Device and method for alleviating symptoms of neuromotor disorders using wearable devices

Abstract

This concerns a medical wearable device that alleviates the symptoms of neuromotor disorders. [Solution] 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 neuromotor disorders, and freeing the user from paralysis induced by the motor disorder. The device uses one or more mechanical transducers to stimulate the user's neural pathways in response to the user's stimulation patterns, which are then used by a processing unit to determine when stimulation is needed, thereby 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 medical wearable devices, and more particularly to medical wearable devices that alleviate 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 occasional freezing or inability to initiate movement. 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 temporary and involuntary immobility).

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

[0006] A second surgical option available to Parkinson's disease patients is Duopa therapy. Duopa therapy requires surgically creating a small opening (stoma) in the stomach to place a tube into the intestine. Duopa, which is similar to regular PD medication taken through tablets, is then pumped directly into the intestines, improving absorption and reducing the off-time of medication taken through tablets.

[0007] A similar disorder called essential tremor is often misdiagnosed as Parkinson's disease, but it is even more common, with an estimated 100 million cases worldwide. These tremors often occur during intentional movements and can worsen to the point where patients are no longer able to cut food, tie their shoelaces, or sign their names. Medications for essential tremor may 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 that affects an estimated 2.8 million people worldwide. Motor symptoms include weakness or fatigue, difficulty walking, rigidity or spasticity, and tremors. Most people experience a so-called "relapsing-remitting" disease course, which includes periods of new symptoms or relapses followed by periods of recovery. As the disease progresses, the onset of symptoms may follow a regular pattern, after which the disease is classified as "secondarily progressive." Some MS patients may experience a gradual onset and progression without any relapsing remissions, which is classified as "primary progressive." There is no cure for MS, and treatment typically focuses on recovery from relapsing attacks using physiotherapy and medication. Disease-modifying drugs reduce the incidence of relapses, but they do not help alleviate symptoms during relapses.

[0009] Restless Leg 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 at rest and are relieved when the legs are moving. As a result, RLS patients are compelled to move or shake their legs. This is particularly detrimental to the patient's sleep quality because they cannot remain still.

[0010] RLS is generally treated with dietary changes, medication, and / or physiotherapy. Dietary changes may include the elimination of caffeine, alcohol, and tobacco. Medications prescribed for RLS may include the same types of medications prescribed for Parkinson's disease (such as dopamine agonists and carbidopa-levodopa) and benzodiazepines (e.g., lorazepam, Xanax, Valium, and Ativan). Physiotherapy for RLS may include leg massage or electrical or vibrational 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, TJ, & Marar, U. (2010). This device is a lower leg sleeve having sensors and actuators, but it does not store or transmit data and does not address any other symptoms common to neuromotor disorders.

[0012] There are several pharmaceutical approaches to manage neuromotor disorders that work by promoting dopamine, a chemical produced by the brain that helps control bodily movement. This chemical is deficient in the brains of patients with conditions such as Parkinson's disease. Pharmaceutical treatments for Parkinson's disease are expensive, costing users thousands of dollars annually; they may be ineffective or their effects may quickly wear off; and they may actually be thought to promote neurodegeneration.

[0013] There is also an injection-based Botox treatment for more severe tremors, which costs tens of thousands of dollars per year and works by killing the nerves that cause the tremor. While this is effective in reducing tremors, the nerve killing also causes a significant decrease in motor skills. Additionally, this treatment is only available at very specialized treatment centers and is therefore not an option for the vast majority of patients.

[0014] While several devices exist that attempt to control unwanted movements using surface-based treatments, none have been proven to be completely non-invasive and effective. Many people have turned to electrical stimulation, which they have chosen as the mode of nerve stimulation, to alleviate unwanted movements. This can involve various devices and cumbersome procedures, such as gel pads or electrodes that require shaving for proper attachment. See U.S. Patent No. 8762065, "Closed-loop feedback-driven neuromodulation," DiLorenzo, DJ (2014). These devices only function after the electrical treatment has ended, and their effects have not been shown to last for extended periods, leading to the assumption that many of these cumbersome treatments 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, KH, Delp, SL, Paderi, J., Rajasekhar, V., & Altman, T. (2016), U.S. 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), and U.S. Patent No. 1,090,5879, "Methods for peripheral nerve stimulation," Wong, SH, Rosenbluth, KH, Hamner, S., Chidester, P., Delp, SL, Sanger, TD, & Klein, D. (2020). The aforementioned procedures have been shown to pose significant risks to patients with pacemakers and can also cause skin irritation. The aforementioned treatments provide only relief from tremors and do not provide relief from other symptoms of neuromotor disorders such as bradykinesia, gait freeze, dystonia, dyskinesia, or involuntary or compulsive rhythmic movements.

