Devices and methods for reducing symptoms of neurological movement disorders using wearable devices

Abstract

The multimodal wearable band uses mechanical vibrations to stimulate sensory neurons in the wrist or ankle, reducing the severity of tremors, rigidity, involuntary muscle contractions, and bradykinesia caused by neurological movement disorders, and relieving the user of movement disorder-induced freezing. The device uses sensors to determine the user's stimulation pattern and provides an output that is used by a processing unit to determine when stimulation is needed, and then uses one or more mechanical transducers to correspondingly stimulate the user's neural pathways, reducing the severity of the user's symptoms. The device can also be adapted to integrate with third-party devices.

Description

[Technical field]

[0001] The present invention relates to a wearable medical device, and in particular to a wearable medical device that alleviates symptoms of neurological movement disorders. [Background technology]

[0002] There are several neurological movement disorders that present with a somewhat similar range of symptoms, examples of which are listed below: Essential tremor is characterized by shaking of the limbs. Parkinson's disease (PD) may cause tremor, rigidity, bradykinesia, and temporary freezing or inability to initiate movement. Restless legs syndrome does not cause tremor, but does cause the patient to have a strong urge to move and shake their legs. Tremor may also be present as a side effect of certain medications.

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

[0004] There is no cure for Parkinson's disease. Current treatment consists of medications to address the patient's symptoms, but these do not reverse the effects of the disease. Patients often take a variety of medications at different doses and at different times to manage symptoms. PD medications are mostly dopaminergic, providing dopamine or mimicking 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 uses a controller to turn the device on and off as needed to help control the tremor. Although DBS can be effective for both Parkinson's disease and essential tremor, the procedure is invasive and expensive.

[0006] The second surgical procedure available to Parkinson's patients is Duopa therapy, which requires a small hole (stoma) to be surgically created in the stomach to allow a tube to be placed in the intestine. Duopa, which is similar to regular PD medication taken via pill, is then pumped directly into the intestine, improving absorption and reducing the off-time of medication taken via pill.

[0007] A similar disorder called essential tremor, often misdiagnosed as Parkinson's disease, has an even higher incidence, with an estimated 100 million cases worldwide. These tremors often occur during purposeful movement and can become so severe that patients are no longer able to chop food, tie their shoelaces, or sign their names. Medications for essential tremor can include beta-blockers and anti-seizure drugs. These drugs are known to cause fatigue, heart problems, and nausea.

[0008] Multiple sclerosis (MS) is an inflammatory autoimmune disease of the central nervous system that affects 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 that includes periods of new symptoms or relapses followed by periods of recovery. As the disease progresses, the occurrence of symptoms may take on a regular pattern, after which the disease is classified as "secondary progressive". Some MS patients may experience a gradual onset and progression without any relapsing remissions, classified as "primary progressive". There is no cure for MS, and treatment typically focuses on recovery from relapsing attacks using physical therapy and medication. Disease-modifying drugs reduce the incidence of relapses but do not help alleviate symptoms during relapses.

[0009] Restless Legs Syndrome (RLS) affects approximately 10% of the US population. RLS can also be a side effect of primary Parkinson's disease. RLS is characterized by an unpleasant tingling sensation in the patient's legs. These sensations occur when the legs are stationary and are relieved when the legs are moving. As a result, RLS patients are compelled to move or rock their legs. This is particularly detrimental to the patient's quality of sleep, as they are unable to remain stationary.

[0010] RLS is generally treated by dietary changes, medications, and / or physical therapy. Dietary changes may include the elimination of caffeine, alcohol, and tobacco. Medications prescribed for RLS may include the same classes of medications 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 or electrical or vibratory stimulation of the legs.

[0011] One 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, TJ, & Marar, U. (2010). The device is a lower leg sleeve with sensors and actuators, but 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 to manage neurological movement disorders that work by promoting dopamine, a chemical produced by the brain that helps control body movement. This chemical is lacking in the brains of patients with diseases such as Parkinson's. Pharmaceutical treatments for Parkinson's are expensive, costing users thousands of dollars each year; they are ineffective or stop working 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 each year and work by killing the nerves that cause the tremors. While this is effective in reducing tremors, the death of the nerves also causes a significant decrease in mobility. Additionally, this treatment is only available at highly specialized treatment centers and is therefore not an option for the majority of patients.

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

[0015] Perhaps the most relevant studies have emerged in the last few years, demonstrating that the use of vibration can improve motor performance. There seems to be a large variability in effectiveness, which depends on the frequency of vibration and the condition of the patient. 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 result in slowing and reducing repetitive movements of the hand. For post-stroke patients, 70 Hz has 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 effect of such vibrations is seen in sustained reduction in weakness and spasticity of 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.Vibration may even have a positive effect on rigidity or stiffness by increasing relaxation using vibrotactile stimulation, as shown in three separate studies: "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, MD, Hensel, CL, & Matheson, DW (1982), Perceptual and Motor Skills, 54(3_suppl), 1071-1076, and "Relaxation measured by EMG as a function of vibrotactile stimulation", Matheson, DW, Edelson, R., Hiatrides, D. et al. (1976), Biofeedback and Self-Regulation 1, 285-292. One device exists that provides haptic signals around the user's wrist using actuators placed along a band. These actuators slide along the band and reposition relative to each other to provide the correct positioning of the signal. See US 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] US Patent Application Publication No. 20100249637 [Patent Document 2] U.S. Patent No. 8,762,065 [Patent Document 3] U.S. Patent No. 9,452,287 [Patent Document 4] U.S. Patent No. 9802041 [Patent Document 5] U.S. Patent No. 10,905,879 [Patent Document 6] US Patent Application Publication No. 20180356890 [Non-patent literature]

