WO2021158807A1

WO2021158807A1 - Wearable biofeedback therapeutic medical device

Info

Publication number: WO2021158807A1
Application number: PCT/US2021/016642
Authority: WO
Inventors: Ryanne Noor RAMANDAN
Original assignee: Neuroform Inc.
Priority date: 2020-02-04
Filing date: 2021-02-04
Publication date: 2021-08-12

Abstract

The present invention provides a system and a method for measuring gait data and providing stimulation therapy using a first and a second ankle wearable device. The first and the second ankle wearable device are enabled to worn on a lower limb of a first leg and a second leg of a user. Each ankle wearable device comprises a stimulation module, a sensor, and a wireless module. The sensor is configured to measure gait data associated with the first leg and the second leg. The wireless module is configured to transmit the gait data associated with the first leg and the second leg to a processing element. The processing element is configured to generate a first input and a second input in response to computation of the gait data. The stimulation module is configured to provide stimulation therapy based on the first input and the second input.

Description

WEARABLE BIOFEEDBACK THERAPEUTIC MEDICAL DEVICE

FIELD OF THE INVENTION

The present invention is related to a system and a method for providing stimulation therapy to a user, and is more specifically related to measuring gait data using an ankle wearable device.

BACKGROUND OF THE INVENTION

The background of the invention section is provided merely to help understand the context of the invention and its application and uses, and may not be considered prior art.

Parkinson’s disease is a cognitive disorder that leads to shaking, stiffness, and difficulty with walking, balance, and coordination. Parkinson’s symptoms usually begin gradually and worsen over time. As the disease progresses, those afflicted may have difficulty walking and talking. They may also experience mental and behavioral changes, sleep problems, depression, memory difficulties, and fatigue. Both men and women may develop Parkinson’s disease. However, the disease affects about 50 percent more men than women. One clear risk factor for Parkinson’s is age. Although most people with Parkinson’s first develop the disease at about age 60, about 5 to 10 percent of people with Parkinson’s have “early-onset” disease, which begins before the age of 50. Early-onset forms of Parkinson’s are often, but not always, inherited, and some forms have been linked to specific gene mutations. Nerve cells, or neurons, produce an important brain chemical known as dopamine. When the neurons in an area of the brain that controls movement die or become impaired, they produce less dopamine, which causes the movement problems of Parkinson’s patients. Scientists are still researching the cause of death of cells that produce dopamine.

The four main gait-related symptoms associated with Parkinson’s are: 1) tremor (trembling) in hands, arms, legs, jaw, or head; 2) stiffness of the limbs and trunk; 3) slowness of movement; and 4) impaired balance and coordination, which sometimes lead to falls. The patient suffering from Parkinson’s may also suffer from depression and other emotional changes, or experience difficulty swallowing, chewing, and speaking. Other symptoms include urinary problems, constipation, skin problems, and sleep disruptions.

Parkinson’s disease often leads to a shuffling gait and impaired balance. A cane or a wheeled walker is traditionally used to help improve the ability of the patient suffering from Parkinson’s to move. A laser cane or a walker can help a patient when the patient freezes-up while walking. The laser cane can beam a color line across the patient’s path. This visual cue helps break freezing episodes. The problem that needs consideration is to reduce the number of falls of the patient suffering from neurological disorder while walking.

The prior research does not solve the problem of patients suffering from neurological disorders by providing them a solution that is discrete, easy to use, and affordable, and that reduces risk of falls, freezing, and shuffling assistance and feedback on mobility. Also, there is no easy and affordable way in which patients can undergo this therapy from the comfort of their own home. The movement of patients suffering from neurological disorders can change daily, which makes it very difficult for the clinicians to understand patient mobility. It would be an advancement in the state of the art to provide therapeutics combined with data analytics to maximize patient care and improve information flow between the clinician and the patient.

It is against this background that various embodiments of the present invention were developed.

SUMMARY OF THE INVENTION

The present invention provides a system and a method of stimulation therapy using a first ankle wearable device, that is enabled to be worn on a lower limb of a first leg of a user. In addition, a second ankle wearable device is enabled to be worn on a lower limb of a second leg of the user.

In one aspect, and according to one illustrative embodiment, the system comprises a first ankle wearable device and a second ankle wearable device. The first ankle wearable device comprises a first stimulation module, a first sensor, and a first wireless module. The first stimulation module is enabled to provide stimulation therapy to the lower limb of the first leg. Further, the first sensor is enabled to measure gait data associated with the first leg and transmits the gait data using a first wireless module to a processing element. The second ankle wearable device is enabled to be worn on the lower limb of the second leg. The second ankle wearable device comprises a second stimulation module, a second sensor, and a second wireless module. The second stimulation module is enabled to provide stimulation therapy to the lower limb of the second leg. The second sensor measures gait data associated with the second leg. The second wireless module transmits the gait data of the second sensor to the processing element. The processing element determines a first input and a second input in response to the computation of the gait data associated with the first leg received from the first wireless module and the second leg received from the second wireless module. The first input is transmitted through the first wireless module and the second input is transmitted through the second wireless module to the first stimulation module and the second stimulation module, respectively.