[0015] Perhaps the most relevant studies have emerged in recent years, demonstrating that the use of vibration may improve motor performance. The effectiveness appears to vary considerably, depending on the vibration frequency and the patient's condition. Macerollo et al., 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, demonstrated that 80 Hz peripheral tactile vibration can lead to slowing and reduction of repetitive hand movements. 70 Hz has been proven effective in post-stroke patients. 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, Scheidd 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, Scheidd RA, Schmit BD (2015), PLoS ONE 10(12):e0144377. It has been shown that even paralyzed muscles respond to frequencies of 150-160 Hz, and the effect of such vibration is seen in the sustained reduction of muscle weakness and spasticity in the treated muscle. See "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.As shown in the following three separate studies, vibration can even have a positive effect on rigidity or stiffness by increasing relaxation using vibrotactile stimulation: "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 positioned along a band. These actuators slide along the band and change position relative to each other to provide signals to 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] U.S. Patent Application Publication No. 20100249637 [Patent Document 2] U.S. Patent No. 8762065 [Patent Document 3] U.S. Patent No. 9452287 [Patent Document 4] U.S. Patent No. 9802041 [Patent Document 5] U.S. Patent No. 10905879 [Patent Document 6] U.S. 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, 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 modulating a set of motor impairment symptoms of a target. The device includes a housing and a mounting system configured to be attached to a body part of a target. 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 mounting system, configured to provide a set of mechanical outputs to the body part. The device also includes a processing unit, the processing unit having (i) an input for receiving body part movement data, operably coupled to the sensor outputs; (ii) noise filtering processing to remove noise unrelated to motor impairment symptoms from the body part movement data in order to generate filtered movement signals; (iii) filtered movement signal feature extraction processing to characterize the features of the filtered movement signals in order to generate a characterized filtered movement signal; and (iv) stimulation processing of the characterized filtered movement signal for generating a stimulation signal output, the output having stimulation processing to cause the transducers to provide mechanical stimuli to the body part that alleviate a set of motor impairment symptoms.

[0020] Alternatively or additionally, the processing unit is further configured to provide active noise cancellation by (a) converting body part motion data in the time domain into frequency domain data, (b) using the frequency domain data to determine the fundamental frequency of the motion disorder symptom, and (c) generating a stimulus signal output having a desired phase shift with respect to the phase of the body part motion data at the fundamental frequency, based on the body part motion data.

[0021] In other embodiments, the processing unit is further configured to provide a sequence of stimulus signals in the stimulus signal output, each signal in the sequence having a distinct set of parameters related to the alleviation of a set of motor impairment symptoms, and feature extraction processing includes 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 stimulus signal in the sequence has the greatest alleviation effect. In a preferred embodiment, the processing unit is further configured to select the stimulus signal that has been determined to have the greatest alleviation effect with respect to a continuous output to a transducer.

[0022] Optionally, the set of motor impairment symptoms is selected from a 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 patients with Parkinson's disease. Alternatively or additionally, the processing unit is further configured to control a set of mechanical transducers to mitigate freezing of gait in patients with Parkinson's disease by controlling a set of mechanical transducers. Optionally, the processing unit is configured to operate in two modes: a first mode in which it is configured to passively monitor the patient's movement to detect motor impairment symptoms exceeding a threshold, and a second mode in which, after detection of such motor impairment symptoms, the processor is configured to enter into active mitigation of the motor impairment symptoms.

[0023] Optionally, the attachment system includes a wristband, and a 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 easy to use for patients whose fine motor control is affected by neurological impairment. Optionally, the wristband is configured with hook-and-loop fasteners so that it can be fastened with one hand, making it easy to use for people whose fine motor control is affected by neurological impairment. Optionally, the wristband is configured to be stretchable via elastic deformation, making it easy to use for people whose fine motor control is affected by neurological impairment. Optionally, the device further includes a battery housed in a housing and a magnetic connector mounted within the housing to connect to the battery and to a mating connector for an external charger, so that the battery may be configured to be convenient for charging by patients lacking fine motor control.

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

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

[0026] Optionally, the body part sensor set includes an inertial motion unit (IMU) configured to calculate data representing the acceleration of the body part, and the active noise canceling processor is further configured to convert the body part acceleration data into frequency data by applying a Fourier transform, extract the peak frequency of the body part acceleration data from the frequency data, select a window size for the body part acceleration data based on the peak frequency, capture a portion of the sensor output based on the selected window size, and invert the captured portion to generate a stimulus signal. Alternatively or additionally, the active noise canceling processor is configured to select the window size by inverting the lowest peak frequency among the peak frequencies and converting the inverted lowest peak frequency among 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 embodiments of the present invention, a method is provided for alleviating a set of motor impairment symptoms of a target. The method includes sensing the movement of a body part of a target and providing a set of sensor outputs related to the movement of the body part. The method also includes processing the sensor outputs to generate a stimulus signal for alleviating the set of motor impairment symptoms. The processing includes filtering the sensor outputs to remove noise unrelated to the motor impairment symptoms in order to generate a filtered signal.

[0028] Optionally, the process also includes actively processing the filtered signal to (a) convert the sensor output into frequency data, (b) determine the fundamental frequency of the motor impairment symptom using the frequency data, (c) generate a stimulus 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 stimulus signal. The method further includes inputting the stimulus signal to a set of mechanical transducers coupled to a body part to alleviate a set of motor impairment symptoms.

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

[0030] Optionally, the set of motor impairment symptoms is selected from a group consisting of tremor, rigidity, bradykinesia, 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 motor impairment symptoms exceeding a threshold, and a second mode of actively mitigating such motor impairment symptoms after detection. 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 the fundamental frequency by applying an argmax function to the converted sensor output, and (ii) using a bandpass filter to remove from the stimulus signal a set of out-of-specified frequency data related to the fundamental frequency.

[0031] Optionally, processing sensor output to generate a stimulus signal includes calculating data representing the acceleration of a body part, converting the acceleration data of the body part into frequency data by applying a Fourier transform, extracting the peak frequency of the acceleration data of the body part from the frequency 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, inverting the captured portion, and generating a stimulus signal. Alternatively or additionally, selecting a 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 a window size includes setting the window size to a fixed value.

[0032] Another embodiment of the present invention provides a method for alleviating a set of motor impairment symptoms of a subject, the method comprising: monitoring the sensor output of a sensor configured to sense the 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 output by filtering the sensor output to remove noise unrelated to the motor impairment symptoms in order to produce a filtered signal, thereby generating a stimulus signal for alleviating the set of motor impairment symptoms; and using a fixed-frequency processor configured to (a) sequentially input a set of stimulus signals of varying frequency to a set of mechanical transducers, (b) convert the sensor output into displacement or power data, and (c) output a stimulus signal that produces the greatest alleviation effect.

