. Current treatments consist of medications to address the patient's symptoms, but these do not reverse the effects of the disease. Patients often take various medications at different doses and at different times to manage their symptoms. PD medications are mostly dopaminergic and supply dopamine or mimic the effects of dopamine to replenish the depleted dopamine state caused by the disease.

[0005] Surgery may be prescribed for patients who have exhausted other medical treatment options. The first method of surgical treatment is deep brain stimulation (DBS). In this procedure, electrodes are inserted into the brain, and then an impulse generator battery is implanted under the collarbone or in the abdomen. The patient can turn the device on or off as needed using a controller to assist with tremor control. DBS can be effective for both Parkinson's disease and essential tremor, but this procedure is invasive and expensive.

[0006] A second surgery available to Parkinson's disease patients is Duopa therapy. With Duopa therapy, a small hole (stoma) needs to be surgically created in the stomach to place a tube in the intestine. Duopa, which is similar to the regular PD medications taken through tablets, is then pumped directly into the intestine, improving absorption and reducing the off time of the medications taken by tablets.

[0007] Similar disorders called essential tremors are often misdiagnosed as Parkinson's disease, but have a higher incidence, with an estimated 100 million cases worldwide. These tremors often occur during intentional movement and can worsen to the point where the patient loses the ability to cut food, tie a shoelace, or sign their name. Medications for essential tremor can include beta blockers and anti-seizure medications. These medications are known to cause fatigue, heart problems, and nausea.

[0008] Multiple sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system, affecting an estimated 2.8 million people worldwide. Motor symptoms include weakness or fatigue, difficulty walking, stiffness or spasticity, and tremors. Most people experience a so-called "relapsing-remitting" disease course, which includes periods of new or recurrent symptoms followed by periods of recovery. As the disease progresses, symptom onset may follow a regular pattern, after which the disease is classified as "secondary progressive." Some MS patients experience a progressive onset and progression without any relapsing-remission, which is classified as "primary progressive." There is no cure for MS, and treatment typically focuses on recovery from relapses using physical therapy and medication. Disease-modifying drugs reduce the incidence of relapses but do not help relieve symptoms during a relapse.

[0009] Restless legs syndrome (RLS) affects approximately 10% of the population in the United States. RLS can also be a side effect of primary Parkinson's disease. RLS is characterized by unpleasant creeping sensations in the legs of the patient. These sensations occur when the legs are at rest and are relieved when the legs are in motion. As a result, RLS patients are compelled to move or shake their legs. This is particularly harmful to the quality of the patient's sleep because the patient cannot continue to stay still.

[0010] RLS is generally treated by dietary changes, medication, and / or physical therapy. Dietary changes may include the removal of caffeine, alcohol, and tobacco. Drugs prescribed for RLS may include the same types of drugs prescribed for Parkinson's disease (such as dopamine agonists and carbidopa-levodopa) and benzodiazepines (such as lorazepam, Xanax, Valium, and Ativan). Physical therapy for RLS may include massage of the legs or electrical or vibratory stimulation.

[0011] An example of a device that uses vibration to treat RLS is described in U.S. Patent Application Publication No. 20100249637, "Systems, devices, and methods for treating restless leg syndrome and periodic limb movement disorder," Walter, T.J., & Marar, U. (2010). This device is a calf sleeve with sensors and actuators, but it does not store or transmit data and does not address any of the other symptoms common to neurological movement disorders.

[0012] There are several pharmaceutical means for managing neurological movement disorders that function by promoting dopamine, a brain-generated chemical that helps control body movement. This chemical is lacking in the brains of patients with diseases such as Parkinson's disease. Pharmaceutical treatments for Parkinson's disease are expensive, costing users thousands of dollars per year; they may be ineffective or lose their effectiveness quickly; and they are thought to actually promote neurodegeneration.

[0013] There are also injection-based Botox treatments for more severe tremors, which cost tens of thousands of dollars per year and function by killing the nerves that cause the tremors. This is effective in reducing tremors, but the death of the nerves also causes a significant loss of motor function. Additionally, this treatment is only available at very specialized treatment centers and is therefore not an option for the majority of patients.

[0014] There are several devices that attempt to control unwanted movement using surface-based treatments, but none have been proven to be completely non-invasive and effective. Many people have turned to electrical stimulation as a form of nerve stimulation to reduce unwanted movement. This can involve various devices such as gel pads or electrodes that require shaving for proper attachment and inconvenient procedures. See U.S. Patent No. 8,762,065, "Closed-loop feedback-driven neuromodulation," DiLorenzo, D.J. (2014). These devices function only after the electrical treatment has ended and have not been shown to have long-lasting effects, leading to the assumption that many of these inconvenient procedures must be administered throughout the day to maintain tremor reduction. See U.S. Patent No. 9,452,287, "Devices and methods for controlling tremor," Rosenbluth, K.H., Delp, S.L., Paderi, J., Rajasekhar, V., & Altman, T. (2016); U.S. Patent No. 9,802,041, "Systems for peripheral nerve stimulation to treat tremor," Wong, S.H., Rosenbluth, K.H., Hamner, S., Chidester, P., Delp, S.L., Sanger, T.D., & Klein, D. (2017); U.S. Patent No. 10,905,879, "Methods for peripheral nerve stimulation," Wong, S.H., Rosenbluth, K.H., Hamner, S., Chidester, P., Delp, S.L., Sanger, T.D., & Klein, D. (2020). The aforementioned treatments pose significant risks to patients with pacemakers and have also been found to cause skin irritation. The aforementioned treatments provide benefit only for tremors and do not provide relief from other symptoms of neurological movement disorders such as bradykinesia, gait freezing, dystonia, dyskinesia, or involuntary or compulsive rhythmic movements.

[0015] Perhaps the most relevant research has emerged in recent years, demonstrating that the use of vibration can improve motor performance. There seems to be a great deal of variability in effectiveness, which depends on the frequency of vibration and the patient's condition. Macerollo et al. demonstrated in "Effect of Vibration on Motor Performance: A new Intervention to Improve Bradykinesia in Parkinson’s Disease?", Macerollo A, et al., (2016), Neurology Apr 2016, 86(16 Supplement) P5.366, that peripheral tactile vibration at 80 Hz can lead to slowness and reduction of repetitive hand movements. For patients after stroke, 70 Hz has been proven effective. This has been demonstrated by Conrad MO et al. in two separate papers: "Effects of wrist tendon vibration on arm tracking in people poststroke", Conrad MO, Scheidt RA, Schmit BD (2011), J Neurophysiol, 2011;106(3):1480-8, and "Effect of Tendon Vibration on Hemiparetic Arm Stability in Unstable Workspaces", Conrad MO, Gadhoke B, Scheidt RA, Schmit BD (2015), PLoS ONE 10(12):e0144377. Even paralyzed muscles have been shown to respond to frequencies of 150 - 160 Hz, and the effects of such vibration are seen in the persistent reduction of weakness and spasticity in the treated muscles. See "The effects of muscle vibration in spasticity, rigidity, and cerebellar disorders", Hagbarth, K.E., & Eklund, G. (1968), Journal of neurology, neurosurgery, and psychiatry, 31(3), 207-13.As shown in the following three separate studies, vibration can even have a positive effect on contraction or rigidity by using vibratory tactile stimulation to increase relaxation: "Joint mobility changes due to low frequency vibration and stretching exercise", Atha J, Wheatley DW, British Journal of Sports Medicine 1976;10:26-34, "Vibration Effects on Three Measures of Relaxation", Johnson, M.D., Hensel, C.L., & Matheson, D.W. (1982), Perceptual and Motor Skills, 54(3_suppl), 1071-1076, and "Relaxation measured by EMG as a function of vibrotactile stimulation", Matheson, D.W., Edelson, R., Hiatrides, D. et al. (1976), Biofeedback and Self-Regulation 1, 285-292. There is one device that provides a tactile signal around the user's wrist using actuators arranged along a band. These actuators slide along the band and change positions relative to each other to provide the signal in the correct position. See U.S. Patent Application Publication No. 20180356890 "Wearable device", Zhang, Haiyan, Helmes, John Franciscus Marie, Villar, Nicolas (2018).