In one exemplary embodiment, the processing element is enabled to transmit the first input for the first stimulation module and the second input for the second stimulation module to at least one of the first wireless module and the second wireless module. The second wireless module is further configured to transfer the gait data associated with the second leg to the first wireless module. The processing element receives the gait data associated with the second leg from the second wireless module via the first wireless module. Further, the processing element transmits the second input for the second stimulation module to the first wireless module. The first wireless module further transmits the second input which is received from the processing element to the second stimulation module through the second wireless module.

In one exemplary embodiment, the first ankle wearable device and the second ankle wearable device comprises at least one battery. The first ankle wearable device and the second ankle wearable device, of the stimulation therapy system, are further selected from the group consisting of an ankle wearable device, a medium length sock device, a long sock device, and a warmer sock device.

In yet another exemplary embodiment, the first stimulation module and the second stimulation module, of the stimulation therapy system, are further selected from the group consisting of a vibrational stimulation module and an electrical stimulation module.

In yet another exemplary embodiment, the stimulation therapy system of the present invention is having the first sensor and the second sensor, which are selected from the group consisting of an accelerometer sensor, a gyroscope sensor, a magnetometer sensor, and GPS (global positioning system) sensor.

In yet another exemplary embodiment, the stimulation therapy system is an open loop system, wherein in the open loop system, the first input and the second input are generated using a predefined criterion, in response to the computation of the gait data associated with the first leg and the gait data associated with the second leg.

In yet another exemplary embodiment, the stimulation therapy system is a closed loop system, wherein in the closed loop system, the first input for the first stimulation module and the second input for the second stimulation module are to be adjusted by the processing element in response to the computation of the gait data associated with the first leg and the second leg, after receiving stimulation therapy by the first stimulation module in accordance with the first input, and by the second stimulation module in accordance with the second input. The first stimulation module and the second stimulation module then, provide the stimulation therapy using the adjusted first input and the adjusted second input.

In yet another embodiment, the present invention provides a method for providing stimulation therapy. The method comprises measuring gait data associated with the first leg and the second leg, by the first sensor and the second sensor respectively, of the first ankle wearable device and the second ankle wearable device, enabled to be worn on the lower limb of the first leg and the second leg. The measured gait data associated with the first leg and the second leg, are further transmitted by a first wireless module of the first ankle wearable device and the second wireless module of the second ankle wearable device respectively, to the processing element. The first input and the second input for the first ankle wearable device of the first stimulation module and the second ankle wearable device of the second stimulation module respectively is determined in response to the computation of the gait data associated with the first leg and the second leg, received from the first wireless module and the second wireless module. The stimulation therapy is provided to the first leg and the second leg, by the first stimulation module of the first ankle wearable device and the second stimulation module of the second ankle wearable device respectively, in accordance with the received first input from the first wireless module and the second input from the second wireless module.

In yet another embodiment, the present invention provides the first ankle wearable device enabled to be worn on the lower limb of the first leg for providing stimulation therapy. The first ankle wearable device comprises the first stimulation module, the first sensor, and the first wireless module. The first sensor is configured to measure gait data associated with the first leg. The first wireless module is configured to transmit the gait data to a processing element. A first input is generated by the processing element, in accordance with the received gait data associated with the first leg. The first stimulation module is enabled to provide the stimulation therapy in response to the first input, received by the first wireless module from the processing element.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention described herein are exemplary, and not restrictive. Embodiments will now be described, by way of examples, with reference to the accompanying drawings. In these drawings, each identical or nearly identical component that is illustrated in various figures is represented by a reference number. For purposes of clarity, not every component is labeled in every drawing. The drawings are not necessarily drawn to scale, with emphasis instead being placed on illustrating various aspects of the techniques and devices described herein.

The foregoing and other objects, aspects, and advantages are better understood from the following detailed description of a preferred embodiment of the invention with reference to the drawings, in which:

FIG. 1 illustrates a perspective view of a user suffering from Parkinson’s disease.

FIG. 2 illustrates a pictorial view of a stimulation therapy system, in accordance with a preferred embodiment of the present invention.

FIG. 3 illustrates a schematic view of a first ankle wearable device and a second ankle wearable device, in accordance with an exemplary embodiment of the present invention.

FIG. 4 illustrates a pictorial view of the first ankle wearable device and the second ankle wearable device, in accordance with an exemplary embodiment of the present invention.

FIG. 5 illustrates a pictorial front side view of the ankle wearable device, in accordance with an exemplary embodiment of the present invention.

FIG. 6 illustrates a block diagram of the stimulation therapy system, in accordance with an exemplary embodiment of the present invention.