[0033] In some embodiments, the processor is further configured to use displacement or power data to select a stimulus signal that produces the greatest relaxation effect. In some embodiments, the processor is further configured to receive user input to select a stimulus signal that produces the greatest relaxation effect.

[0034] According to some embodiments, a method for diagnosing a patient's motor impairment, (1) To induce symptoms of motor impairment, a processor is used to provide a set of mechanical transducers with the output of a series of stimulus signals in which the stimulus parameters are continuously changing. (2) A step of receiving body part motion data related to the movement of a body part from a set of body part sensors, (3) A step of filtering body part movement data to remove noise unrelated to movement disorder symptoms in order 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 the fundamental frequency of the movement of the body part. (5) A step of further processing the frequency data to determine the probability that the subject is suffering from a motor disorder, A method is provided that includes this.

[0035] The aforementioned features of the embodiment will be more readily understood by referring to the following detailed description, which is made with reference to the attached drawings. [Brief explanation of the drawing]

[0036] [Figure 1] This figure shows a system for alleviating motor impairment according to one embodiment of the present invention. [Figure 2] Figure 1 is an electrical circuit diagram highlighting the main subcircuits of the system. [Figure 3] This is an isometric view of a wearable device according to one embodiment of the present invention. [Figure 4] This is an exploded isometric view of a wearable device according to one embodiment of the present invention, in which the vibration motor is housed within a band rather than within the main electronic device housing. [Figure 5] This is an isometric view of a wearable device according to one embodiment of the present invention, which includes a loop mechanism that allows for one-handed adjustment of a band on the user's wrist. [Figure 6]This is a diagram of one embodiment of the device, viewed from above while worn on the hand. [Figure 7] This is a diagram of one embodiment of the device, viewed from the side when worn on the hand. [Figure 8] This figure shows a test configuration for a medical wearable device that can be used for more rigorous data collection, according to one embodiment of the present invention. [Figure 9] This figure shows a wearable device for mitigating movement disorders, which is an accessory for a third-party smartwatch or other computing wearable device, according to one embodiment of the present invention. [Figure 10] This 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] This figure shows a process for calculating a set of stimulus parameters using raw sensor input, according to one embodiment of the present invention. [Figure 12] This figure shows a feature extraction process according to one embodiment of the present invention. [Figure 13] This figure shows a stimulus optimization algorithm according to one embodiment of the present invention. [Figure 14] This figure shows a neurological signal cancellation system according to one embodiment of the present invention, illustrating how a wearable device interacts with the body. [Figure 15] This figure shows a process according to one embodiment of the present invention that can process raw sensor input using active noise cancellation and calculate a set of stimulus parameters. [Figure 16] This figure shows one exemplary embodiment of the process of generating tremor-suppressing stimulus signals by an active noise-canceling (ANC) processor. [Figure 17] Another exemplary embodiment of the process of generating tremor-suppressing stimulus signals by an ANC processor is shown. [Figure 18]This figure shows pairs of spiral drawings by Parkinson's disease patients under conditions of no and with treatment by the device, according to one embodiment of the present invention. [Figure 19] This figure shows an embodiment of a simple non-convex gradient descent optimization method by exploring a parameter configuration space, which is used in embodiments of the present invention to alleviate symptoms. [Figure 20] This figure shows power spectral density (PSD) plots of postural tremor in Parkinson's disease patients with and without using a device according to one embodiment of the present invention. [Figure 21] This figure shows power spectral density (PSD) plots of posture-dependent tremor in essential tremor with and without the use of a device according to one embodiment of the present invention. [Figure 22] This shows the tremor response in peak tremor displacement of 21 PD patients when stimulation was applied using a process of generating a tremor-suppressing stimulus signal by varying the phase rotation of the stimulus and selecting the phase rotation that yields the greatest tremor relief. [Figure 23] This shows the tremor response of eight ET patients at peak tremor displacement when stimulation was applied using a process of generating a tremor-suppressing stimulus signal by varying the phase rotation of the stimulus and selecting the phase rotation that yields the greatest tremor relief. [Figure 24] This shows the tremor response of 19 PD patients in terms of peak tremor displacement when stimulation was applied using a process of generating tremor-suppressing stimulus signals by varying the stimulation frequency and selecting the frequency that yields the greatest tremor relief. [Figure 25] This shows the tremor response of six ET patients at peak tremor displacement when stimulation was applied using a process of generating tremor-suppressing stimulus signals by varying the stimulation frequency and selecting the frequency that yields the greatest tremor relief. [Modes for carrying out the invention]

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

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

[0039] "Body parts" refer to parts of the human body such as limbs (for example, arms, legs, ankles, wrists, etc.) and the neck.

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

[0041] A "mechanical transducer" is a device having electrical inputs and mechanical outputs configured to provide physical stimulation to an object.

[0042] A "motor impairment sensor" is a sensor configured to provide measurements related to neurological motor impairments.

[0043] A "mounting system" is a system or device that has means for mechanically fixing component subsystems to the user's body.

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

[0045] A "band" is a flexible segment of a material that surrounds a body part or a portion of a body part for the purpose of fixing it in place, and can also accommodate one or more component subsystems.