Prior Art Documents

Patent Documents

[0016]

Patent Document 1

Patent Document 2

Patent Document 3

[0017] [Non-Patent Document 1] “Effect of Vibration on Motor Performance: A new Intervention to Improve Bradykinesia in Parkinson’s Disease?”, Macerollo A, et al., (2016), Neurology Apr 2016, 86 (16 Supplement) P5.366. [Non-Patent Document 2] “Effects of wrist tendon vibration on arm tracking in people poststroke,” Conrad MO, Scheidt RA, Schmit BD (2011), J Neurophysiol, 2011;106(3):1480-8 [Non-Patent Document 3] “Effect of Tendon Vibration on Hemiparetic Arm Stability in Unstable Workspaces,” Conrad MO, Gadhoke B, Scheidt RA, Schmit BD (2015), PLoS ONE 10(12):e0144377。 [Non-Patent Document 4] “The effects of muscle vibration in spasticity, rigidity, and cerebellar disorders,” Hagbarth, K.E., & Eklund, G.(1968), Journal of neurology, neurosurgery, and psychiatry, 31(3), 207-13 [Non-Patent Document 5] “Joint mobility changes due to low frequency vibration and stretching exercise,” Atha J, Wheatley DW, British Journal of Sports Medicine 1976; 10:26-34 [Non-Patent Document 6] “Vibration Effects on Three Measures of Relaxation,” Johnson, M.D., Hensel, C.L., & Matheson, D.W.(1982), Perceptual and Motor Skills, 54(3_suppl), 1071-1076 [Non-Patent Document 7] “Relaxation measured by EMG as a function of vibrotactile stimulation,” Matheson, D.W., Edelson, R., Hiatrides, D. et al.(1976), Biofeedback and Self-Regulation 1, 285-292 [Summary of the Invention] [Problems to be Solved by the Invention]

[0018] Therefore, there is a need for a device that non-invasively, reliably, and inexpensively alleviates the symptoms of neurological movement disorders. [Means for Solving the Problems]

[0019] According to one embodiment of the present invention, a wearable device is provided for adjusting a set of movement disorder symptoms of a subject. The device includes a housing and an attachment system configured to be attached to a body part of the subject. The device further includes a set of body part sensors for providing a set of sensor outputs related to the movement of the body part, and a set of mechanical transducers coupled to the attachment system and configured to provide a set of mechanical outputs to the body part. The device also includes a processing unit, and the processing unit includes: (i) an input for receiving body part movement data, operably coupled to the sensor outputs; (ii) a noise filtering process for removing noise unrelated to the movement disorder symptoms from the body part movement data to generate a filtered movement signal; (iii) a filtered movement signal feature extraction process for characterizing the features of the filtered movement signal to generate a characterized filtered movement signal; and (iv) a stimulation process for the characterized filtered movement signal to generate a stimulation signal output, such output being operably coupled to the set of mechanical transducers and having a stimulation process that causes the transducers to provide a mechanical stimulation to the body part to relieve the set of movement disorder symptoms.

[0020] Alternatively or additionally, the processing unit is further configured to provide active noise cancellation by: (a) converting the body part movement data in the time domain to frequency domain data; (b) determining a fundamental frequency of the movement disorder symptoms using the frequency domain data; and (c) generating a stimulation signal output having a desired phase shift relative to the phase of the body part movement data at the fundamental frequency based on the body part movement data.

[0021] In other embodiments, the processing unit is further configured to provide a sequence of stimulation signals in the stimulation signal output, each signal in the sequence having a distinct set of parameters related to the alleviation of a set of dyskinesia symptoms, the feature extraction process including determining displacement or power data related to the movement of a body part, and the processing unit is also configured to use the displacement or power data to determine which stimulation signal in the sequence has the greatest alleviation effect. In a preferred embodiment, the processing unit is further configured to select the stimulation signal determined to have the greatest alleviation effect with respect to the continuous output to the transducer.

[0022] Optionally, the set of dyskinesia symptoms is selected from the group consisting of tremors, contractions, bradykinesia, dyskinesia, the urge to move, and combinations thereof. Optionally, the processing unit is further configured to detect the shuffling gait of a Parkinson's disease patient. Alternatively or additionally, the processing unit is further configured to control a set of mechanical transducers to alleviate the shuffling gait of a Parkinson's disease patient by controlling the set of mechanical transducers. Optionally, the processing unit operates in two modes: a first mode configured to passively monitor the patient's movement to detect dyskinesia symptoms exceeding a threshold, and a second mode configured such that after detection of such dyskinesia symptoms, the processor enters active alleviation of the dyskinesia symptoms.

[0023] Optionally, the attachment system includes a wristband, and the set of mechanical transducers is distributed around the circumference of the wristband. Optionally, the device is operated by buttons on the surface of the device, and the buttons are configured to be user-friendly for patients whose fine motor control is affected by neurological movement disorders. Optionally, the wristband is configured with a hook-and-loop fastener so that a person with fine motor control affected by neurological movement disorders can fasten the wristband with one hand. Optionally, the wristband is configured to be extensible via elastic deformation so that it is user-friendly for a person with fine motor control affected by neurological movement disorders. Optionally, the device further includes a battery disposed in the housing and a magnetic connector mounted in the housing for coupling to the battery and for coupling to a mating connector for an external charger so that the battery can be conveniently configured for charging by a patient lacking fine motor control.

[0024] Optionally, the processing unit is further configured to store the body part movement data in a memory coupled to the processing unit.

[0025] Optionally, the active noise cancellation processor is configured to convert the sensor output to frequency data by applying a Fourier transform to the sensor output. Alternatively or additionally, the active noise cancellation processor is configured to (i) select a fundamental frequency by applying an argmax function to the converted sensor output and (ii) use a bandpass filter to remove a set of frequency data outside a specified range associated with the fundamental frequency from the stimulus signal.

[0026] Optionally, the set of body part sensors includes an inertial motion unit (IMU) configured to calculate data representing the acceleration of the body part, and the active noise cancellation processor applies a Fourier transform to convert the acceleration data of the body part into frequency data, extracts the peak frequency of the acceleration data of the body part from the frequency data, selects a window size of the acceleration data of the body part based on the peak frequency, captures a portion of the sensor output based on the selected window size, and is further configured to generate a stimulation signal by inverting the captured portion. Alternatively or additionally, the active noise cancellation processor is configured to invert the lowest peak frequency among the peak frequencies and select a window size by converting the inverted lowest peak frequency among the peak frequencies into the time domain. Optionally or additionally, the active noise cancellation processor is configured to set the window size to a fixed value.

[0027] According to an embodiment of the present invention, a method for alleviating a set of movement disorder symptoms of a subject is provided. The method includes sensing movement of a body part of the subject and providing a set of sensor outputs related to the movement of the body part. The method also includes processing the sensor output to generate a stimulation signal for alleviating the set of movement disorder symptoms. The processing includes filtering the sensor output to remove noise unrelated to the movement disorder symptoms so as to generate a filtered signal.

[0028] Optionally, the processing also actively processes the filtered signal to (a) convert the sensor output into frequency data, (b) use the frequency data to determine a fundamental frequency of the movement disorder symptoms, (c) generate a stimulation signal by processing the sensor output based on the fundamental frequency, and (d) apply a time delay calculated based on the fundamental frequency to the stimulation signal. The method further includes inputting the stimulation signal to a set of mechanical transducers coupled to the body part so as to alleviate the set of movement disorder symptoms.