[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, KE, & 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, MD, Hensel, CL, & Matheson, DW(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, DW, Edelson, R., Hiatrides, D. et al.(1976), Biofeedback and Self-Regulation 1, 285-292 Summary of the Invention [Problem to be solved by the invention]

[0018] Thus, there is a need for devices that non-invasively, reliably, and inexpensively alleviate the symptoms of neurological movement disorders. [Means for solving the problem]

[0019] According to one embodiment of the present invention, a wearable device for adjusting a set of movement disorder symptoms of a subject is provided. 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 configured to provide a set of mechanical outputs to the body part. The device also includes a processing unit having (i) an input operably coupled to the sensor outputs for receiving body part movement data, (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 feature extraction process for the filtered movement signal for characterizing 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 causing the transducers to provide a mechanical stimulation to the body part that alleviates 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 into frequency domain data, (b) determining a fundamental frequency of the movement disorder symptom using the frequency domain data, and (c) generating a stimulation signal output based on the body part movement data having a desired phase shift relative to the phase of the body part movement data at the fundamental frequency.

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

[0022] Optionally, the set of dyskinetic symptoms is selected from the group consisting of tremor, rigidity, bradykinesia, dyskinesia, urge to move, and combinations thereof. Optionally, the processing unit is further configured to detect freezing of gait in the Parkinson's disease patient. Alternatively or additionally, the processing unit is further configured to control the set of mechanical transducers to mitigate freezing of gait in the Parkinson's disease patient by controlling the set of mechanical transducers. Optionally, the processing unit is configured to operate in two modes: a first mode configured to passively monitor the patient's movements to detect dyskinetic symptoms above a threshold, and a second mode configured to enter into active mitigation of dyskinetic symptoms after detection of such dyskinetic symptoms.

[0023] Optionally, the attachment system includes a wristband, the set of mechanical transducers being distributed around the entire circumference of the wristband. Optionally, the device is operated by a button on a face of the device, the button being configured for ease of use by a patient whose fine motor control is affected by a neurological motor disorder. Optionally, the wristband is configured with a hook-and-loop fastener so that the wristband can be fastened with one hand for ease of use by a person whose fine motor control is affected by a neurological motor disorder. Optionally, the wristband is configured to be stretchable via elastic deformation for ease of use by a person whose fine motor control is affected by a neurological motor disorder. Optionally, the device further includes a battery disposed in the housing, and a magnetic connector coupled to the battery and mounted within the housing for coupling to a mating connector for an external charger, such that the battery may be conveniently configured for charging by a patient who lacks 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 cancelling processor is configured to convert the sensor output into frequency data by applying a Fourier transform to the sensor output. Alternatively or additionally, the active noise cancelling 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 from the stimulation signal a set of frequency data outside a specified range related to the fundamental frequency.

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

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

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

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

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

[0031] Optionally, processing the sensor output to generate the stimulation signal further includes calculating data representative of an acceleration of the body part, converting the acceleration data of the body part into frequency data by applying a Fourier transform, extracting peak frequencies 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 frequencies, capturing a portion of the sensor output based on the selected window size and inverting the captured portion to generate the stimulation signal. Alternatively or additionally, selecting the window size includes inverting a lowest peak frequency of the peak frequencies and transforming the inverted lowest peak frequency of 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, there is provided a method for alleviating a set of movement disorder symptoms in a subject, the method comprising the steps of monitoring sensor outputs of a sensor configured to sense movement of a body part of a subject and provide a set of sensor outputs related to the movement of the body part; processing the sensor outputs by filtering the sensor outputs to remove noise not related to the movement disorder symptoms to generate a filtered signal to generate a stimulation signal for alleviating the set of movement disorder symptoms; and using a fixed frequency processor configured to (a) sequentially input the set of frequency varying stimulation signals to a set of mechanical transducers; (b) convert the sensor outputs 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 greatest relief effect. In some embodiments, the processor is further configured to receive input from a user to select the stimulation signal that provides the greatest relief effect.