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

[0047] The term "stimulus pattern" refers to an oscillatory stimulus characterized by several parameters, including frequency, amplitude, and waveform. "Stimulus pattern" can also refer to behavior on longer time scales where the aforementioned 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 the force applied through those body parts. Proprioceptors are 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 "neuromotor disorder" refers to any neurological condition that causes an abnormal increase or decrease in movement, whether voluntary or involuntary. These include, but are not limited to, ataxia, cervical dystonia, chorea, dystonia, functional motor disorders, Huntington's disease, multiple system atrophy (MSA), paresis, hemiplegia, quadriplegia, post-stroke motor disorders, myoclonus, Parkinson's disease (PD), parkinsonism, drug-induced parkinsonism (DIP), progressive supranuclear palsy (PSP), restless leg syndrome (RLS), tardive dyskinesia, Tourette syndrome, spasticity, rigidity, bradykinesia, tremor, essential tremor (ET), alcohol or drug withdrawal-induced tremor, drug-induced tremor, psychogenic tremor, resting tremor, behavioral 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 in the device's operation during which the device is conducting experiments or collecting and analyzing data for the purpose of estimating the optimal stimulus pattern.

[0051] "Computer process" refers to the execution of the described functions in a computer system using computer hardware (e.g., a processor, a field-programmable gate array, or other electronic combination logic, or similar devices), which may operate under the control of software, firmware, or a combination thereof, or independently of any of the aforementioned controls. All or part of the described functions may be performed by active or passive electronic components such as transistors or resistors. When using the term "computer process," it is not necessarily required to be a schedulable entity or a computer program or part thereof; however, in some embodiments, a computer process may be implemented by such a schedulable entity or a computer program or part thereof. Furthermore, unless the context specifically requires otherwise, a "process" may be implemented using two or more processors or two or more (single or multiprocessor) computers.

[0052] Wearable devices for treatment. The present invention generally relates to medical wearable devices, and more particularly to the alleviation of tremors, rigidity, bradykinesia, involuntary rhythmic movements, and freezing associated with neuromotor disorders by mechanical vibration stimulation of the tendon bundles of the wrist, as well as autonomous sensing, feedback, and adjustment. Embodiments of the device also include several considerations to facilitate ease of use by people with disabilities, to whom the invention is intended, including integration with third-party devices.

[0053] Embodiments of the present invention include systems and methods for treating symptoms of neuromotor disorders by stimulating proprioceptors. In some embodiments, the system is a wearable device. In some embodiments, the system and methods can be used for any neuromotor disorder, including but not limited to Parkinson's disease, essential tremor, post-stroke motor disorder, or restless limb syndrome. In some embodiments, symptoms treated include tremor, rigidity, bradykinesia, stiffness, hemiplegia, and freezing. In some embodiments, symptoms treated include muscle contractions caused by dystonia. In some embodiments, symptoms treated include the inability to locate 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 proprioceptors (proprioceptors) to alleviate symptoms by using a vibration motor positioned around the surface of the wrist. In some embodiments, the system repeats the frequency pattern and waveform of the stimulation to find the pattern that yields the greatest reduction in motor impairment symptoms. In some embodiments, the system uses random white noise subthreshold stimulation to leverage 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 the set of possible stimulation patterns, and the system assigns the stimulation pattern associated with the greatest measured reduction in the user's tremor amplitude for the tremor exhibited in the absence of stimulation.

[0055] In some embodiments, the device finds (learns) optimal stimulus parameters for use in symptom reduction by using sensor-based optimization, including, but not limited to, model-free reinforcement learning, genetic algorithms, and Q-learning. These parameters may include any quantities used to define the stimulus waveform, such as frequency, amplitude, phase, and duty cycle. In some embodiments, these learned parameters also describe the behavior of the stimulus pattern as it changes over time on longer time scales. In some embodiments, the device determines the optimal stimulus as a weighted average of the optimal stimuli for each of the independently observed symptoms, 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 severity of the tremor is twice that of the rigidity, the output stimulus would be twice the optimal tremor reduction pattern superimposed with one optimal rigidity reduction pattern. In some embodiments, the device senses all active symptoms and chooses to reduce only the symptom with the worst severity. In some embodiments, the device measures the user's RLS-induced sway via a sensor and assigns a pattern associated with the greatest reduction in the user's sway amplitude, where amplitude is the amplitude of the sensor signal and difference is defined relative to the amplitude observed when there is no stimulation from the device.

[0056] In some embodiments, the sensors coupled to the device are a combination of accelerometers, gyroscopes, IMUs, or other motion-based sensors. In some embodiments, the sensors coupled to the device also include electromyography (EMG) sensors that monitor muscle activation to sense tremor severity, rigidity, or motion due to RLS. In some embodiments, the device uses sensors housed in the device, such as accelerometers, pressure sensors, force sensors, gyroscopes, inertial measurement units (IMUs), or electromyography (EMG) sensors, to collect data on the amplitude and frequency of motion or characteristics of the user's symptoms, such as muscle activity. In some embodiments, the aforementioned data is stored via memory components housed within the device. In some embodiments, the aforementioned data is periodically aggregated for the purpose of large-scale data analysis by wired or wireless transfer of the data to a larger storage location not on the device.

[0057] In some embodiments, the actuator is a resistance 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 rotational 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 connect wirelessly (e.g., via Bluetooth) to the user's smartphone. In some embodiments, the device may be configured to provide contextualized data about the user's state. For example, the system can correlate the onset or severity of symptoms with time, activity level, medication, diet, other symptoms, etc. In some embodiments, this can be achieved by transmitting extracted sensor signal features to the user's smartphone. An 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 symptoms, time-coordinated, for review by the user and / or their doctor.