[0029] Alternatively or additionally, the processing unit comprises a fixed-frequency processor configured to (a) continuously input a set of stimulus signals with varying frequencies to a set of mechanical transducers, (b) convert the sensor output into displacement or power data, (c) use the displacement or power data to determine which stimulus signal results in the lowest displacement, and (d) transmit the stimulus signal to the set of mechanical transducers. Optionally, the processing unit is configured to receive an input from a user and use the input to control the stimulus signal.

[0030] Optionally, the set of dyskinesia symptoms is selected from the group consisting of tremors, contractions, bradykinesia, dyskinesia, the urge to move, and combinations thereof. Optionally, sensing movement of a body part includes operating in two modes: a first mode of passively monitoring the patient's movement to detect dyskinesia symptoms exceeding a threshold, and a second mode of entering into active mitigation of the dyskinesia symptoms after detection of such dyskinesia symptoms. Optionally, actively processing the filtered signal to convert the sensor output into frequency data includes applying a Fourier transform to the sensor output. Alternatively or additionally, actively processing the filtered signal to convert the sensor output into frequency data includes (i) selecting a fundamental frequency by applying an argmax function to the converted sensor output, and (ii) using a bandpass filter to remove a set of frequency data outside a specified range related to the fundamental frequency from the stimulus signal.

[0031] Optionally, processing the sensor output to generate a stimulation signal further includes calculating data representing the acceleration of the body part, applying a Fourier transform to convert the acceleration data of the body part into frequency data, extracting the peak frequency of the acceleration data of the body part from the frequency data, selecting a window size of the acceleration data of the body part based on the peak frequency, capturing a portion of the sensor output based on the selected window size, and inverting the captured portion to generate a stimulation signal. Alternatively or additionally, selecting the window size includes inverting the lowest peak frequency among the peak frequencies and converting the inverted lowest peak frequency among the peak frequencies into the time domain. Alternatively or additionally, selecting the window size includes setting the window size to a fixed value.

[0032] According to another embodiment of the present invention, a method for alleviating a set of movement disorder symptoms of a subject is provided. The method includes monitoring the sensor output of a sensor configured to sense the movement of a body part of the subject and provide a set of sensor outputs related to the movement of the body part, processing the sensor output by filtering the sensor output to remove noise not related to the movement disorder symptoms to generate a filtered signal, and generating a stimulation signal for alleviating the set of movement disorder symptoms, and using a fixed-frequency processor configured to (a) continuously input a set of stimulation signals with changing frequencies to a set of mechanical transducers, (b) convert the sensor output into displacement or power data, and (c) output the stimulation signal that provides the maximum alleviation effect.

[0033] In some embodiments, the processor is further configured to use the displacement or power data to select the stimulation signal that provides the maximum alleviation effect. In some embodiments, the processor is further configured to receive an input from a user and select the stimulation signal that provides the maximum alleviation effect.

[0034] According to some embodiments, a method for diagnosing a patient's movement disorder, comprising: (1) Using a processor to provide an output of a sequence of stimulation signals with continuously changing stimulation parameters to a set of mechanical transducers to induce symptoms of movement disorder; (2) Receiving body part movement data related to the movement of a body part from a set of body part sensors; (3) Filtering the body part movement data to remove noise unrelated to movement disorder symptoms to generate a filtered signal; (4) Actively processing the filtered signal to (a) convert the sensor output to frequency data and (b) use the frequency data to determine the fundamental frequency of the movement of the body part; (5) Further processing the frequency data to determine the probability that the subject suffers from a movement disorder. A method is provided.

[0035] The foregoing features of the embodiments will be more readily understood by reference to the following detailed description taken in conjunction with the accompanying drawings.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0037] Definitions. As used in this specification and the appended claims, unless the context requires otherwise, the following terms shall have the meanings set forth below.

[0038] "Set" includes at least one member.

[0039] "Body part" means a part of the human body such as the hands and feet (e.g., arms, legs, ankles, wrists, etc.) or the neck.

[0040] "Body part sensor" is a sensor that responds to parameters associated with a body part, where the parameters are selected from the group consisting of force, movement, position, EMG signals directed at a set of muscles of the body part, and combinations thereof.

[0041] "Mechanical transducer" is a device having an electrical input and a mechanical output configured to provide a physical stimulus to a target.

[0042] "Movement disorder sensor" is a sensor configured to provide measurements related to neurological movement disorders.

[0043] "Attachment system" is a system or device having means for mechanically fixing a component subsystem to a user's body.

[0044] "Housing" is a primary sealed casing that houses one or more component subsystems.

[0045] "Band" is a flexible segment of material that surrounds a body part or a portion of a body part for the purpose of fixation and that can also house one or more component subsystems.

[0046] The term "vibration stimulus" refers to vibrations or a series of vibrations generated by a vibration motor or a group of vibration motors incorporated in a device. These vibrations are used to stimulate a response from target proprioceptors within the user's body.

[0047] The term "stimulation pattern" refers to a vibration stimulus characterized by several parameters including frequency, amplitude, and waveform. The "stimulation pattern" may also refer to the behavior on a longer time scale where the above parameters change over time.

[0048] The term "proprioceptive sensation" refers to the sensation of the position of one's own limbs or body parts and the intensity of the force applied through that body part. Proprioceptors are the sensory neurons used for proprioceptive sensation. There are two types of proprioceptors: the "muscle spindle" located in the muscle and the "Golgi tendon organ" located in the tendon.

[0049] The term "neurological movement disorder" refers to any neurological condition that causes abnormal increases or decreases in movement, which can be voluntary or involuntary. These include, but are not limited to, ataxia, cervical dystonia, chorea, dystonia, functional movement disorder, Huntington's disease, multiple system atrophy (MSA), hemiparesis, hemiplegia, quadriparesis, post-stroke movement disorder, myoclonus, Parkinson's disease (PD), parkinsonism, drug-induced parkinsonism (DIP), progressive supranuclear palsy (PSP), restless legs syndrome (RLS), tardive dyskinesia, Tourette syndrome, spasticity, contracture, bradykinesia, tremor, essential tremor (ET), alcohol or drug withdrawal-induced tremor, drug-induced tremor, psychogenic tremor, rest tremor, action tremor, cerebellar lesion, rubral tremor, isometric tremor, task-specific tremor, orthostatic tremor, intention tremor, postural tremor, periodic limb movement disorder, and Wilson's disease.

[0050] The term "training period" refers to the period or stage of operation of the device during which the device is conducting an experiment or collecting and analyzing data for the purpose of estimating the optimal stimulation pattern.

[0051] A "computer process" is the execution of the described functions in a computer system that uses computer hardware (e.g., a processor, a field programmable gate array or other electronic combinational logic, or similar devices), and may operate under the control of software or firmware, or a combination of either, or may operate outside the control of any of the above. All or part of the described functions may be executed by active or passive electronic components such as transistors or resistors. When using the term "computer process", it is not necessarily a schedulable entity, or requires the operation of a computer program or a part thereof, but in some embodiments, a computer process may be implemented by such a schedulable entity, or the operation of a computer program or a part thereof. Further, unless the context specifically requires, a "process" may be implemented using two or more processors or two or more (single or multi-processor) computers.

[0052] Treatment wearable device. The present invention generally relates to medical wearable devices, and in particular to the alleviation of tremors, muscle contractions, bradykinesia, involuntary dyskinetic movements, and freezing related to neurological movement disorders by mechanical vibration stimulation of the wrist tendon bundles and autonomous sensing, feedback, and adjustment. Embodiments of the device also have several considerations that facilitate ease of use by people with the disorders contemplated by the present invention, including integration with third party devices.