FIG. 7 is a flow chart illustrating the working of the ankle wearable device, in accordance with an exemplary embodiment of the present invention.

FIG. 8 is a flow chart illustrating operation of the ankle wearable device in an active mode, in accordance with an exemplary embodiment of the present invention.

FIG. 9 illustrates a graphical and pictorial representation of the vibration power provided to the left leg and the right leg of the user when the user is wearing the ankle wearable device on both legs.

FIG. 10 illustrates a pictorial view of the first ankle wearable device, the second ankle wearable device, a remote -control device, and an online portal, in accordance with an exemplary embodiment of the present invention. FIG. 11 illustrates a pictorial view of the online portal, in accordance with an exemplary embodiment of the present invention.

FIG. 12 is a flow chart illustrating a method for providing stimulation therapy, in accordance with an exemplary embodiment of the present invention.

FIG. 13 illustrates a schematic view of a server, in accordance with an exemplary embodiment of the present invention.

FIG. 14 is an illustrative schematic representative of a user computing entity (such as a mobile device) that can be used in conjunction with embodiments of the present disclosure.

FIG. 15 shows an illustrative system architecture for implementing one embodiment of the present invention in a client server environment.

DETAILED DESCRIPTION OF THE INVENTION

Overview

With reference to the figures provided, embodiments of the present invention are now described in detail.

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without these specific details. In other instances, structures, devices, activities, and methods are shown using schematics, use cases, and/or flow diagrams in order to avoid obscuring the invention. Although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to suggested details are within the scope of the present invention. Similarly, although many of the features of the present invention are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the invention is set forth without any loss of generality to, and without imposing limitations upon, the invention.

The present invention provides a system and method for providing a stimulation therapy to a user wearing an ankle wearable device for assisting the user in walking and reducing the risk of fall. Some embodiments provide a closed loop system for providing stimulation therapy to the user wherein the closed loop system is configured to continuously measure gait data and providing adjusted input to ankle wearable device for providing stimulation therapy. Some embodiments of the present invention provide an open loop system for measuring gait data of the user and providing stimulation therapy to the user based on the computation of the gait data with a predefined criterion. Some embodiments of the present invention provide an ankle wearable device comprising a vibrational stimulation module and/or an electrical stimulation module for providing stimulation therapy to the user wearing the ankle wearable device. FIG. 1 illustrates a perspective view (100) of the user (102) suffering from Parkinson’s disease. The user suffering from Parkinson’s disease is having persistent tremor of the hands (104) and legs (106a, 106b), even at rest and while walking. The walking pattern of the user suffering from the disease is also affected as shown in the figure. A normal neuron and a neuron of a Parkinson’s affected user are also shown in the figure. The four main gait-related symptoms associated with Parkinson’s are: 1) freezing of gait (FoG); 2) tremors; 3) rigidity; and 4) slowness (bradykinesia). A healthy person neuron (110a) is shown in comparison to the Parkinson’s affected neuron (110b). Further, dopamine levels (112a) of the healthy person are very high in comparison to the dopamine levels (112b) of the Parkinson’s affected user. The reduced dopamine level (112b) causes movement disorder (114b) in the Parkinson’s affected user in comparison to the normal movement (114a) of the healthy person. Dopamine is a type of neurotransmitter. The body of every healthy person makes sufficient dopamine for the nervous system to use for sending messages between nerve cells. These are also called a chemical messenger.

FIG. 2 illustrates a pictorial view of a stimulation therapy system (200), in accordance with a preferred embodiment of the present invention. The stimulation therapy system (200) is configured to collect gait data and improve gait parameters (for example, the walking speed of a user and reducing the risk of fall). The stimulation therapy system (200) comprises a first ankle wearable device (202a), a second ankle wearable device (202b), a mobile device (204), a network cloud (206), and an online portal (208). The mobile device (204) is enabled to control the first ankle wearable device (202a) and the second ankle wearable device (202b) to provide vibration and/or electronic stimulation therapy.

The first ankle wearable device (202a) and the second ankle wearable device (202b) are selected from the group consisting of an ankle wearable device, a medium length sock device, long sock device, and a warmer sock device. The user (102) is wearing the first ankle wearable device (202a) on a lower limb of a first leg (106a) and the second ankle wearable device (202b) on a lower limb of a second leg (106b).

The first ankle wearable device (202a) comprises a first stimulation module, a first sensor, and a first wireless module. The second ankle wearable device (202b) comprises a second stimulation module, a second sensor, and a second wireless module.

The first stimulation module of the first ankle wearable device (202a) is configured to provide stimulation therapy to the lower limb of the first leg, and the second stimulation module of the second ankle wearable device (202b) is configured to provide stimulation therapy to the lower limb of the second leg.