[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 taking 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 tremor is detected and revealed visually or by extracted features of the sensor data exceeding several preset thresholds. In some embodiments, this can be achieved autonomously by heuristically inverting a stimulus selection algorithm to converge on a stimulus 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 mitigating movement impairment according to one embodiment of the present invention. A wearable device 11 and the user's body 12 function as a system having inputs and outputs that can be manipulated to modify the desired outcome 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, which 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, which 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 a processing unit 1101. The processing unit 1101 receives local data 1103 and executes local algorithm 1102. Using the local data 1103, the processing unit 110 instructs the mechanical transducer 1105 to deliver a specific vibration stimulus 13 based on the result of local algorithm 1102. The proprioceptive nerve 1201 detects the vibration stimulus 13 and transmits the perceived position and movement 1202 of the limbs 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 alters or sustains their electrical activity 1206 and movement 1207. The local data 1103 collected by the sensor suite 1106 continues to be processed by local algorithm 1102 and continues to influence the output of the mechanical transducer 1105. The local data 1103 is also transmitted to the teleprocessing algorithm 14 via the communication module 1104 and to the 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 teleprocessing algorithm 14. The teleprocessing 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 Cloud 15 can affect how 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 is configured to passively monitor the patient's movements to detect movement impairments that exceed a threshold, and a second mode in which, after detection of such movement impairment, the processor is configured to enter into active mitigation of the movement impairment. In some embodiments, the processing unit 1101 enters into 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 an "off" state and switching to an "on" state when one of the sensor data features, such as amplitude, exceeds a preset threshold.

[0061] Figure 2 is an electrical circuit diagram highlighting the main subcircuits of the system in Figure 1. Figure 2 shows a processing unit 1101 that receives body part motion data (e.g., tremor vibration data) from an inertial measurement unit 1108 in an IMU circuit 22. This data is used by the processing unit 1101 to drive mechanical transducers 1105 in a 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 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 in a communication circuit 25. The entire system receives power from a rechargeable battery 211 in 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 is turned on / 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. Figure 3 shows the main electronic housing 32 of the device and the device band 31 that interfaces with the user's wrist.

[0063] Figure 4 is an exploded isometric view of a wearable device according to one embodiment of the present invention, in which the vibration motor is housed in a band rather than in a main electronics housing. The mechanical transducer 1105 is housed in a band 31 that interfaces with the user's wrist. Between the upper half 321 and 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 undesirable discharge. To recharge the battery 26, a magnetic connector 421 coupled to the battery and mounted in the housing is inserted into the PCB 42. The magnetic connector 421 is coupled to a mating connector from an external charger, so 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 using a magnetic charging cable. The device is intended to function when the patient presses a single large button 323 on the top of the upper part 321 of the electronic housing after turning on the device. The buttons are designed to be easy to use for patients whose fine motor control is affected by neurological disorders.

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

[0065] Figure 6 shows one embodiment of the device as seen from above when 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] Figure 7 shows one embodiment of the device as seen from the side when worn on the hand. Figure 7 shows the main electronic device housing 32, a band 31 that interfaces with the user's wrist, and an on / off button 323 that the patient can use to start / stop vibration stimulation. The on / off button 323 is integrated into the main electronic device housing 32.

[0067] Figure 8 shows a test configuration of a medical wearable device that can be used for more rigorous data collection according to one embodiment of the present invention. Figure 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. Because 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. During analysis, the larger processor enables more complex and computationally intensive data analysis on the collected data stored in the data logging device 81.

[0068] Figure 9 shows a wearable device for mitigating motor impairment, according to one embodiment of the present invention, which is an accessory to a third-party smartwatch or other computing wearable device 91. 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 transmits a set of motor commands wirelessly 94 (e.g., via Bluetooth 913, 925) to a processing unit 1101 on an accessory band 92. This processing unit 1101 interfaces with transducers 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 electromyography sensor), and its signals are communicated to the accessory processing unit 1101 and the third-party processing unit 912 via wireless communication 913, 925, 94. The 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 one embodiment of the present invention. It shows a main electronics housing 32 and a band 31 housing a mechanical transducer 1105. The band 31 interfaces with the user's wrist. This figure shows an example of the installation of an accessory battery 925 and a processing unit 1101.

[0070] Figure 11 shows a process according to one embodiment of the present invention in which a set of stimulus parameters can be calculated using a raw sensor input. The stimulus parameters are continuously updated in a closed loop. These parameters may include any quantities used to define the stimulus waveform, such as frequency, amplitude, phase, and duty cycle. In each iteration of the update loop, the current stimulus parameter 111 and the raw sensor input 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 the sense to the "off" phase of the pulsed stimulus using knowledge of the timing of the output waveform. This filtering then allows for feature extraction 114 of the raw sensor input 112. Next, a stimulus selection algorithm 115 uses the current stimulus parameter 111 and the extracted features 114 to select a new stimulus parameter 116. This process is shown in more detail in Figure 13. As the process is repeated, the previous new stimulus parameter 116 becomes the current stimulus parameter 111.

[0071] Figure 12 shows a feature extraction process 114 according to one 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, maximum, first three standard deviations, 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) cepstrum coefficients, spectral centroid, and bandwidth. The features are extracted using standard digital signal processing techniques mounted on the device's main processing unit. The collected set of features is then fed to a stimulus selection algorithm 115.

[0072] Figure 13 shows a stimulus optimization algorithm 115 according to one embodiment of the present invention. The stimulus selection algorithm takes in extracted features 114 and current stimulus parameters 111 and uses them to determine a new set of stimulus parameters 116. The process by which the new parameters are determined is an optimization 1151 to minimize the severity of the symptom. Given that there is no analytical model of the symptom response to the stimulus pattern, this optimization is inherently model-free. Examples of model-free policy optimization techniques include argmin (i.e., minimization across a set of input arguments), Q-learning, neural networks, genetic algorithms, differential dynamic programming, iterative quadratic regulators, and guided policy search. Some 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 in Beasley, D., Bull, DR, & Martin, R. (1993), "An Overview of Genetic Algorithms: Part 1, Fundamentals," 1-8 (the entire text is incorporated herein by reference).