[0053] Embodiments of the present invention include systems and methods for treating symptoms of neurological movement disorders by stimulating proprioceptors. In some embodiments, the system is a wearable device. In some embodiments, the systems and methods can be used for any neurological movement disorder including, but not limited to, Parkinson's disease, essential tremor, post-stroke movement disorder, or restless legs syndrome. In some embodiments, the symptoms to be treated include tremors, contractions, bradykinesia, rigidity, hemiplegia, and freezing. In some embodiments, the symptoms to be treated include muscle contractions caused by dystonia. In some embodiments, the symptoms to be treated include the inability to identify the position of one's own limbs in space. In some embodiments, the proprioceptors targeted for stimulation are located in the wrist. In some embodiments, the proprioceptors targeted for stimulation are located in the ankle. In some embodiments, the proprioceptors targeted for stimulation are located in the neck.

[0054] In some embodiments, the system provides stimulation to proprioceptive nerves (proprioceptors) to reduce symptoms by using a vibration motor disposed around the surface of the wrist. In some embodiments, the system repeatedly adjusts the frequency pattern and waveform of the stimulation to find the pattern that results in the greatest reduction of movement disorder symptoms. In some embodiments, the system uses sub-threshold random white noise stimulation to utilize the effect of sensory stochastic resonance. In some embodiments, the system is coupled to one or more sensors that measure the user's tremor for each of a set of possible stimulation patterns, and the system assigns the stimulation pattern associated with the greatest measured reduction in the user's tremor amplitude relative to the tremor exhibited in the absence of stimulation.

[0055] In some embodiments, the device finds (learns) optimal stimulation parameters for use in reducing symptoms by using sensor-based optimization including, but not limited to, model-free reinforcement learning, genetic algorithms, Q-learning. These parameters can include any quantity used to define a stimulation waveform such as frequency, amplitude, phase, duty cycle, etc. In some embodiments, these learned parameters also describe the behavior of a time-varying stimulation pattern over a longer time scale. In some embodiments, the device determines the optimal stimulus as a weighted average of the optimal stimuli for each of the observed individual symptoms, where the weights are proportional to the severity of the symptom relative to the other observed symptoms. For example, if a patient experiences tremors and contractions, and the severity of the tremors is twice that of the contractions, the output stimulus would be the optimal tremor reduction pattern multiplied by two, plus the optimal contraction reduction pattern multiplied by one. In some embodiments, the device senses all active symptoms and chooses to reduce only the symptoms of the worst severity. In some embodiments, the device measures, via a sensor, the user's RLS-induced rocking, and assigns a pattern associated with the greatest reduction in the user's rocking amplitude, where the amplitude is the amplitude of the sensor signal and the difference is defined relative to the amplitude observed in the absence of stimulation from the device.

[0056] In some embodiments, the sensors coupled to the device are an accelerometer, a gyroscope, an IMU, or a combination of other motion-based sensors. In some embodiments, the sensors coupled to the device also include an electromyogram (EMG) sensor that monitors muscle activation to sense movement due to tremor severity, dystonia, or RLS. In some embodiments, the device uses sensors housed within the device, such as an accelerometer, a pressure sensor, a force sensor, a gyroscope, an inertial measurement unit (IMU), or an electromyogram (EMG) sensor, to collect data regarding characteristics of the user's symptoms, such as amplitude and frequency of movement, or muscle activity. In some embodiments, the data described above is stored via a memory component housed within the device. In some embodiments, the data described above is periodically integrated for the purpose of large-scale data analysis by wired or wireless transfer of the data to a larger storage location that is not on the device.

[0057] In some embodiments, the actuator is a resistive heating element rather than a vibrating motor. In some embodiments, the actuator is a vibrating motor. In some embodiments, the actuator is an electromagnet. In some embodiments, the actuator is an electric permanent magnet. In some embodiments, the actuator is a piezoelectric actuator. In some embodiments, the actuator is a voice coil vibrating motor. In some embodiments, the actuator is a rotating eccentric mass vibrating motor. In some embodiments, the device is an accessory band for a third-party smartwatch or other computing wearable device. In some embodiments, the device may be wirelessly connected (e.g., via Bluetooth) to the user's smartphone. In some embodiments, the device may be configured to provide contextualized data regarding the user's state. For example, the system can correlate the onset or degree of symptoms with time, activity level, medications, diet, other symptoms, etc. In some embodiments, this can be accomplished by transmitting the extracted sensor signal features to the user's smartphone. The accompanying smartphone application can periodically prompt the user for input of other information such as activity level, diet, and medication. The application then records this data along with the sensor signal features of the symptoms in time alignment for review by the user and / or their physician.

[0058] In some embodiments, the device can be initiated by passively sensing the onset of symptoms such as the on / off phenomenon in Parkinson's disease patients who have ingested L-DOPA. In some embodiments, this can be achieved by continuously reading sensor data even while in the "off" state and switching to the "on" state when one of the sensor data features, such as amplitude, exceeds a preset threshold. In some embodiments, the device can be used to amplify tremors that are present but slight for the purpose of early diagnosis. In some embodiments, this can be achieved by manually testing a set of stimulation patterns until the tremors are detected and revealed visually or by the extracted features of the sensor data exceeding some preset thresholds. In some embodiments, this can be achieved autonomously by heuristically inverting a stimulation selection algorithm to converge on a stimulation pattern that maximizes the tremor amplitude measured by the symptom sensor relative to the tremor amplitude measured in the absence of stimulation from the device.

[0059] Figure 1 shows a system for alleviating movement disorders according to an embodiment of the present invention. The wearable device 11 and the user's body 12 function as a system having inputs and outputs that can be manipulated to change the desired results of the system. The wearable device 11 interfaces with the user's body 12. The user's body 12 includes proprioceptive nerves 1201, perceived limb position and movement 1202, a nervous system 1203, a desired activation control signal 1204, and muscles 1205. The muscles 1205 output electrical activity 1206 that is detected by EMG sensors 1107 within the wearable sensor suit 1106 and collected as local data 1103. The muscles 1205 also output movement 1207 that is detected by an inertial measurement unit (IMU) 1108. The IMU 1108 measures specific forces, angular velocities, and orientations of the body and reports them to the processing unit 1101. The processing unit 1101 receives the local data 1103 and executes a local algorithm 1102. Using the local data 1103, the processing unit 110 commands the mechanical transducer 1105 to deliver a specific vibration stimulus 13 based on the results of the local algorithm 1102. The proprioceptive nerves 1201 detect the vibration stimulus 13 and transmit the perceived limb position and movement 1202 to the nervous system 1203. Based on that signal, the nervous system 1203 transmits a desired activation control signal 1204 to activate the muscles 1205 in a way that changes or sustains their electrical activity 1206 and movement 1207. The local data 1103 collected by the sensor suit 1106 continues to be processed by the local algorithm 1102 and continues to affect the output of the mechanical transducer 1105. The local data 1103 is also transmitted via the communication module 1104 to a remote processing algorithm 14 and to a remote database 16 for long-term data storage and access by researchers 17, patients 18, or physicians 19. The remote database 16 also receives data from a larger population, or the cloud 15, and transmits this data to the remote processing algorithm 14. The remote processing algorithm 14 analyzes the data and returns the results of the analysis to the processing unit 1101 via the communication module 1104 and also to the cloud 15.In this way, the data from the cloud 15 may affect the way the local algorithm 1102 operates.