The first sensor of the first ankle wearable device (202a) is configured to measure gait data associated with the first leg (106a) and the second sensor of the second ankle wearable device (202b) is configured to measure gait data associated with the second leg (106b). The first wireless module and the second wireless module are at least one of the but not limited to Bluetooth, WiFi, ZigBee, or any wireless network. Further, the first ankle wearable device (202a) and the second ankle wearable device (202b) are configured to communicate with the mobile device (204) and a network cloud (206) using the first wireless module and the second wireless module. The first ankle wearable device (202a) is configured to transmit the gait data associated with the first leg (106a) to the network cloud (206) via the mobile device (204) by using the first wireless module. The second ankle wearable device (202b) is configured to transmit the gait data associated with the second leg ( 106b) to the network cloud (206) via the mobile device (204) by using the second wireless module. The network cloud (206) comprises of a processing element configured to determine a first input for the first stimulation module in response to the computation of the gait data associated with the first leg received from the first wireless module and a second input for the second stimulation module in response to the computation of the gait data associated with the second leg received from the second wireless module. Further, the processing element is configured to transmit the first input for the first stimulation module and the second input for the second stimulation module to at least one of the first wireless module and the second wireless module. The first input and the second input are at least one of the vibration intensities, vibration frequencies, amount of time for vibration (e.g., 10 seconds, 20 seconds, or 1 minute). Further, the user is enabled to view gait data associated with the first leg and the second leg on the online portal (208). The online portal can be viewed on the mobile device (204) or an online website.

In one exemplary embodiment, the second wireless module of the second ankle wearable device (202b) is configured to transfer the gait data associated with the second leg (106b) to the first wireless module of the first ankle wearable device (202a), wherein the first wireless module of the first ankle wearable device (202a) is configured to transfer the gait data associated with the second leg to the processing element of the network cloud (206). The processing element is configured to transmit the second input for the second stimulation module to the first wireless module. Further, the first wireless module is configured to transmit the second input for the second stimulation module to the second wireless module.

In another exemplary embodiment, the first ankle wearable device (202a) is paired with the second ankle wearable device (202b) via Bluetooth, WiFi, or any wireless network.

In another exemplary embodiment, the stimulation therapy system (200) is an open loop system, and wherein the first input and the second input are generated in response to the computation of the gait data associated with the first leg (106a) and the gait data associated with the second leg (106b) with a predefined criterion.

In another exemplary embodiment, the stimulation therapy system (200) is a closed-loop system, and wherein the processing element is enabled to adjust the first input for the first stimulation module, and the second input for the second stimulation module in response to the computation of the gait data associated with the first leg (106a), measured by the first sensor after receiving stimulation therapy from the first stimulation module in accordance with the first input, and the gait data associated with the second leg (106b), measured by the second sensor after receiving stimulation therapy from the second stimulation module in accordance with the second input, and wherein the first stimulation module is enabled to provide stimulation therapy in accordance with the adjusted first input and the second stimulation module is enabled to provide stimulation therapy in accordance with the adjusted second input.

In one exemplary embodiment, the first ankle wearable device (202a) and the second ankle wearable device (202b) comprises at least one battery.

In yet another exemplary embodiment, the first stimulation module and the second stimulation module are selected from the group consisting of a vibrational stimulation module and an electrical stimulation module.

In yet another exemplary embodiment, the first sensor and the second sensor are selected from the group consisting of an accelerometer, a gyroscope sensor, a magnetometer sensor and a GPS (global positioning system) sensor.

FIG. 3 illustrates a schematic view (300) of the first ankle wearable device (202a) and the second ankle wearable device (202b), in accordance with an exemplary embodiment of the present invention. The figure shows a PCB having electronic components connected to form the first ankle wearable device (202a) and the second ankle wearable device (202b). The on-board components of the first ankle wearable device (202a) and the second ankle wearable device (202b) are an accelerometer (310), a microcontroller (302), a recharging port (306), a battery (308), a vibration motor (304), Bluetooth/WiFi chip (312), a gyroscope, a magnetometer, and an NPN transistor. The accelerometer (310), gyroscope, and magnetometer are configured to measure the user’s gait data. The microcontroller (302) is configured to receive the user’s gait data from the accelerometer (310), gyroscope, and magnetometer. The microcontroller (302) is configured to determine when to turn on the vibration motor (304) to provide vibrational therapy based on the user’s gait data. Further, the microcontroller (302) enables the first ankle wearable device (202a) and the second ankle wearable device (202b) to transmit the user’s gait data to the network cloud (206) via Bluetooth/WiFi chip (312). The processing element of the network cloud (206) receives the user’s gait data and determines the vibrational frequency and speed of the vibration impulses that need to be given to the user of the first ankle wearable device (202a) and the second ankle wearable device (202b) to assist in walking. Further, the processing element of the network cloud (206) transmits the vibrational frequency and speed of the vibration impulses to the microcontroller (302) of the first ankle wearable device (202a) and the second ankle wearable device (202b). The microcontroller (302) of the first ankle wearable device (202a) and the second ankle wearable device (202b) turn on the vibration motor of the first ankle wearable device (202a) and the second ankle wearable device (202b) for providing stimulation therapy based on the received vibrational frequency and speed of the vibration impulses.