[0034] According to some embodiments, there is provided a method of diagnosing a movement disorder in a patient, comprising: (1) using a processor to provide to a set of mechanical transducers a train of stimulation signals outputting successively varying stimulation parameters to induce symptoms of a movement disorder; (2) receiving body part movement data from a set of body part sensors relating to movement of the body part; (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 into frequency data, and (b) use the frequency data to determine a fundamental frequency of motion of the body part; (5) further processing the frequency data to determine a probability that the subject suffers from a movement disorder; A method is provided, comprising:

[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, in which: [Brief description of the drawings]

[0036] [Figure 1] FIG. 1 illustrates a system for alleviating movement disorders according to one embodiment of the present invention. [Diagram 2] FIG. 2 is an electrical schematic diagram highlighting major subcircuits of the system of FIG. 1. [Diagram 3] FIG. 1 is an isometric view of a wearable device according to one embodiment of the present invention. [Figure 4] FIG. 1 illustrates an exploded isometric view of a wearable device in which the vibration motor is housed in the band rather than in the main electronics housing, according to one embodiment of the present invention. [Diagram 5] FIG. 1 is an isometric view of a wearable device including a loop mechanism that allows for one-handed adjustment of the band on a user's wrist, according to one embodiment of the present invention. [Figure 6]FIG. 1 is a diagram of one embodiment of the present invention as viewed from above when worn on a hand. [Figure 7] FIG. 2 is a side view of an embodiment of the present invention worn on a hand. [Figure 8] FIG. 1 illustrates a test configuration for a medical wearable device that can be used for more rigorous data collection, according to an embodiment of the present invention. [Figure 9] FIG. 1 illustrates a wearable device for alleviating movement disorders that is an accessory to a third party smartwatch or other computing wearable device, according to one embodiment of the present invention. [Figure 10] FIG. 1 is a side view of a wearable device as an accessory band for a third party smartwatch or other computing wearable device, according to one embodiment of the present invention. [Figure 11] FIG. 1 illustrates a process for calculating a set of stimulation parameters using raw sensor inputs, according to one embodiment of the present invention. [Figure 12] FIG. 2 illustrates a feature extraction process according to one embodiment of the present invention. [Figure 13] FIG. 1 illustrates a stimulation optimization algorithm, according to one embodiment of the present invention. [Figure 14] FIG. 1 illustrates a neurological signal canceling system showing how a wearable device interacts with the body, according to one embodiment of the present invention. [Figure 15] FIG. 1 illustrates a process by which raw sensor input may be processed using active noise cancellation to compute a set of stimulation parameters, according to one embodiment of the present invention. [Figure 16] FIG. 1 illustrates one example embodiment of processing of tremor suppression stimulation signal generation by an active noise canceling (ANC) processor. [Figure 17] 1 illustrates another example embodiment of a process for tremor suppression stimulation signal generation by an ANC processor. [Figure 18]1A-1C show pairs of spiral drawings by a Parkinson's disease patient under untreated and treated conditions with a device, in accordance with an embodiment of the present invention. [Figure 19] FIG. 1 illustrates an embodiment of a simple non-convex gradient descent optimization by exploring a parameter configuration space used in an embodiment of the present invention for symptom reduction. [Figure 20] FIG. 1 shows power spectral density (PSD) plots of postural tremor in a Parkinson's disease patient with and without a device according to an embodiment of the present invention. [Figure 21] FIG. 1 shows power spectral density (PSD) plots of essential tremor postural tremor with and without a device according to an embodiment of the present invention. [Figure 22] Figure 1 shows the tremor response of 21 PD patients at peak tremor displacement when stimulation was applied using a process for tremor suppression stimulation signal generation by varying the phase rotation of the stimulation and selecting the phase rotation that results in maximum tremor reduction. [Diagram 23] FIG. 1 shows the tremor response of eight ET patients at peak tremor displacement when stimulation was applied using a process for tremor suppression stimulation signal generation by varying the phase rotation of the stimulation and selecting the phase rotation that results in maximum tremor reduction. [Figure 24] FIG. 1 shows the tremor response of 19 PD patients at peak tremor displacement when stimulation was applied using a process for generating a tremor suppressing stimulation signal by varying the frequency of stimulation and selecting the frequency that results in maximum tremor reduction. [Diagram 25] FIG. 1 shows the tremor response of six ET patients at peak tremor displacement when stimulation was applied using the process of tremor suppression stimulation signal generation by varying the frequency of stimulation and selecting the frequency that results in maximum tremor reduction. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

[0038] A "set" contains at least one member.

[0039] A "body part" is a part of the human body, such as a limb (e.g., arm, leg, ankle, wrist, etc.) or neck.

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

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

[0042] A "movement disorder sensor" is a sensor configured to provide a measurement related to a neurological movement disorder.

[0043] A "mounting system" is a system or device that has a means for mechanically fastening a component subsystem to the body of a user.

[0044] A "housing" is a primary enclosed casing that contains one or more component subsystems.

[0045] A "band" is a flexible segment of material that encircles a body part or portion of a body part for immobilization purposes and may also house one or more component subsystems.

[0046] The term "vibration stimulation" refers to a vibration or series of vibrations generated by a vibration motor or group of vibration motors incorporated into the device. These vibrations are used to stimulate a response from targeted 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. "Stimulation pattern" may also refer to longer time scale behavior in which the above parameters change over time.

[0048] The term "proprioception" refers to the sense of the position of one's own limbs or body parts and the intensity of forces exerted through that body part. Proprioceptors are the sensory neurons used for proprioception. There are two types of proprioceptors: "muscle spindles" located in muscles and "Golgi tendon organs" located in tendons.