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

[0074] Two exemplary stimulus selection algorithms that may be used in the embodiments are as follows: JPEG2026108811000002.jpg193151

[0075] In some embodiments, the structure of the output stimulus pattern may be a weighted average of 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 stimulus pattern may be a pattern optimized to alleviate the most severe symptom.

[0076] Figure 14 illustrates a neurological signal-canceling system illustrating how a wearable device 11 and a body 12 interact, according to one embodiment of the present invention in which the body and device are considered collectively as a motion system. The system includes the user's nervous system 1203 which transmits a control signal 141 to the body 12. In a further embodiment of the system, the wearable 11 senses the body's movement and transmits a counter-control signal 142 defined by the output of an active noise-canceling algorithm 143. The control signals 141, 142 undergo a signal-canceling process within the user's nervous system 1203, resulting in a smoother perceived motion signal 144.

[0077] Figure 15 shows a process according to one embodiment of the present invention in which a raw sensor input 112 can be processed using active noise cancellation to calculate a set of stimulus parameters. In Figure 15, a series of motion impairment sensors 1106 within the device 11 quantify the actual position and movement 1206 of the limbs to generate a raw sensor input 112, which is then filtered using both a noise filter 113 and a motion impairment filter 151. The noise filter 113 can subtract from the sensed waveform using knowledge of the output waveform, or limit the sense to the "off" phase of the pulsed stimulus using knowledge of the timing of the output waveform. The motion impairment filter 151 uses a 0-15 Hz bandpass filter to remove other signal components not attributable to motion impairment. The resulting filtered sensor data 152 is then fed to an active noise cancellation processor 153, which generates a tremor suppression stimulus signal 154. The active noise cancellation processor 153 is described in more detail in relation to Figures 16 and 17. Device 11 uses vibration stimulation 13 to transmit a tremor-suppressing stimulus signal 154 to the user's body's proprioceptors 1201. Information regarding the body's response to the tremor-suppressing stimulus signal can be found in more detail in relation to Figure 1.

[0078] Figure 16 shows one exemplary embodiment of the process by which the active noise-canceling (ANC) processor 153 generates a tremor-suppressing stimulus signal 154. The ANC processor 153 takes the resulting filtered sensor data 152 from Figure 15 as input, and this data is then transformed into the frequency domain by applying a Fourier transform 1531. The fundamental frequency ω is, by definition, the frequency with the maximum amplitude. maxThe fundamental frequency is selected via argmax 1532 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 the time delay 1534 in degrees required to achieve the phase offset so that the resulting signal acts as negative feedback. The time delay to achieve the phase offset of x is:

number

[0079] Figure 17 shows another exemplary embodiment of the process by which the ANC processor 153 generates tremor suppression stimulus signals 154. The ANC processor 153 acquires filtered sensor data 152 from the inertial motion unit (IMU) and calculates the resulting limb accelerations 171. The limb accelerations 171 in the time domain are then transformed into the frequency domain by applying a Fourier transform 1531, and the ANC processor 153 can extract the peak frequencies 172 of the limb accelerations using a common peak-finding algorithm, which acquires a set of data and returns the local maximum or set of peaks such that the data points on both sides of the peak are smaller than the local maximum. The ANC processor 153 selects a window size 173 of the accelerations 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 within the window and can be adequately reproduced when generating the tremor-suppressing stimulus 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 Figure 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. The acceleration data captured within the selected fixed window size is then inverted to become the output of the tremor-suppressing stimulus signal 154. The lower limit of the acceptable window size is determined using the first method 174, i.e., the time-domain transformation of the lowest peak frequency. A smaller window cannot capture all relevant features of the limb accelerations 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] Figure 18 shows a pair of Archimedes spiral drawings 181 and 182 by a tremor patient under conditions of no and with device treatment according to one embodiment of the present invention. The spiral tracing test allows physicians to gain insights into the frequency, amplitude, and direction of the patient's tremor. It can also inform a physician of impaired motor function, dystonia, and abnormal movements of tremors. This task requires the patient to trace an Archimedean spiral in a continuous motion. Patients with tremors have difficulty tracing the spiral, often deviating from the spiral line while tracing, resulting in a disordered spiral 181. When the patient wears a device according to one embodiment of the present invention, they can trace the spiral more accurately, resulting in a smoother spiral 182.

[0083] Figure 19 shows an embodiment of a simple non-convex gradient descent optimization by exploring a parameter configuration space, used in embodiments of the present invention for symptom relief. This is a graphical representation of the stimulus selection algorithm 193 related to the present invention. The algorithm 193 attempts to minimize the symptom severity 191 by traversing the stimulus parameter space 192. The movement through the stimulus parameter space involves trying different sets of stimulus parameters and comparing the resulting symptom severity quantified by each sensor. The algorithm attempts to minimize the symptom severity by testing different sets of parameters until an optimal set for minimizing the symptom severity is found.

[0084] Diagnostic use. A benchtop alternative to the device can be used to induce tremors in Parkinson's disease patients for the purpose of early detection. This is done using the same mechanism as tremor reduction, but with a reverse stimulation parameter search heuristic. User trials have shown that, for each patient, there is a stimulus pattern that, when applied to Parkinson's disease patients with very slight tremors, produces 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 study. Figures 20 and 21 show power spectral density (PSD) plots of postural tremor in Parkinson's disease patients and essential tremor patients with and without a device according to one embodiment of the present invention, respectively. Data were obtained by having each patient extend their arm for 10 seconds, regardless of whether the device was used or not. In Figures 20 and 21, tremor amplitude is compared with and without the device.