The first ankle wearable device (202a) and the second ankle wearable device (202b) are configured to detect gait irregularities in order to apply vibration impulses to the user (e.g., patient) using the vibration motor (304) to correct gait irregularities before a fall occurs. The microcontroller (302) is powered by a battery (308). The battery (308) may be a lithium-ion battery. The battery (308) is recharged via the recharging port (306). The recharging port (306) may be a micro-USB, USB, USB- c port, mini-USB, wireless charging port, or any other port. The accelerometer (310) is coupled to the microcontroller (302). The microcontroller (302) is further coupled to the vibration motor (304) through the NPN transistor for generating vibrations of a different time interval. The microcontroller (302) is coupled to a base terminal of the NPN transistor and the vibration motor (304) is coupled to a collector terminal of the NPN transistor. The microcontroller (302) further communicates to a remote-control device (314) via Bluetooth/WiFi chip (312), wherein the remote-control device (314) comprises an “on/off’ button (316), and a “cue me” button (318). The user (e.g., wearer of the ankle device, or a doctor) is enabled to control the first ankle wearable device (202a), and the second ankle wearable device (202b) using the remote-control device (314). When the “on/off’ button is pressed (i.e., the ankle wearable device (202a, 202b) is turned on), it detects an irregularity in the movement of the user wearing the ankle wearable device (202a, 202b) (i.e., measuring gait data of the user) and provides stimulation therapy when gait irregularities are detected (for example, the user is about to fall, or the user is having difficulty in walking). The “cue me” button enables the user to have the stimulation therapy continuously while detecting the movement pattern of the user.

In yet another embodiment, the first ankle wearable device (202a) and the second ankle wearable device (202b) comprises an inertial measurement unit (IMU). The inertial measurement unit (IMU) comprises either a 6-axis or a 9-axis inertial measurement unit (IMU). The 6-axis inertial measurement unit (IMU) comprises a 3-axis accelerometer and a 3-axis gyroscope. The 9-axis inertial measurement unit (IMU) comprises the 3 -axis accelerometer, the 3 -axis gyroscope, and a 3 -axis magnetometer. In one embodiment, the inertial measurement unit (IMU) comprises one or more accelerometer, gyroscope, magnetometer, and GPS sensors.

The inertial measurement unit (IMU) is configured to measure a user’s motion, user’s position, specific force, angular rate, and orientation of a user’s body, by using a combination of 3-axis accelerometer, 3 -axis gyroscope, 3 -axis magnetometer, and GPS sensors. The accelerometer is configured to measure acceleration in one or more axis (e.g., x-, y-, or z-axis). The gyroscope is configured to measure the rate of rotation and angular position around a particular axis. The 3 -axis gyroscope is configured to measure rotation rate along three orthogonal axes. The gyroscope is the IMU component that provides an estimation of angular velocity. The magnetometer is configured to measures the magnetic field or magnetic dipole moment. Also, the magnetometer is configured to measure the direction, strength, or relative change of a magnetic field at a particular location. The GPS sensor is configured to measure user’s position in real-time.

Anklet Wearable Devices

FIG. 4 illustrates a pictorial view (400) of the first ankle wearable device (202a) and the second ankle wearable device (202b), in accordance with an exemplary embodiment of the present invention. The first ankle wearable device (202a) and the second ankle wearable device (202b) comprises the inertial measurement unit (IMU) (e.g., accelerometer (310), gyroscope, magnetometer, and GPS sensors) to measure user’s gait data. The vibration motor (304) comprises receptors for receiving the signals from the remote-control device (314) in response to pressing the “on/off’ button (316) or the “cue me” button (318). The first ankle wearable device (202a) and the second ankle wearable device (202b) having a Velcro strap (402) for securing the first ankle wearable device (202a) and the second ankle wearable device (202b) around the ankle of the user.

With reference to FIG. 5, a pictorial front side view (500) of the ankle wearable device (202a, 202b) is shown, in accordance with an exemplary embodiment of the present invention. The ankle wearable device (202a, 202b) is having the attachment mechanism (402) (e.g., Velcro strap) coupled at both sides of the ankle wearable device (202a, 202b) for securing the ankle wearable device (202a, 202b) around the left and right ankle of the user. The vibration motor (304) is coupled to the ankle wearable device in such a manner as to run along the length of the ankle wearable device (202a, 202b). The ankle wearable device can weigh up to 41bs to aid with ankle proprioception.