[0049] The term "neurological movement disorder" refers to any neurological condition that causes abnormally increased or decreased 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), paresis, hemiplegia, tetraparesis, 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's syndrome, spasticity, rigidity, bradykinesia, tremor, essential tremor (ET), alcohol or drug withdrawal induced tremor, drug-induced tremor, psychogenic tremor, rest tremor, action tremor, cerebellar lesions, 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 a period or stage of operation of the device during which the device is experimenting or collecting and analyzing data with the aim of estimating optimal stimulation patterns.

[0051] A "computer process" is the execution of a described function in a computer system using computer hardware (e.g., a processor, a field programmable gate array or other electronic combinatorial logic, or similar devices), which may operate under the control of software or firmware, or any combination of these, or outside the control of any of the above. All or part of the described functions may be performed by active or passive electronic components, such as transistors or resistors. Use of the term "computer process" does not necessarily require a schedulable entity, or the operation of a computer program or portions thereof, although in some embodiments a computer process may be implemented by such a schedulable entity, or the operation of a computer program or portions thereof. Furthermore, unless the context requires otherwise, a "process" may be implemented using more than one processor or more than one (single or multi-processor) computer.

[0052] PROCEDURAL WEARABLE DEVICE. The present invention relates generally to a medical wearable device, and in particular to the alleviation of tremors, rigidity, bradykinesia, involuntary rhythmic movements, and freezing associated with neurological movement disorders through mechanical vibration stimulation of tendon bundles in the wrist and autonomous sensing, feedback, and adjustment. Device embodiments also have several considerations that facilitate ease of use by people with disabilities for which the present invention is intended, 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 system and method 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 treated include tremor, rigidity, bradykinesia, stiffness, hemiplegia, and freezing. In some embodiments, the symptoms treated include muscle contractions caused by dystonia. In some embodiments, the symptoms treated include an inability to locate one's limbs in space. In some embodiments, the proprioceptors targeted for stimulation are located on the wrist. In some embodiments, the proprioceptors targeted for stimulation are located on the ankle. In some embodiments, the proprioceptors targeted for stimulation are located on the neck.

[0054] In some embodiments, the system provides stimulation to the proprioceptive nerves (proprioceptors) to reduce symptoms through the use of vibration motors placed around the surface of the wrist. In some embodiments, the system cycles through stimulation frequency patterns and waveforms to find the pattern that provides the greatest reduction in movement disorder symptoms. In some embodiments, the system uses random white noise subthreshold stimulation to exploit the effects 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 to the tremor exhibited in the absence of stimulation.

[0055] In some embodiments, the device finds (learns) optimal stimulation parameters to use for symptom reduction by using sensor-based optimization, including but not limited to model-free reinforcement learning, genetic algorithms, and 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 the stimulation pattern over time on longer time scales. In some embodiments, the device determines the optimal stimulation as a weighted average of the optimal stimulation for each observed independent symptom, where the weights are proportional to the severity of the symptom relative to the other observed symptoms. For example, if a patient experiences tremor and rigidity, and the tremor is twice as severe as the rigidity, the output stimulation will be twice the optimal tremor reduction pattern superimposed with one time the optimal rigidity reduction pattern. In some embodiments, the device senses all active symptoms and chooses to reduce only the worst severity symptom. In some embodiments, the device measures the user's RLS sway via sensors and assigns a pattern associated with the greatest reduction in sway amplitude for that user, 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 sensor coupled to the device is a combination of an accelerometer, gyroscope, IMU, or other motion-based sensor. In some embodiments, the sensor coupled to the device also includes an electromyogram (EMG) sensor that monitors muscle activation to sense tremor severity, rigidity, or movement due to RLS. In some embodiments, the device uses sensors housed in the device, such as an accelerometer, pressure sensor, force sensor, gyroscope, inertial measurement unit (IMU), or electromyogram (EMG) sensor, to collect data on the characteristics of the user's symptoms, such as the amplitude and frequency of movement, or muscle activity. In some embodiments, the above-mentioned data is stored via a storage component housed in the device. In some embodiments, the above-mentioned data is periodically consolidated for the purpose of large-scale data analysis by wired or wireless transfer of data to a larger storage location not on the device.

[0057] In some embodiments, the actuator is a resistive heating element rather than a vibration motor. In some embodiments, the actuator is a vibration 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 vibration motor. In some embodiments, the actuator is a rotating eccentric mass vibration motor. In some embodiments, the device is an accessory band to a third party smartwatch or other computing wearable device. In some embodiments, the device may be wirelessly connected (e.g., via Bluetooth) to a user's smartphone. In some embodiments, the device may be configured to provide contextualized data regarding the user's condition. For example, the system may correlate the onset or extent of symptoms with time of day, activity level, medication, diet, other symptoms, etc. In some embodiments, this may be accomplished by transmitting extracted sensor signal features to the user's smartphone. An accompanying smartphone application may periodically prompt the user to input other information such as activity level, diet, and medication. The application then records this data along with the sensor signal features of the symptoms matched to time 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 on / off phenomena in Parkinson's patients taking L-dopa. In some embodiments, this can be accomplished by continuously reading the 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 subtle, for the purpose of early diagnosis. In some embodiments, this can be accomplished by manually testing a set of stimulation patterns until the tremor is detected and evident, either visually or by extracted features of the sensor data exceeding some preset threshold. In some embodiments, this can be accomplished autonomously by heuristically reversing the stimulation selection algorithm to converge to 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] FIG. 1 illustrates 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 with inputs and outputs that can be manipulated to change the desired outcome of the system. The wearable device 11 interfaces with the user's body 12, which includes proprioceptive nerves 1201, perceived limb position and movement 1202, a nervous system 1203, desired activation control signals 1204, and muscles 1205. The muscles 1205 output electrical activity 1206 that is detected by an EMG sensor 1107 in the wearable's sensor suite 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 and reports specific forces, angular velocities, and orientations of the body to the processing unit 1101. The processing unit 1101 receives the local data 1103 and executes the local algorithm 1102. Using the local data 1103, the processing unit 110 commands the mechanical transducer 1105 to deliver a particular 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 the desired activation control signals 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 suite 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 a 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 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, data from the cloud 15 can influence the way the local algorithms 1102 operate.