[0060] In some embodiments, the processing unit 1101 is configured to operate in two modes: a first mode in which it passively monitors the patient's movement to detect movement disorders exceeding a threshold, and a second mode in which, after such a movement disorder is detected, the processor is configured to enter an active mitigation of the movement disorder. In some embodiments, the processing unit 1101 enters active mitigation by passively sensing the onset of symptoms such as the on / off phenomenon in Parkinson's disease patients taking L-DOPA. In some embodiments, such passive sensing is performed by continuously reading sensor data even while in the "off" state and switching to the "on" state when one of the sensor data features, such as amplitude, exceeds a preset threshold.

[0061] FIG. 2 is an electrical circuit diagram highlighting the main sub-circuits of the system of FIG. 1. FIG. 2 shows a processing unit 1101 that receives body part movement data (e.g., tremor vibration data) from an inertial measurement unit 1108 within an IMU circuit 22. This data is used by the processing unit 1101 to drive a mechanical transducer 1105 within a mechanical transducer circuit 23 at various frequencies and amplitudes. Optionally, the processing unit 1101 may also receive muscle activity data from an electromyogram (EMG) sensor 1107 within an EMG circuit 24. The processing unit 1101 can transmit and receive data to and from a remote database 16 using a communication module 1104 within a communication circuit 25. The entire system receives power from a rechargeable battery 211 within a power / charging circuit 21. A charging port 212 is used to charge the battery 211. Optionally, the charging port 212 may also be used to reprogram the processing unit 1101. Power on / off is switched via a power switch 213.

[0062] Figure 3 is an isometric view of a wearable device according to an embodiment of the present invention. Figure 3 shows the main electronic device housing 32 of the device and the band 31 of the device that interfaces with the user's wrist.

[0063] Figure 4 is an exploded isometric view of a wearable device in which a vibration motor is housed in the band rather than in the main electronic device housing, according to an embodiment of the present invention. The mechanical transducer 1105 is housed within the band 31 that interfaces with the user's wrist. Between the upper half 321 and the lower half 322 of the housing are a printed circuit board (PCB) 42, silicone adapted to insulate the bottom of the PCB 42, and a rechargeable battery 26. The battery 26 includes a protection circuit to protect against overcharging and unwanted discharge. To recharge the battery 26, a magnetic connector 421 coupled to the battery and attached to the housing is inserted into the PCB 42. Since the magnetic connector 421 couples to a mating connector from an external charger, the battery 26 can be conveniently configured to be charged by the external charger. The magnetic connector 421 enables patients who have difficulty performing tasks that require fine motor skills to easily charge the device using a magnetic charging cable. The device is intended to function by pressing a single large button 323 on the upper part of the upper portion 321 of the electronic housing after the patient turns the device on. The button is provided to be easy to use for patients whose fine motor control is affected by neurological movement disorders.

[0064] Figure 5 is an isometric view of a wearable device including a loop mechanism 51 that allows for one-handed adjustment of the band 31 on the user's wrist, according to an embodiment of the present invention. Figure 5 shows the main electronic device housing 32, the band 31 that appears to be worn on the user's wrist, and the adjustment mechanism 51 integrated into the main electronic device housing 32.

[0065] Figure 6 shows an embodiment of the present invention as viewed from above with the device worn on the hand. Figure 6 shows the main electronic device housing 32 and the band 31 that interfaces with the user's wrist.

[0066] FIG. 7 shows one embodiment of the present invention as seen from the side in a state of being worn on the hand. FIG. 7 shows a main electronic device housing 32, a band 31 that interfaces with the user's wrist, and an on / off button 323 that can be used by a patient to start / stop vibration stimulation. The on / off button 323 is integrated with the main electronic device housing 32.

[0067] FIG. 8 shows a test configuration of a medical wearable device that can be used for more precise data collection according to one embodiment of the present invention. FIG. 8 shows a main electronic device housing 32 and a band 31 that interfaces with the user's wrist. These are connected to a data logging device 81 that collects and stores data. Since this test configuration has a larger processor and storage capacity, it can continue to collect and store data on a larger time scale than the device alone. At the time of analysis, more complex and computationally intensive data analysis can be performed on the collected data stored in the data logging device 81 using a larger processor.

[0068] Figure 9 shows a wearable device for alleviating movement disorders, which is an accessory to a third-party smartwatch or other computing wearable device 91 according to an embodiment of the present invention. In such an embodiment, some or all of the computing 912 and sensing 911 are offloaded to the third-party wearable device 91. The third-party device then wirelessly 94 transmits a set of motor commands to the processing unit 1101 on the accessory band 92 (e.g., via Bluetooth 913, 925). This processing unit 1101 interfaces with the transducer 1105 on the band to execute the desired motor commands. In this embodiment, the accessory band 92 has its own battery 924. In some embodiments, the band also has its own dedicated sensor 923 (such as an electromyogram sensor), and its signal is communicated to the third-party processing unit 912 via the accessory processing unit 1101 and wireless communications 913, 925, 94. Data may also be recorded on the user's smartphone 93 via the same wireless connection 94.

[0069] Figure 10 shows a side view of a wearable device as an accessory band for a third-party smartwatch or other computing wearable device according to an embodiment of the present invention. This shows the main electronic device housing 32 and the band 31 that houses the mechanical transducer 1105. The band 31 interfaces with the user's wrist. This figure shows an example of the installation of the accessory battery 925 and the processing unit 1101.

[0070] Figure 11 shows a process by which a set of stimulation parameters can be calculated using biosensor input according to an embodiment of the present invention. The stimulation parameters are continuously updated in a closed loop. These parameters can include any quantity used to define a stimulation waveform, such as frequency, amplitude, phase, duty cycle, etc. In each iteration of the update loop, the current stimulation parameters 111 and the biosensor input 112 are used to subtract the sensed waveform from the output waveform using knowledge of the output waveform, or to exclude transducer / sensor crosstalk 113 by restricting sensing to the "off" phase of the pulse stimulation using knowledge of the timing of the output waveform. This filtering then enables feature extraction 114 of the biosensor input 112. Next, a stimulation selection algorithm 115 selects new stimulation parameters 116 using the current stimulation parameters 111 and the extracted features 114. This process is shown in more detail in Figure 13. As the process is repeated, the previous new stimulation parameters 116 become the current stimulation parameters 111.

[0071] Figure 12 shows a feature extraction process 114 according to an embodiment of the present invention. This process takes in the filtered sensor signal 113, as described with respect to Figure 11, and extracts temporal features 1141, 1142, 1143 and / or spectral features 1144. Examples of common temporal features include minimum value, maximum value, first three standard deviation values, signal energy, root mean square (RMS) amplitude, zero crossing rate, principal component analysis (PCA), kernel or wavelet convolution, or auto-convolution. Examples of common spectral features include Fourier transform, fundamental frequency, (Mel frequency) cepstrum coefficients, spectral centroid, and bandwidth. The features are extracted using standard digital signal processing techniques implemented on the main processing unit of the device. The set of collected features is then supplied to the stimulation selection algorithm 115.

[0072] Figure 13 shows a stimulation optimization algorithm 115 according to an embodiment of the present invention. The stimulation selection algorithm takes in the extracted features 114 and the current stimulation parameters 111 and uses them to determine a new set of stimulation parameters 116. The process by which the new parameters are determined is an optimization 1151 to minimize the severity of the symptoms. Given that there is no analytical model of the response of the symptoms to the stimulation pattern, this optimization is essentially model-free. Examples of model-free policy optimization techniques are argmin (i.e., minimization over a set of input arguments), Q-learning, neural networks, genetic algorithms, differential dynamic programming, iterative quadratic regulators, and inductive policy search. Descriptions of some such algorithms can be found in Deisenroth, M.P. (2011), "A Survey on Policy Search for Robotics", Foundations and Trends in Robotics, 2(1-2), 1-142. doi:10.1561 / 2300000021, and Beasley, D., Bull, D.R., & Martin, R. (1993), "An Overview of Genetic Algorithms: Part 1, Fundamentals", 1-8 (the entirety of which is incorporated herein by reference).