Stimulation Therapy System and Network

With reference to FIG. 6, a block diagram (600) of the stimulation therapy system is shown, in accordance with an exemplary embodiment of the present invention. The first ankle wearable device (202a) and the second ankle wearable device (202b) that the user is wearing in the first leg and the second leg are connected to each other using a first wireless module and a second wireless module of the first ankle wearable device (202a) and the second ankle wearable device (202b). The first ankle wearable device (202a) and the second ankle wearable device (202b) are connected to the mobile device (204) using the first wireless module and the second wireless module of the first ankle wearable device (202a) and the second ankle wearable device (202b) respectively. The mobile device (204) is further connected to the network cloud (206) using a wireless network (e.g., WiFi, Zigbee, 3G, 4G, or 5G). The network cloud (206) further enables the user to view the gait analytics data via the online portal (208). The online portal (208) can be further accessed through the web or via a mobile device.

Control Flow of Stimulation Therapy System

FIG. 7 is a flow chart (700) illustrating the working of the ankle wearable device in a standby mode, in accordance with an exemplary embodiment of the present invention. The ankle wearable device is configured to operate in two different modes. The first is a standby mode and the second is an active mode. At Step (702), in the standby mode, the gait data is collected from the first sensor of the first ankle wearable device. At Step (704), the collected gait data is then analyzed by the processing element for finding out the risk of falling for the user. At Step (706), if the user is at risk of falling, the first stimulation module at step (708) receives signals from the processing element to provide stimulation therapy to the user. At Step (710), if the analysis data does not find any risk of falling off the user, then the ankle wearable device stops providing stimulation therapy to the user.

FIG. 8 is a flow chart (800) illustrating the working of the ankle wearable device in an active mode, in accordance with an exemplary embodiment of the present invention. In the active mode, At Step (802), the gait data received from the first sensor of the first ankle wearable device and the gait data received from the second sensor of the second ankle wearable device is analyzed by the processing element. At Step (804), a first stimulation therapy and a second stimulation therapy is updated in response to the analyzed gait data. At Step (806), further, the gait data received from the first sensor of the first ankle wearable device and the gait data received from the second sensor of the second ankle wearable device is analyzed. At Step (808), the processing element determines whether the user is habituating to the therapy. If the answer is no, the processing element is enabled to again analyzing the first sensor gait data and the second sensor gait data of the user. If the answer is yes, then the first stimulation therapy and the second stimulation therapy is again updated for providing better stimulation therapy to the user, and again the first sensor gait data and the second sensor gait data of the user is analyzed by the processing element.

Illustrative System Operation

With reference to FIG. 9, a graphical and pictorial representation of the vibration power (900) provided to the left leg and the right leg of the user when the user is wearing the ankle wearable device in both the legs (202a, 202b). The figure shows the user is walking while wearing the first ankle wearable device (202a) and the second ankle wearable device (202b) on the first leg and the second leg. The graph represents the amount of vibration power given to both the legs of the user in terms of the first stimulation therapy and the second stimulation therapy. The graph also shows an in-depth analysis of the first stimulation therapy and the second stimulation therapy, when the left leg of the user is given full vibration power (904) and the right leg is given half vibration power (902). This graph is showing pulses rather than a constant signal level for representing half and full vibration power, which is the actual representation of the high or low signals.

Illustrative Stimulation Therapy System and Portal

FIG. 10 illustrates a pictorial view (1000) of the first ankle wearable device (202a), the second ankle wearable device (202b), the remote-control device (314), and the online portal (208), in accordance with an exemplary embodiment of the present invention. The online portal (208) shows graphical representations of the user’s health condition as well as the physician’s prescription.

FIG. 11 illustrates a pictorial view (1100) of the online portal (208), in accordance with an exemplary embodiment of the present invention. The online portal (208) can be viewed on the mobile device or any website. The online portal (208) enables the user or a physician to view the walking speed, steps, and freezing episodes of the user wearing the ankle wearable devices. The online portal (208) shows the user’s progress, physician notes, medicine entry, mood tracker, community, user’s progress, fall reduction, mood, symmetry, and resources. Further, the online portal (208) also shows the user’s mood improvement and medication adherence. The online portal (208) can be accessed by the physician for the proper observation and care of the user.

Illustrative Stimulation Therapy Method

FIG. 12 is a flow chart (1200) illustrating a method for providing stimulation therapy, in accordance with an exemplary embodiment of the present invention. The method is configured for: At Step (1202), measuring gait data associated with a first leg by a first sensor of a first ankle wearable device. The first ankle wearable device is enabled to be worn on a lower limb of the first leg. At Step (1204), measuring gait data associated with a second leg by a second sensor of a second ankle wearable device. The second ankle wearable device is enabled to be worn on the lower limb of the second leg. At Step (1206), transmitting the gait data associated with the first leg by a first wireless module of the first ankle wearable device to a processing element. At Step (1208), transmitting the gait data associated with the second leg by a second wireless module of the second ankle wearable device to the processing element. At Step (1210), determining a first input, by a processing element, for a first stimulation module of the first ankle wearable device and a second input for a second stimulation module of the second ankle wearable device in response to computation of the gait data associated with the first leg, received from the first wireless module, and the gait data associated with the second leg, received from the second wireless module. At Step (1212), providing the stimulation therapy to the first leg by a first stimulation module of the first ankle wearable device in accordance with the received first input from the processing element by the first wireless module to the first leg and the second leg by a second stimulation module of the second ankle wearable device in accordance with the received second input from the processing element by the second wireless module.