[0060] In some embodiments, the processing unit 1101 is configured to operate in two modes: a first mode configured to passively monitor the patient's movements to detect movement disorders above a threshold, and a second mode in which the processor is configured to enter into active mitigation of the movement disorder after detection of such a movement disorder. In some embodiments, the processing unit 1101 enters into active mitigation by passively sensing the onset of symptoms, such as on / off phenomena, in a Parkinson's patient taking L-dopa. In some embodiments, such passive sensing is performed by continuously reading the 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 major subcircuits of the system of FIG. 1. FIG. 2 shows a processing unit 1101 receiving body part motion data (e.g., tremor vibration data) from an inertial measurement unit 1108 in the IMU circuit 22. This data is used by the processing unit 1101 to drive a mechanical transducer 1105 in the mechanical transducer circuit 23 at various frequencies and amplitudes. Optionally, the processing unit 1101 may also receive muscle activity data from an electromyography (EMG) sensor 1107 in the EMG circuit 24. The processing unit 1101 may transmit and receive data to and from a remote database 16 using a communication module 1104 in the communication circuit 25. The entire system receives power from a rechargeable battery 211 in the 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. The power is switched on and off via a power switch 213.

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

[0063] FIG. 4 is an exploded isometric view of a wearable device in which the vibration motor is housed in the band rather than in the main electronics housing, according to an embodiment of the present invention. The mechanical transducer 1105 is housed in the band 31 that interfaces with the user's wrist. Between the top half 321 and the bottom half 322 of the housing are a printed circuit board (PCB) 42, a silicone adapted to insulate the bottom of the PCB 42, and a rechargeable battery 26. The battery 26 includes protection circuitry 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. The magnetic connector 421 mates with a mating connector from an external charger, so that the battery 26 can be conveniently configured to be charged by an external charger. The magnetic connector 421 allows patients who have difficulty performing tasks requiring fine motor skills to easily charge the device with a magnetic charging cable. The device is intended to function by pressing a single large button 323 on the top of the electronics housing top 321 after the patient turns the device on. The buttons are provided for ease of use by patients whose fine motor control is affected by neurological movement disorders.

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

[0065] Figure 6 shows one embodiment of the invention from above as it would appear on the hand, showing the main electronics housing 32 and the band 31 that interfaces with the user's wrist.

[0066] Figure 7 shows one embodiment of the invention from a side view as worn on the hand. Figure 7 shows the main electronics housing 32, the band 31 that interfaces with the user's wrist, and an on / off button 323 that the patient can use to start / stop the vibration stimulation. The on / off button 323 is integrated into the main electronics housing 32.

[0067] Figure 8 shows a test setup for a medical wearable device that can be used for more rigorous data collection according to an embodiment of the invention. Figure 8 shows the main electronics housing 32 and the band 31 that interfaces with the user's wrist. These are connected to a data logging device 81 that collects and stores data. This test setup has a larger processor and storage capacity and can continue to collect and store data on a larger time scale than the device alone. During analysis, more complex and computationally intensive data analysis can be performed on the collected data stored in the data logging device 81 utilizing the larger processor.

[0068] FIG. 9 illustrates a wearable device for mitigating movement disorders that 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 computation 912 and sensing 911 are offloaded to the third-party wearable device 91. The third-party device then wirelessly transmits a set of motor commands (e.g., via Bluetooth 913, 925) to a processing unit 1101 on the accessory band 92. This processing unit 1101 interfaces with a 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), whose 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] 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 one embodiment of the present invention. It shows the main electronics 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 placement of the accessory battery 925 and the processing unit 1101.