[0073] In one example, the extracted feature may be the amplitude of the tremor, and the set of current stimulation parameters may be the stimulation waveform. The stimulation selection algorithm can then compare the tremor amplitude observed with the set of current stimulation parameters to the tremor amplitude observed with the set of previous stimulation parameters to determine which of the two sets of stimulation parameters resulted in the minimum tremor amplitude. Next, the set with the lowest resulting tremor amplitude can be used as a baseline for the next iteration of the stimulation selection algorithm to compare with new sets.

[0074] Two exemplary stimulation selection algorithms that may be used in embodiments are as follows. JPEG2025097981000002.jpg158124

[0075] In some embodiments, the structure of the output stimulation pattern may be a weighted average of the optimization patterns corresponding to each symptom, where the weights are proportional to the severity of the symptom relative to other observed symptoms. In some embodiments, the structure of the output stimulation pattern may be a pattern optimized to reduce the most severe symptom.

[0076] FIG. 14 shows a neurological signal cancellation system that illustrates how a wearable device 11 and a body 12 interact according to one embodiment of the present invention in which the body and the device are collectively considered an operating system. The system includes a user's nervous system 1203 that transmits a control signal 141 to the body 12. As a further aspect of the system, the wearable 11 senses the movement of the body and transmits an opposing control signal 142 defined by the output of an active noise cancellation algorithm 143. The control signals 141, 142 undergo a signal cancellation process within the user's nervous system 1203, resulting in a smoother perceived movement signal 144.

[0077] FIG. 15 shows a process that can process a raw sensor input 112 using active noise cancellation and calculate a set of stimulation parameters, according to one embodiment of the present invention. In FIG. 15, a series of movement disorder sensors 1106 within device 11 quantify the actual limb position and movement 1206 to generate a raw sensor input 112, which is then filtered using both a noise filter 113 and a movement disorder filter 151. The noise filter 113 can subtract from the sensed waveform using knowledge of the output waveform, or limit the sensing to the "off" phase of the pulse stimulation using knowledge of the timing of the output waveform. The movement disorder filter 151 uses a band-pass filter of 0 to 15 Hz to remove other signal components not caused by movement disorders. The resulting filtered sensor data 152 is then supplied to an active noise cancellation processor 153, which generates a tremor suppression stimulation signal 154. The active noise cancellation processor 153 will be described in more detail in connection with FIGS. 16 and 17. Device 11 uses vibration stimulation 13 to transmit the tremor suppression stimulation signal 154 to the proprioceptors 1201 of the user's body. Information regarding the body's response to the tremor suppression stimulation signal can be found in more detail in connection with FIG. 1.

[0078] FIG. 16 shows one exemplary embodiment of the processing of the generation of the tremor suppression stimulation signal 154 by an active noise cancellation (ANC) processor 153. The ANC processor 153 takes as input the resulting filtered sensor data 152 of FIG. 15, which is then transformed into the frequency domain by applying a Fourier transform 1531. By definition, the fundamental frequency ω, which is the frequency with the maximum amplitude maxis selected through the argmax1532 of the frequency domain data from the Fourier transform 1531, and the output is used as the center of the bandpass filter 1533 to ensure accurate time delay calculation 1534. For example, if the fundamental frequency is calculated to be 10 Hz, one exemplary embodiment of the bandpass filter 1533 may be set to 8 - 12 Hz. The fundamental frequency is also used to calculate in degrees the time delay 1534 required to achieve the phase offset such that the resulting signal operates as negative feedback. The time delay to achieve the phase offset of x is [Number] equal to. This filtered delay signal is output as the tremor suppression stimulation signal 154. This process can be repeated for the second to the Nth fundamental frequencies.

[0079] FIG. 17 shows another exemplary embodiment of the process of generating the tremor suppression stimulation signal 154 by the ANC processor 153. The ANC processor 153 acquires the filtered sensor data 152 from an inertial motion unit (IMU) and calculates the resulting limb acceleration 171. Next, the limb acceleration 171 in the time domain is converted to the frequency domain by applying the Fourier transform 1531, and the ANC processor 153 can extract the peak frequency 172 of the limb acceleration using a general peak finding algorithm, which takes a set of data and returns the set of maxima or peaks such that the data points on both sides of the peak are smaller than the maximum value. The ANC processor 153 selects the window size 173 of the acceleration 171 using one of two methods.

[0080] The first method 174 calculates the window size using the lowest peak frequency. By selecting the lowest peak frequency, it is ensured that all relevant features of the limb acceleration 171 are captured in the window and can be appropriately reproduced when generating the tremor suppression stimulation signal 154. This method involves inverting the lowest peak frequency corresponding to the lowest frequency feature of the limb acceleration 171 and converting it to the time domain [Hz = 1 / s]. In FIG. 17, since the lowest peak frequency is 3 Hz, the window size is 1 / 3 s.

[0081] The second method 175 uses a fixed - length window size. Then, the acceleration data captured with the selected fixed window size is inverted and becomes the output of the tremor suppression stimulation signal 154. The lower limit of the acceptable window size is determined using the first method 174, i.e., the time - domain conversion of the lowest peak frequency. Windows smaller than this cannot capture all relevant features of the limb acceleration 171. Theoretically, there is no upper limit to the acceptable window size 173, but in practice, the upper limit depends on the available memory of the device 11.

[0082] FIG. 18 is a pair of drawings 181 and 182 of an Archimedes spiral traced by a tremor patient under conditions without and with treatment by a device according to an embodiment of the present invention. The spiral tracing test enables a physician to gain insights into the frequency, amplitude, and direction of a patient's tremor. It can also inform the physician of hypokinesia, dystonia, and abnormal movements of tremor. This task requires the patient to continuously trace an Archimedes spiral. A patient with tremor has difficulty following the spiral and deviates from the spiral line during tracing, resulting in a disrupted spiral 181. When wearing a device according to an embodiment of the present invention, the patient can trace the spiral more accurately, resulting in a smoother spiral 182.

[0083] FIG. 19 shows an embodiment of a simple non-convex gradient descent optimization by exploring a parameter configuration space, which is used in an embodiment of the present invention for symptom reduction. This is a graphical representation of a stimulus selection algorithm 193 related to the present invention. The algorithm 193 moves through a stimulus parameter space 192 and attempts to minimize the severity of symptoms 191. Movement through the stimulus parameter space involves trying different sets of stimulus parameters and comparing the severity of the symptoms that result as the outcome quantified by each sensor. The algorithm attempts to minimize the severity of the symptoms by testing different parameter sets until an optimal set for minimizing the severity of the symptoms is found.

[0084] Diagnostic use. For the purpose of early detection, an alternative bench-top version of the device can be used to induce tremors in Parkinson's disease patients. This is done using the same mechanism as for tremor reduction, but using a reverse stimulus parameter exploration heuristic. User tests have shown that for each patient, there is a stimulus pattern that, when applied to Parkinson's disease patients with very slight tremors, results in very large tremors. This effect does not occur in users without Parkinson's disease. This phenomenon can be used for the early detection and diagnosis of Parkinson's disease, which can be difficult to diagnose.

[0085] Patient studies. FIGS. 20 and 21 respectively show power spectral density (PSD) plots of postural tremors of Parkinson's disease patients and essential tremor patients with and without using the device according to an embodiment of the present invention. The data were obtained by having each patient extend their own hand for 10 seconds, regardless of the presence or absence of the device. In FIGS. 20 and 21, the tremor amplitude is compared with and without the device.