Alternative Embodiments

In yet another exemplary embodiment, the first sensor measures the gait data associated with the first leg and the second sensor measures the gait data associated with the second leg. The measurement of the first sensor and the second sensor is transmitted simultaneously by the first wireless module and the second wireless module respectively to the processing element of the network cloud.

In yet another exemplary embodiment, the stimulation therapy system comprising of a first ankle wearable device, enabled to be worn on a lower limb of a first leg for providing stimulation therapy is provided. The ankle wearable device comprising a first stimulation module enabled to provide stimulation therapy to the lower limb of the first leg, a first sensor enabled to measure gait data associated with the first leg and a first wireless module enabled to transmit the gait data associated with the first leg to a processing element located at a remote location. The at least one first stimulation module is enabled to provide stimulation therapy in response to a first input, received by the first wireless module from the processing element, wherein the first input is generated in response to computation by the processing element in accordance with the received gait data associated with the first leg.

In yet another exemplary embodiment, the first stimulation module is selected from the group consisting of a vibrational stimulation module and an electrical stimulation module and the first sensor is selected from the group consisting of an accelerometer, a gyro sensor, a magnetometer sensor, and a GPS (global positioning system) sensor.

In yet another exemplary embodiment, the first input is generated in response to the computation of the gait data associated with the first leg with a predefined criterion. The processing element is enabled to adjust the first input for the first stimulation module in response to computation of the gait data associated with the first leg, measured by the first sensor after receiving stimulation therapy from the first stimulation module in accordance with the first input, and wherein the first stimulation module is enabled to provide stimulation therapy in accordance with the adjusted first input. The first ankle wearable device is selected from the group consisting of an ankle wearable device, a medium length sock device, a long sock device, and a warmer sock device.

In yet another exemplary embodiment, the mobile device comprises a Bluetooth controller and is enabled to be activated via a button or a voice command of the user. The mobile device is at least one of, but not limited to, a mobile phone, smart phone, laptop, PDA, or any other device.

The mobile device enables the user and the physician to view the online portal on the mobile device to view the user’s progress. Further, the user is enabled to view the online portal on any website.

In another exemplary embodiment of the present invention, the first sensor of the first ankle wearable device is configured to measure gait data associated with the first leg and the second sensor of the second ankle wearable device is configured to measure gait data associated with the second leg. The first wireless module and the second wireless module are at least one of the but not limited to Bluetooth, WiFi, ZigBee, or any wireless network. The first ankle wearable device and the second ankle wearable device are configured to communicate with the mobile device using the first wireless module and the second wireless module. The first ankle wearable device is configured to transmit the gait data associated with the first leg to the mobile device via the first wireless module. The second ankle wearable device is configured to transmit the gait data associated with the second leg to the mobile device via the second wireless module. The mobile device comprises of a processing element configured to determine a first input for the first stimulation module in response to the computation of the gait data associated with the first leg received from the first wireless module and a second input for the second stimulation module in response to the computation of the gait data associated with the second leg received from the second wireless module. Further, the processing element is configured to transmit the first input for the first stimulation module and the second input for the second stimulation module to at least one of the first wireless module and the second wireless module. The first input and the second input are at least one of the vibration intensities, vibration frequencies, amount of time for vibration (e.g., 10 seconds, 20 seconds, or 1 minute). Further, the user is enabled to view gait data associated with the first leg and the second leg on the online portal. The online portal can be viewed on the mobile device or an online website.

In yet another exemplary embodiment, the second wireless module of the second ankle wearable device is configured to transfer the gait data associated with the second leg to the first wireless module of the first ankle wearable device, wherein the first wireless module of the first ankle wearable device is configured to transfer the gait data associated with the second leg to the processing element of the mobile device. The processing element is configured to transmit the second input for the second stimulation module to the first wireless module. Further, the first wireless module is configured to transmit the second input for the second stimulation module to the second wireless module.

Illustrative Hardware and Software Implementation Details

FIG. 13. illustrates a schematic view (1300) of a server (such as, a networked cloud server), in accordance with an exemplary embodiment of the present invention. In general, the terms server, computer, entity, device, system, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktop computers, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, gaming consoles, watches, glasses, iBeacons, proximity beacons, key fobs, radio frequency identification (RFID) tags, earpieces, scanners, televisions, dongles, cameras, wristbands, wearable items/devices, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. Such functions, operations, and/or processes may include, for example, transmitting, receiving, operating on, processing, displaying, storing, determining, creating/generating, monitoring, evaluating, and/or comparing (similar terms used herein interchangeably). In one embodiment, these functions, operations, and/or processes can be performed on data, content, and/or information (similar terms used herein interchangeably) .