[0070] FIG. 11 illustrates a process by which a set of stimulation parameters may be calculated using raw sensor inputs, according to an embodiment of the present invention. The stimulation parameters are continuously updated in a closed loop. These parameters may 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 raw sensor inputs 112 are used to filter out transducer / sensor crosstalk 113 by subtracting it from the sensed waveform using knowledge of the output waveform, or by limiting sensing to the "off" phase of the pulsed stimulation using knowledge of the timing of the output waveform. This filtering then allows for feature extraction 114 of the raw sensor inputs 112. A stimulation selection algorithm 115 then uses the current stimulation parameters 111 and the extracted features 114 to select new stimulation parameters 116. This process is illustrated in more detail in FIG. 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 the filtered sensor signal 113 and extracts temporal features 1141, 1142, 1143 and / or spectral features 1144 as described with respect to Figure 11. Examples of common temporal features include minimum, maximum, first three standard deviation values, signal energy, root mean square (RMS) amplitude, zero crossing rate, principal component analysis (PCA), kernel or wavelet convolution, or autoconvolution. Examples of common spectral features include Fourier transform, fundamental frequency, (Mel frequency) cepstral coefficients, spectral centroid, and bandwidth. The features are extracted with standard digital signal processing techniques implemented in the main processing unit of the device. The collected set of features is then fed to a stimulus selection algorithm 115.

[0072] Figure 13 illustrates a stimulation optimization algorithm 115, according to one embodiment of the present invention. The stimulation selection algorithm takes the extracted features 114 and the current stimulation parameters 111 and uses them to determine a set of new stimulation parameters 116. The process by which the new parameters are determined is an optimization 1151 for minimizing symptom severity. Given that there is no analytical model of the response of symptoms to stimulation patterns, 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 guided policy search. Descriptions of several such algorithms can be found in Deisenroth, MP (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, DR, & Martin, R. (1993), "An Overview of Genetic Algorithms: Part 1, Fundamentals", 1-8, each of which is incorporated by reference in its entirety.

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

[0074] Two exemplary stimulus selection algorithms that may be used in embodiments are as follows: JPEG0007644532000001.jpg158124

[0075] In some embodiments, the structure of the output stimulation pattern may be a weighted average of the optimized 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 the pattern optimized to relieve the most severe symptoms.

[0076] 14 shows a neurological signal cancelling system illustrating how the wearable device 11 and the body 12 interact according to one embodiment of the invention where the body and device are collectively considered a motion 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 body movements and transmits a counter control signal 142 defined by the output of an active noise cancelling algorithm 143. The control signals 141, 142 undergo a signal cancelling process within the user's nervous system 1203 resulting in a smoother perceived movement signal 144.

[0077] FIG. 15 illustrates a process by which raw sensor inputs 112 may be processed using active noise cancellation to calculate a set of stimulation parameters, according to an embodiment of the present invention. In FIG. 15, a set of dyskinesia sensors 1106 in device 11 quantifies actual limb position and movement 1206 to generate raw sensor inputs 112, which are then filtered using both noise filter 113 and dyskinesia filter 151. Noise filter 113 may use knowledge of the output waveform to subtract from the sensed waveform, or use knowledge of the timing of the output waveform to limit sensing to the "off" phase of the pulsed stimulation. Dyskinesia filter 151 uses a 0-15 Hz bandpass filter to remove other signal components that are not attributable to dyskinesia. The resulting filtered sensor data 152 is then fed to active noise cancellation processor 153, which generates a tremor suppression stimulation signal 154. Active noise cancellation processor 153 is described in more detail in connection with FIGs. 16 and 17. The device 11 transmits the tremor suppression stimulation signal 154 to the user's body's proprioceptors 1201 using vibration stimulation 13. Information regarding the body's response to the tremor suppression stimulation signal can be found in further detail in connection with FIG.

[0078] 16 illustrates one example embodiment of the process of generating a tremor suppression stimulation signal 154 by an active noise canceling (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. The fundamental frequency ω maxis selected through argmax 1532 of the frequency domain data from the Fourier transform 1531, and the output is used to center a bandpass filter 1533 to ensure an 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 the time delay 1534 in degrees required to achieve a phase offset such that the resulting signal acts as negative feedback. The time delay to achieve a phase offset of x is

number

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

[0080] The first method 174 uses the lowest peak frequency to calculate the window size. Selecting the lowest peak frequency ensures that all relevant features of limb acceleration 171 are captured in the window and can be properly reproduced when generating the tremor suppression stimulation signal 154. This method involves inverting the lowest peak frequency, which corresponds to the lowest frequency feature of limb acceleration 171, and transforming it to the time domain [Hz=1 / s]. In FIG. 17, the lowest peak frequency is 3 Hz, so the window size is 1 / 3 s.

[0081] The second method 175 uses a fixed length window size. The acceleration data captured with the selected fixed window size is then inverted and becomes the output of the tremor suppression stimulation signal 154. A lower limit for the acceptable window size is determined using the first method 174, i.e., the time domain transformation of the lowest peak frequency. A window smaller than this cannot capture all relevant features of the limb acceleration 171. In theory, there is no upper limit for 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 Archimedes spiral drawings 181 and 182 by a tremor patient without and with treatment with a device according to an embodiment of the present invention. The spiral tracing test allows a physician to gain insight into the frequency, amplitude, and direction of a patient's tremor. It can also alert the physician to abnormal movements of hypokinesia, dystonia, and tremors. The task requires the patient to continuously trace an Archimedes spiral. A patient with tremor has difficulty tracing the spiral and deviates from the spiral line while tracing, resulting in a messy spiral 181. 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 searching a parameter configuration space used in an embodiment of the present invention for symptom relief. This is a graphical representation of a stimulation selection algorithm 193 associated with the present invention. The algorithm 193 moves through a stimulation parameter space 192 and attempts to minimize symptom severity 191. The movement through the stimulation parameter space involves trying different stimulation parameter sets and comparing the resulting symptom severity as quantified by the respective sensors. The algorithm attempts to minimize symptom severity by testing different parameter sets until an optimal set is found for minimizing symptom severity.