[0086] The test examples of one embodiment of the present invention will be described below. Participants were asked to trace a printed Archimedes spiral, a common test used to diagnose Parkinson's disease, with or without the device, as shown in FIG. 18. The results were measured using image processing software to evaluate the accuracy of the traced spiral. In the first test, the device was tested on approximately 20 participants with Parkinson's disease and 1 participant with resting tremor. However, most of the participants either did not experience tremors or had already received treatment for Parkinson's disease and experienced only slight tremors. It was observed that the reduction in tremor severity was strongly correlated with the initial tremor severity. That is, patients with the least tremors experienced the least benefit, and patients with more extreme tremors experienced more dramatic benefits. The participant with the most severe postural tremor caused by Parkinson's disease showed the greatest improvement in performance, as shown in FIG. 20. Another participant with postural tremor caused by essential tremor also showed significant improvement, as shown in FIG. 21. The results were reproducible in both of these participants. It was observed that participants suffering from contracture had a greater range of hand movement and completed the spiral test faster when using the device than when not using it.

[0087] FIGS. 22 and 23 show the tremor responses of 21 PD patients and 8 ET patients at peak tremor displacement when stimulation was applied using a process of generating a tremor suppression stimulation signal by varying the phase rotation of the stimulation and selecting the phase rotation that results in the maximum tremor alleviation. Using this method, the frequency of the stimulation was selected by an active noise cancellation (ANC) processor and thus varied between patients, but each patient's test session was performed at only one frequency.

[0088] FIGS. 24 and 25 show the tremor responses of 19 PD and 6 ET patients at peak tremor displacement when stimulation was applied using a process of generating a tremor suppression stimulation signal by varying the frequency of the stimulation and selecting the frequency that results in the maximum tremor alleviation. The phase rotation was not adjusted.

[0089] The test examples of an embodiment of the present invention will be described below. Participants were asked to perform several tasks in which tremors were observed regardless of the presence or absence of stimulation. The tasks were extracted from the validated scales for upper limb tremor assessment in both PD and ET, the MDS-UPDRS (Movement Disorder Society-Unified Parkinson’s Disease Rating Scale) and the TETRAS (The Essential Tremor Rating Assessment Scale), respectively. To evaluate postural tremor, participants were asked to extend their arms in front of their bodies. To evaluate kinetic tremor, participants were asked to start with their arms extended and then return their fingers to touch their noses and back to the extended position. To evaluate resting tremor, participants were asked to relax with their arms on the surface while counting down from 100 with their eyes closed. Between each task, participants exercised for 80 seconds and the vibration stimulation was switched off and on every 20 seconds. During the treatment stimulation, participants were randomly started with either 10 seconds of Option A (treatment) or Option B (no treatment), followed by a 10-second rest period to account for potential carry-over effects. After the break, a crossover was performed, and participants who received Option A received Option B for 10 seconds and vice versa. Then, participants took another 10-second break and then repeated the randomization and crossover once again. The results in FIGS. 22 to 25 show the best responses for each participant.

[0090] Embodiments to be wrapped. The above embodiments refer to an accelerometer, a vibration motor, a micro USB, and a wristband, but the present invention is not limited to such implementation forms. Additionally, the above embodiments do not limit the scope of the present invention. For example, various modifications and variations of interfaces, types of electromyogram sensors, gyroscopes, inertial measurement units, piezoelectricity, electromagnets, permanent magnets, pneumatics, voice coils, hydraulics, resistive heating elements should be included. The range of form factors should also include headbands, colors, anklets, armbands, and rings. The range of electrical interfaces should include Thunderbolt cables, USB, USB C, micro USB, wireless communication, wireless charging, and Bluetooth communication.

[0091] The present invention can be embodied in many different forms, including, but not limited to, computer program logic for use with a processor (e.g., a microprocessor, a microcontroller, a digital signal processor, or a general-purpose computer), programmable logic for use with a programmable logic device (e.g., a field programmable gate array (FPGA) or other PLD), discrete components, integrated circuits (e.g., application specific integrated circuits (ASIC)), or any other means including any combination thereof.

[0092] The computer program logic that implements all or part of the functions described above in this specification can be implemented in various forms including, but not limited to, source code form, computer-executable form, and various intermediate forms (e.g., forms generated by assemblers, compilers, networkers, or locators). The source code can include a series of computer program instructions implemented in any of various programming languages (e.g., object code, assembly language, or high-level languages such as Fortran, C, C++, JAVA, or HTML) for use in various operating systems or operating environments. The source code can define and use various data structures and communication messages. The source code may be in computer-executable form (e.g., via an interpreter), or the source code may be converted into computer-executable form (e.g., via a translator, assembler, or compiler).

[0093] A computer program may be permanently or temporarily fixed in a tangible storage medium such as a semiconductor memory device (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), a magnetic memory device (e.g., a floppy disk or a fixed disk), an optical memory device (e.g., a CD-ROM), a PC card (e.g., a PCMCIA card), or other memory devices in any form (e.g., source code form, computer-executable form, or intermediate form). The computer program may be fixed in any form of signal transmissible to a computer using any of various communication technologies including, but not limited to, analog technology, digital technology, optical technology, wireless technology, networking technology, and internetworking technology. The computer program may be distributed in any form as a removable storage medium with an attached printed or electronic document (e.g., shrinkwrap software or magnetic tape), may be preloaded in a computer system (e.g., on a system ROM or a fixed disk), or may be distributed from a server or an electronic bulletin board via a communication system (e.g., the Internet or the World Wide Web).

[0094] Hardware logic (including programmable logic for use in programmable logic devices) implementing all or part of the functions described above in this specification may be designed using conventional manual methods or may be electronically designed, captured, simulated, or documented using various tools such as computer-aided design (CAD), a hardware description language (e.g., VHDL or AHDL), or a PLD programming language (e.g., PALASM, ABEL, or CUPL).

[0095] Although the present invention has been particularly shown and described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the spirit and scope of the invention as defined by the appended claims. Some of these embodiments are defined by process steps in the claims, but an apparatus comprising a computer having an associated display capable of performing the process steps of the following claims is also included in the present invention. Similarly, a computer program product stored on a computer-readable medium and including computer-executable instructions for performing the process steps of the following claims is also included in the present invention.

[0096] The above-described embodiments of the present invention are intended to be illustrative only. Numerous variations and modifications will be apparent to those skilled in the art. All such variations and modifications are intended to be within the scope of the present invention as defined by the appended claims.

Claims

1. 1. A wearable device for regulating a set of movement disorder symptoms in a subject, comprising: a. a housing; b. an attachment system coupled to the housing and configured to be attached to a body part of a subject; c. a set of body part sensors disposed within said housing and providing a set of sensor outputs related to movement of said body part; d. a set of mechanical transducers coupled to the mounting system configured to provide a set of mechanical outputs to the body part; e. a processing unit having (i) an input operatively coupled to said sensor output for receiving body part movement data, (ii) a noise filtering process for removing noise unrelated to said movement disorder symptoms from said body part movement data to generate a filtered movement signal, (iii) a feature extraction process for said filtered movement signal for characterizing features of said filtered movement signal to generate a characterized filtered movement signal, and (iv) a stimulation process for said characterized filtered movement signal to generate a stimulation signal output, such output being operatively coupled to said set of mechanical transducers, causing said mechanical transducers to provide mechanical stimulation to said body part that modulates said set of movement disorder symptoms; A device comprising:

2. 2. The device of claim 1, wherein the processing unit is further configured to provide active noise cancellation by: (a) converting body part movement data in the time domain into frequency domain data; (b) determining a fundamental frequency of the movement disorder symptom using the frequency domain data; and (c) finally generating the stimulation signal output based on the body part movement data having a desired phase shift relative to a phase of the body part movement data at the fundamental frequency.