[0086] The following describes a test example of one embodiment of the present invention. 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 Figure 18. The results were measured using image processing software to evaluate the accuracy of the traced spiral. In the first trial, the device was tested on approximately 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 received treatment for Parkinson's disease and experienced only slight tremor. A strong correlation was observed between the reduction in tremor severity and the initial tremor severity. That is, patients with the least tremor experienced the smallest benefit, while patients with more extreme tremor experienced a more dramatic benefit. The participant with the most severe postural tremor caused by Parkinson's disease showed the greatest improvement in performance, as shown in Figure 20. Another participant with postural tremor caused by essential tremor also showed a significant improvement, as shown in Figure 21. The results were reproducible in both of these participants. Participants suffering from rigidity were observed to have a greater range of motion in their hands and to complete the spiral test faster when using the device than when not using it.

[0087] Figures 22 and 23 show the tremor responses of 21 PD patients and 8 ET patients, respectively, at peak tremor displacement when stimuli were applied using a process of generating tremor-suppressing stimulus signals by varying the phase rotation of the stimulus and selecting the phase rotation that yielded the greatest tremor relief. Using this method, the stimulus frequency was selected by an active noise-canceling (ANC) processor and therefore varied between patients, although each patient's test session was performed at only one frequency.

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

[0089] The following describes a test example of one embodiment of the present invention. Participants were asked to perform several tasks in which tremors were observed with or without stimulation. The tasks were extracted from validated scales for evaluating upper limb tremors 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 tremors, participants were asked to extend their arms in front of their bodies. To assess motor tremor, participants were asked to start with their arm extended, then to return their fingers to touch their nose, and then return to the extended position. To assess resting tremor, participants were asked to relax their arm on a surface while closing their eyes and counting down from 100. During each task, participants performed the movement for 80 seconds, with the vibration stimulus switching off and on every 20 seconds. During the treatment stimulus, participants randomly started 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 rest, a crossover was performed, with participants who received option A receiving option B for 10 seconds, and vice versa. Participants then rested 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] Enclosed embodiments. While the embodiments described above refer to accelerometers, vibration motors, micro USB, and wristbands, the present invention is not limited to such implementation forms. Additionally, the embodiments described above do not limit the scope of the present invention. For example, various modifications and variations of interfaces, types of electromyography sensors, gyroscopes, inertial measurement units, piezoelectrics, electromagnets, permanent electromagnets, pneumatics, voice coils, hydraulics, and resistance 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 can be embodied in many different forms, but is not limited to computer programmable 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 functions described herein can be implemented in a variety of forms, including but not limited to source code, computer executable, and various intermediate forms (e.g., forms generated by an assembler, compiler, networker, or locator). Source code may include a set of computer program instructions implemented in any of the various programming languages ​​for use in various operating systems or operating environments (e.g., object code, assembly language, or high-level languages ​​such as Fortran, C, C++, Java, or HTML). Source code may define and use various data structures and communication messages. Source code may be computer executable (e.g., via an interpreter), or source code may be converted to computer executable (e.g., via a translator, assembler, or compiler).

[0093] Computer programs may be permanently or temporarily fixed in any format (e.g., source code format, computer executable format, or intermediate format) on tangible storage media such as semiconductor memory devices (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), magnetic memory devices (e.g., diskettes or fixed disks), optical memory devices (e.g., CD-ROMs), PC cards (e.g., PCMCIA cards), or other memory devices. Computer programs may also be fixed in any format of signals that can be transmitted to a computer using any of a variety of communication technologies, including but not limited to analog, digital, optical, wireless, networking, and internetworking technologies. Computer programs may be distributed in any format as removable storage media with accompanying print or electronic documentation (e.g., shrink-wrapped software or magnetic tape), pre-loaded onto a computer system (e.g., on system ROM or fixed disk), or distributed from a server or 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) that implements all or part of the functions described herein may be designed using conventional manual methods, or may be designed, captured, simulated, or documented electronically using various 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] While the present invention has been specifically described with reference to certain embodiments, it will be understood by those skilled in the art that various modifications in form and detail can be made without departing from the spirit and scope of the invention as defined by the appendices. Some of these embodiments are described in the claims by process steps, but the present invention also includes devices comprising a computer having a relevant display capable of performing the process steps of the following claims. Similarly, the present invention also includes computer program products stored on a computer-readable medium, which include 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 illustrative. Numerous variations and modifications will be apparent to those skilled in the art. All such variations and modifications are intended to fall within the scope of the present invention as defined in the appended claims.

Claims

1. A wearable device for adjusting a set of target motor impairment symptoms, a. Housing and, b. An attachment system configured to be coupled to the housing and attached to a body part of the target, c. A set of body part sensors disposed within the housing and providing a set of sensor outputs related to the movement of the body part, d. A set of mechanical transducers coupled to the mounting system, configured to provide a set of mechanical outputs to the body portion, e. A processing unit having (i) an input for receiving body part motion data operably coupled to the sensor output, (ii) noise filtering for removing noise unrelated to the motion disorder symptoms from the body part motion data to generate a filtered motion signal, (iii) feature extraction for characterizing the features of the filtered motion signal to generate a characterized filtered motion signal, and (iv) stimulation processing for generating a stimulation signal output, wherein such output is operably coupled to a set of mechanical transducers, causing the mechanical transducers to provide mechanical stimuli to the body parts to modulate the set of motion disorder symptoms, A device equipped with the following features.

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

3. The device according to claim 1, wherein the processing unit is further configured to provide a sequence of stimulus signals in the stimulus signal output, each signal in the sequence having a distinct set of parameters relating to the alleviation of the set of motor impairment symptoms, the feature extraction process includes determining displacement or power data relating to the movement of the body part, and the processing unit is also configured to use the displacement or power data to determine which stimulus signal in the sequence has the greatest alleviation effect.