[0084] Diagnostic Uses. An alternative bench-top version of the device can be used to induce tremor in Parkinson's patients for the purpose of early detection. This is done using the same mechanism as tremor reduction, but using an inverse stimulation parameter search heuristic. User testing has shown that for each patient, there is a stimulation pattern that results in a very large tremor when applied to Parkinson's patients with very slight tremor. This effect does not occur in users who do not have Parkinson's. This phenomenon can be used for early detection and diagnosis of Parkinson's disease, which can be difficult to diagnose.

[0085] Patient Study. Figures 20 and 21 show power spectral density (PSD) plots of postural tremor for Parkinson's disease and essential tremor patients, respectively, with and without a device according to an embodiment of the present invention. Data was obtained by having each patient extend their hand for 10 seconds, with and without the device. In Figures 20 and 21, tremor amplitude is compared with and without the device.

[0086] A test example of one embodiment of the present invention is described below. Participants were asked to trace a printed Archimedes spiral, a common test used to diagnose Parkinson's disease, with and without the device, as shown in Figure 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 about 20 participants with Parkinson's disease and one participant with resting tremor. However, the majority of participants either did not experience tremor or had already been treated for Parkinson's disease and experienced only slight tremor. It was observed that the reduction in tremor severity was strongly correlated with the initial tremor severity. That is, patients with minimal tremor experienced the least benefit, and patients with more extreme tremor experienced more dramatic benefits. The participant with the most severe postural tremor caused by Parkinson's disease saw the greatest improvement in performance, as shown in Figure 20. Another participant with postural tremor caused by essential tremor also showed significant improvement, as shown in Figure 21. The results were reproducible in both of these participants. Participants suffering from rigidity observed a greater range of hand motion and completed the spiral test faster with the device than without it.

[0087] 22 and 23 show the tremor response of 21 PD and 8 ET patients, respectively, at peak tremor displacement when stimulation was applied using a process of tremor suppression stimulation signal generation by varying the phase rotation of the stimulation and selecting the phase rotation that resulted in the greatest tremor reduction. Using this method, the frequency of stimulation was selected by an active noise canceling (ANC) processor and therefore varied between patients, but each patient's test session was performed at only one frequency.

[0088] 24 and 25 show the tremor responses of 19 PD and 6 ET patients, respectively, at peak tremor displacement when stimulation was applied using the process of generating a tremor suppressing stimulation signal by varying the frequency of stimulation and selecting the frequency that resulted in the greatest tremor reduction. Phase rotation was not adjusted.

[0089] A test example of one embodiment of the present invention will be described below. Participants were asked to perform several tasks in which tremors were observed with or without stimulation. The tasks were extracted from the validated scales for upper limb tremor assessment in both PD and ET, MDS-UPDRS (Movement Disorder Society-Unified Parkinson's Disease Rating Scale) and TETRAS (The Essential Tremor Rating Assessment Scale), respectively. To assess postural tremor, participants were asked to extend their arms in front of their bodies. To assess kinetic tremor, participants were asked to start with their arms extended, then bring their fingers back to touch their nose and return to the extended position. To assess resting tremor, participants were asked to relax their arms on a surface while closing their eyes and counting down from 100. During each task, participants performed the exercise for 80 seconds, and vibration stimulation was switched off and on every 20 seconds. During treatment stimulation, participants randomly start with either option A (treatment) or option B (no treatment) for 10 seconds, followed by a 10-second rest period to account for potential carryover effects. After the break, a crossover occurs, and participants who received option A receive option B for 10 seconds, and vice versa. Participants then rest for another 10 seconds before repeating the randomization and crossover once more. The results in Figures 22-25 show the best response for each participant.

[0090] Enveloped embodiments. Although the above embodiments refer to accelerometers, vibration motors, micro USB, and wristbands, the present invention is not limited to such implementations. Additionally, the above embodiments are not intended to limit the scope of the present invention. For example, various modifications and variations of interfaces, electromyographic sensor types, gyroscopes, inertial measurement units, piezoelectrics, electromagnets, permanent magnets, pneumatics, voice coils, hydraulics, and resistive heating elements should be included. The range of form factors should also include headbands, collars, 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 may be embodied in many different forms, including, but not limited to, computer program logic for use with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or 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 (ASICs)), or any other means including any combination thereof.

[0092] Computer program logic implementing all or part of the functionality previously described herein may be implemented in a variety of forms, including but not limited to source code form, computer executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, networker, or locator). Source code may include a series of computer program instructions implemented in any of a variety of programming languages ​​(e.g., object code, assembly language, or high-level languages ​​such as Fortran, C, C++, JAVA, or HTML) for use with a variety of operating systems or operating environments. Source code may define and use various data structures and communication messages. Source code may be in a computer executable form (e.g., via an interpreter) or the source code may be converted into a computer executable form (e.g., via a translator, assembler, or compiler).