3. 2. The device of claim 1, wherein the processing unit is further configured to provide at the stimulation signal output a train of stimulation signals, each signal in the train having a distinct set of parameters associated with the alleviation of the set of movement disorder symptoms, and the feature extraction process includes determining displacement or power data associated with the movement of the body part, and the processing unit is also configured to use the displacement or power data to determine which stimulation signal in the train has the greatest alleviation effect.

4. The device of claim 3 , wherein the processing unit is further configured to select the stimulation signal determined to have the greatest relaxation effect for continuous output to the mechanical transducer.

5. 10. The device of claim 1, wherein the processing unit is configured to detect and alleviate a set of movement disorder symptoms selected from the group consisting of tremor, rigidity, bradykinesia, dyskinesia, urge to move, and combinations thereof.

6. The device of claim 1 , wherein the processing unit is configured to detect freezing of gait in a patient with Parkinson's disease.

7. 7. The device of claim 6, wherein the processing unit is further configured to mitigate freezing of gait in a Parkinson's disease patient by controlling the set of mechanical transducers.

8. The device of claim 1 , wherein the attachment system includes a wristband, and the set of mechanical transducers are distributed around a circumference of the wristband.

9. 10. The device of claim 1, operated by buttons on a face of the device, the buttons configured for ease of use by patients whose fine motor control is affected by neurological movement disorders.

10. 2. The device of claim 1, wherein the processing unit is configured to operate in two modes: a first mode for passively monitoring a patient's movement to detect a movement disorder symptom above a threshold, and a second mode for mitigating the movement disorder symptom after detection of such a movement disorder symptom.

11. 10. The device of claim 1, further comprising a battery disposed on the housing and a magnetic connector coupled to the battery and mounted within the housing for coupling to a mating connector from an external charger such that the battery can be conveniently charged by a patient lacking fine motor control.

12. 10. The device of claim 8, wherein the wristband is constructed with hook-and-loop fasteners to allow the wristband to be fastened with one hand for ease of use by individuals whose fine motor control is affected by neurological motor disorders.

13. 10. The device of claim 8, wherein the wristband is configured to be stretchable via elastic deformation for ease of use by individuals whose fine motor control is affected by neurological motor disorders.

14. The device of claim 1 , wherein the processing unit is further configured to store the body part movement data in a memory coupled to the processing unit.

15. The device of claim 1 , wherein the processing unit is configured to provide active noise cancellation by converting the sensor output into frequency data by applying a Fourier transform to the sensor output.

16. 16. The device of claim 15, wherein the processing unit is configured to provide active noise cancellation by (i) selecting a fundamental frequency by applying an argmax function to the transformed sensor output, and (ii) using a bandpass filter to remove a set of frequency data from the stimulation signal output that is outside a specified range associated with the fundamental frequency.

17. The set of body part sensors includes an inertial motion unit (IMU) configured to calculate data representative of an acceleration of the body part, and an active noise canceling processor configured to: converting the acceleration data of the body part into the frequency domain data by applying a Fourier transform; extracting peak frequencies of acceleration data of said body part from said frequency domain data; selecting a window size for the acceleration data of the body part based on the peak frequency; and capturing a portion of the sensor output based on the selected window size and inverting the captured portion to generate the stimulation signal output; The device of claim 2 , further configured to provide active noise cancellation by:

18. 20. The device of claim 17, wherein in selecting the window size, the processing unit inverts a lowest one of the peak frequencies and transforms the inverted lowest one of the peak frequencies into a time domain.

19. The device of claim 17 , wherein the processing unit is configured to set the window size to a fixed value.

20. 1. A method for alleviating a set of movement disorder symptoms in a subject, comprising: receiving a set of sensor outputs related to movement of the subject's body part from a set of body part sensors; filtering the sensor output to remove noise unrelated to the movement disorder condition to generate a filtered signal; and (a) converting the sensor output into frequency data; (b) using the frequency data to determine a fundamental frequency of the movement disorder symptom; and (c) actively processing the filtered signal by applying a time delay to the filtered signal to produce a desired phase shift at the fundamental frequency, thereby generating a stimulation signal output. processing the sensor output to generate a stimulation signal for alleviating the set of movement disorder symptoms by transmitting the stimulation signal output to a set of mechanical transducers coupled to the body part to alleviate the set of movement disorder symptoms; A method comprising:

21. 21. The method of claim 20, wherein the set of movement disorder symptoms is selected from the group consisting of tremor, rigidity, bradykinesia, dyskinesia, urge to move, and combinations thereof.

22. 21. The method of claim 20, wherein receiving a set of sensor outputs related to movement of the target body part from a set of body part sensors comprises operating in two modes: a first mode of passively monitoring patient movement to detect a movement disorder symptom above a threshold, and a second mode of entering into active mitigation of the movement disorder symptom after detection of such a movement disorder symptom.

23. 21. The method of claim 20, wherein actively processing the filtered signal to convert the sensor output into frequency data comprises applying a Fourier transform to the filtered signal to generate transformed sensor output data.

24. 24. The method of claim 23, wherein actively processing the filtered signal to convert the sensor output to frequency data comprises: (i) selecting the fundamental frequency by applying an argmax function to the converted sensor output data; and (ii) using a bandpass filter to remove from the stimulus signal a set of frequency data outside a specified range associated with the fundamental frequency.

25. Processing the sensor output to generate the stimulus signal includes: calculating data representative of the acceleration of said body part; converting acceleration data of the body part into said frequency data by applying a Fourier transform; extracting a peak frequency of acceleration data of the body part from the frequency data; selecting a window of acceleration data for the body part based on the peak frequency; capturing a portion of the sensor output based on the selected window and inverting the captured portion to generate the stimulus signal; 21. The method of claim 20, further comprising:

26. 26. The method of claim 25, wherein selecting the window comprises inverting a lowest one of the peak frequencies and transforming the inverted lowest one of the peak frequencies into a time domain signal.

27. 26. The method of claim 25, wherein selecting the window comprises setting the window to a fixed value.

28. 1. A method for alleviating a set of movement disorder symptoms in a subject, comprising: monitoring sensor outputs of sensors configured to sense movement of the subject's body part to provide a set of sensor outputs related to movement of the body part; processing the sensor output to generate a stimulation signal for alleviating the set of movement disorder symptoms; delivering the stimulation signals to a set of mechanical transducers coupled to the body part to alleviate the set of movement disorder symptoms; and processing the sensor output comprises: filtering the sensor output to remove noise unrelated to the movement disorder condition to generate a filtered signal; and using a processor configured to: (a) provide to the set of mechanical transducers an output of a train of stimulation signals in which stimulation parameters are continuously varied; (b) convert the sensor output into displacement or power data; and (c) output the stimulation signal that produces the greatest relaxation effect; A method comprising:

29. 30. The method of claim 28, wherein the processor is further configured to use displacement or power data to select the stimulation signal that results in the greatest relief effect.

30. 30. The method of claim 28, wherein the processor is further configured to receive input from a user to select the stimulation signal that provides the greatest relief effect.

31. 1. A method for diagnosing a movement disorder of a body part in a subject suspected of having the disorder, comprising: using a processor configured to provide an output of a train of stimulation signals to a set of mechanical transducers, the stimulation signals having successively varying stimulation parameters to induce symptoms of a movement disorder; receiving body part movement data from a set of body part sensors relating to movement of the body part; filtering the body part movement data to remove noise unrelated to symptoms of the movement disorder to generate a filtered signal; actively processing the filtered signal to (a) convert the body part movement data into frequency data, and (b) use the frequency data to determine a fundamental frequency of the movement of the body part; further processing the frequency data to determine a probability that the subject is afflicted with the movement disorder; A method comprising:

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