4. The device according to claim 3, wherein the processing unit is further configured to select the stimulus signal which is determined to have the greatest relaxation effect with respect to a continuous output to the mechanical transducer.

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

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

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

8. The device according to claim 1, wherein the mounting system includes a wristband, and the set of mechanical transducers is distributed around the entire circumference of the wristband.

9. The device according to claim 1, wherein the device is operated by buttons on the surface of the device, and the buttons are configured to be easy to use for patients whose fine motor control is affected by neurological disorders.

10. The device according to 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 motor impairment symptom exceeding a threshold, and a second mode for alleviating such motor impairment symptoms after detection.

11. The device according to claim 1, further comprising a battery disposed on the housing, and a magnetic connector coupled to the battery and mounted in the housing for coupling to a mating connector from an external charger, so that a patient lacking fine motor control can conveniently charge the battery.

12. The device according to claim 8, wherein the wristband is equipped with a hook-and-loop fastener so that it can be fastened with one hand, making it easy to use for a person whose fine motor control is affected by a neurological disorder.

13. The device according to claim 8, wherein the wristband is configured to be stretchable via elastic deformation so that it is easy to use for a person whose fine motor control is affected by a neurological disorder.

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

15. The device according to 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. The device according to 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 converted sensor output, and (ii) using a bandpass filter to remove from the stimulus signal output a set of frequency data 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 representing the acceleration of the body part, and an active noise canceling processor, By applying a Fourier transform, the acceleration data of the body part is converted into the frequency domain data. Extracting the peak frequency of the acceleration data of the body part from the frequency domain data, Selecting the window size for the acceleration data of the body part based on the aforementioned peak frequency, and Based on the selected window size, a portion of the sensor output is captured, and the captured portion is inverted to generate the stimulus signal output. The device according to claim 2, further configured to provide active noise cancellation.

18. The device according to claim 17, wherein, when selecting the window size, the processing unit inverts the lowest peak frequency among the peak frequencies and converts the inverted lowest peak frequency among the peak frequencies into the time domain.

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

20. A method for alleviating a set of target motor impairment symptoms, Receiving a set of sensor outputs related to the movement of the target body part from a set of body part sensors, Filtering the sensor output to remove noise unrelated to the motor impairment symptoms in order to generate a filtered signal, and, (a) converting the sensor output into frequency data; (b) determining the fundamental frequency of the motor impairment symptom using the frequency data; and (c) actively processing the filtered signal by applying a time delay to the filtered signal to generate a desired phase shift at the fundamental frequency, thereby generating a stimulus signal output. This involves processing the sensor output to generate a stimulus signal to alleviate the set of motor impairment symptoms, To alleviate the set of motor impairment symptoms, the stimulus signal output is transmitted to a set of mechanical transducers coupled to the body part. Methods that include...

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

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

23. The method according to claim 20, wherein actively processing the filtered signal to convert the sensor output into frequency data includes applying a Fourier transform to the filtered signal to generate converted sensor output data.

24. The method according to claim 23, wherein actively processing the filtered signal to convert the sensor output into frequency data includes (i) selecting the fundamental frequency by applying the 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 is, To calculate data representing the acceleration of the aforementioned body part, By applying the Fourier transform, the acceleration data of body parts is converted into the aforementioned frequency data, Extracting the peak frequency of the acceleration data of the body part from the frequency data, Based on the aforementioned peak frequency, select a window for the acceleration data of the body part, Based on the selected window, a portion of the sensor output is captured, and the captured portion is inverted to generate the stimulus signal. The method according to claim 20, further comprising:

26. The method according to claim 25, wherein selecting the window includes inverting the lowest peak frequency among the peak frequencies and converting the inverted lowest peak frequency among the peak frequencies into a time-domain signal.

27. The method according to claim 25, wherein selecting the window includes setting the window to a fixed value.

28. A method for alleviating a set of target motor impairment symptoms, To monitor the sensor output of a sensor configured to sense the movement of the body part in question, and to provide a set of sensor outputs related to the movement of the body part, The sensor output is processed to generate a stimulus signal to alleviate the set of motor impairment symptoms, To deliver the stimulus signals to a set of mechanical transducers coupled to the body parts in order to alleviate the set of motor impairment symptoms, Including processing the sensor output, Filtering the sensor output to remove noise unrelated to the motor impairment symptoms in order to generate a filtered signal, and, (a) Using a processor configured to provide the set of mechanical transducers with an output of a sequence of stimulus signals in which the stimulus parameters change continuously, (b) convert the sensor output into displacement or power data, and (c) output the stimulus signal that produces the greatest relaxation effect. Methods that include...

29. The method according to claim 28, wherein the processor is further configured to use displacement or power data to select the stimulus signal that produces the greatest relaxation effect.

30. The method according to claim 28, wherein the processor is further configured to receive input from a user and to select the stimulus signal that produces the greatest relaxation effect.

31. A method for diagnosing motor impairment in a body part suspected of being disabled, To induce symptoms of motor impairment, a processor is used that is configured to provide a set of mechanical transducers with the output of a series of stimulus signals in which the stimulus parameters are continuously changing. Receiving body part motion data related to the movement of the body part from a set of body part sensors, The body part movement data is filtered to remove noise unrelated to the symptoms of the movement disorder in order to generate a filtered signal, The filtered signal is actively processed to (a) convert the body part motion data into frequency data, and (b) determine the fundamental frequency of the motion of the body part using the frequency data. Further processing of the frequency data to determine the probability that the subject is suffering from the motor disorder, Methods that include...

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