[0093] The computer program may be fixed in any form (e.g., source code form, computer executable form, or intermediate form) permanently or temporarily 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., diskette or fixed disk), an optical memory device (e.g., CD-ROM), a PC card (e.g., PCMCIA card), or other memory device. The computer program may be fixed in any form of a signal transmittable to a computer using any of a variety of communication technologies, including, but not limited to, analog, digital, optical, wireless, networking, and internetworking technologies. The computer program may be distributed in any form as a removable storage medium with accompanying printed or electronic documentation (e.g., shrink-wrap software or magnetic tape), preloaded into a computer system (e.g., on a system ROM or fixed disk), or distributed from a server or electronic bulletin board via a communication system (e.g., the Internet or World Wide Web).

[0094] Hardware logic (including programmable logic for use in a programmable logic device) implementing all or a portion of the functionality previously described herein may be designed using conventional manual methods, or may be electronically designed, captured, simulated, or documented using a variety of tools, such as computer-aided design (CAD), hardware description languages ​​(e.g., VHDL or AHDL), or PLD programming languages ​​(e.g., PALASM, ABEL, or CUPL).

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

[0096] The embodiments of the present invention described above are intended to be merely exemplary. 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 in 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; Equipped with 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, based on the body part movement data, the stimulation signal output having a desired phase shift relative to a phase of the body part movement data at the fundamental frequency. device.

2. 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.

3. The device of claim 2 , 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.

4. 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.

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

6. 6. The device of claim 5, 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.

7. 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.

8. 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.

9. 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; Equipped with the processing unit is configured to operate in two modes: a first mode for passively monitoring the patient's movement to detect a dyskinetic symptom exceeding a threshold, and a second mode for mitigating the dyskinetic symptom after detection of such a dyskinetic symptom. device.

10. 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.

11. 8. The device of claim 7, wherein the wristband is constructed with hook-and-loop fasteners so that the wristband can be fastened with one hand for ease of use by individuals whose fine motor control is affected by neurological motor disorders.

12. 8. The device of claim 7, 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.

13. 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.

14. 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; Equipped with 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. device.

15. 15. The device of claim 14, 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.

16. 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 1 , further configured to provide active noise cancellation by:

17. 17. The device of claim 16, 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.

18. The device of claim 16 , wherein the processing unit is configured to set the window size to a fixed value.

19. 1. A method of controlling a device for alleviating a set of movement disorder symptoms in a subject, comprising: Depending on the device, 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 for controlling a device, including:

20. The method for controlling a device according to claim 19, wherein the set of movement disorder symptoms is selected from the group consisting of tremor, rigidity, bradykinesia, dyskinesia, the urge to move, and combinations thereof.

21. 20. The method of claim 19, wherein receiving by the device a set of sensor outputs related to movement of the subject's body part from a set of body part sensors includes operating in two modes: a first mode in which the device passively monitors patient movement to detect a movement disorder symptom above a threshold, and a second mode in which, upon detection of such a movement disorder symptom, the device enters into active mitigation of the movement disorder symptom.

22. 20. The method of claim 19, wherein actively processing the filtered signal by the device to convert the sensor output into frequency data includes applying a Fourier transform to the filtered signal by the device to generate transformed sensor output data.

23. 23. The method of controlling a device of claim 22, wherein actively processing the filtered signal by the device to convert the sensor output into frequency data includes: (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 stimulation signal a set of frequency data outside a specified range associated with the fundamental frequency.

24. Processing the sensor output by the device to generate the stimulus signal includes: The device 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; The device control method of claim 19 , further comprising:

25. 25. The method of claim 24, wherein selecting the window by the device includes inverting a lowest peak frequency of the peak frequencies by the device, and converting the inverted lowest peak frequency of the peak frequencies into a time domain signal.

26. 25. The method of claim 24, wherein selecting the window by the device includes setting the window to a fixed value by the device.

27. 1. A method of controlling a device for alleviating a set of movement disorder symptoms in a subject, comprising: Depending on the device, 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 by the device comprises: The device 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; Including, filtering the sensor output includes, by the processor: (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) generating an output of the stimulation signal based on the body part movement data, the stimulation signal having a phase shift relative to a phase of the body part movement data at the fundamental frequency, thereby providing active noise cancellation. How to control the device.

28. 28. The method of claim 27, wherein the processor is further configured to use displacement or power data to select the stimulation signal that results in the greatest relaxation effect.

29. 28. The method of claim 27, wherein the processor is further configured to receive input from a user to select the stimulation signal that results in the greatest relief effect.

30. The processor is further configured to apply a time delay to the filtered signal to produce a desired phase shift. A method for controlling a device according to claim 27.

31. Depending on the device, 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 relating to movement of a body part of a subject from a set of body part sensors; 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 motion data into frequency data, (b) determine a fundamental frequency of the motion of the body part using the frequency data, and (c) generate, based on the body part motion data, the train of stimulation signals having a phase shift relative to the phase of the body part motion data at the fundamental frequency; further processing the frequency data to determine a probability that the subject is afflicted with the movement disorder; A method for controlling a device, including:

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