As indicated, in one embodiment, the server (206) may also include one or more communications interfaces (1308) for communicating with various computing entities, such as by communicating data, content, and/or information (similar terms used herein interchangeably) that can be transmitted, received, operated on, processed, displayed, stored, and/or the like.

As shown in FIG. 13, in one embodiment, the server (206) may include or be in communication with one or more processing elements (1302) (also referred to as processors and/or processing circuitry - similar terms used herein interchangeably) that communicate with other elements within the server (206) via a bus, for example. As will be understood, the processing element (1302) may be embodied in a number of different ways. For example, the processing element (1302) may be embodied as one or more complex programmable logic devices (CPLDs), microprocessors, multi-core processors, coprocessing entities, application-specific instruction-set processors (ASIPs), microcontrollers, and/or controllers. Further, the processing element (1302) may be embodied as one or more other processing devices or circuitry. The term circuitry may refer to an entire hardware embodiment or a combination of hardware and computer program products. Thus, the processing element (1302) may be embodied as integrated circuits, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), hardware accelerators, other circuitry, and/or the like. As will therefore be understood, the processing element (1302) may be configured for a particular use or configured to execute instructions stored in volatile or non-volatile media or otherwise accessible to the processing element (1302). As such, whether configured by hardware or computer program products, or by a combination thereof, the processing element (1302) may be capable of performing steps or operations according to embodiments of the present disclosure when configured accordingly.

In one embodiment, the server (206) may further include or be in communication with non volatile media (1304) (also referred to as non-volatile storage, memory, memory storage, and/or memory circuitry - similar terms used herein interchangeably). In one embodiment, the non-volatile storage or memory may include one or more non-volatile storage or memory media (1304), including but not limited to hard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like. As will be recognized, the non-volatile storage or memory media may store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like. The term database, database instance, and/or database management system (similar terms used herein interchangeably) may refer to a collection of records or data that is stored in a computer-readable storage medium using one or more database models, such as a hierarchical database model, network model, relational model, entity- relationship model, object model, document model, semantic model, graph model, and/or the like.

In one embodiment, the server (206) may further include or be in communication with volatile media (1306) (also referred to as volatile storage, memory, memory storage, memory and/or circuitry - similar terms used herein interchangeably). In one embodiment, the volatile storage or memory may also include one or more volatile storage or memory media (1306), including but not limited to RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. As will be recognized, the volatile storage or memory media may be used to store at least portions of the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like being executed by, for example, the processing element (1302). Thus, the databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like may be used to control certain aspects of the operation of the server (206) with the assistance of the processing element (1302) and operating system.

As indicated, in one embodiment, the server (206) may also include one or more communications interfaces (1308) for communicating with various computing entities, such as by communicating data, content, and/or information (similar terms used herein interchangeably) that can be transmitted, received, operated on, processed, displayed, stored, and/or the like. Such communication may be executed using a wired data transmission protocol, such as fiber distributed data interface (FDDI), digital subscriber line (DSL), Ethernet, asynchronous transfer mode (ATM), frame relay, data over cable service interface specification (DOCSIS), or any other wired transmission protocol. Similarly, the server (206) may be configured to communicate via wireless external communication networks using any of a variety of protocols, such as general packet radio service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access 2000 (CDMA2000), CDMA2000 IX (lxRTT), Wideband Code Division Multiple Access (WCDMA), Time Division- Synchronous Code Division Multiple Access (TD-SCDMA), Uong Term Evolution (LTE), Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Evolution-Data Optimized (EVDO), High- Speed Packet Access (HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi), Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR) protocols, near field communication (NFC) protocols, Wibree, Bluetooth protocols, wireless universal serial bus (USB) protocols, and/or any other wireless protocol.

Although not shown, the server (206) may include or be in communication with one or more input elements, such as a keyboard input, a mouse input, a touch screen/display input, motion input, movement input, audio input, pointing device input, joystick input, keypad input, and/or the like. The server (206) may also include or be in communication with one or more output elements (not shown), such as audio output, video output, screen/display output, motion output, movement output, and/or the like.

As will be appreciated, one or more of the components of the server (206) may be located remotely from other management computing entity components, such as in a distributed system. Furthermore, one or more of the components may be combined and additional components performing functions described herein may be included in the server (206). Thus, the server (206) can be adapted to accommodate a variety of needs and circumstances. As will be recognized, these architectures and descriptions are provided for exemplary purposes only and are not limiting to the various embodiments.

With reference to FIG. 14, an illustrative schematic representative (1400) of a user computing entity (such as, a mobile device 204) that can be used in conjunction with embodiments of the present disclosure. In general, the terms device, system, computing entity, entity, and/or similar words used herein interchangeably may refer to, for example, one or more computers, computing entities, desktops, mobile phones, tablets, phablets, notebooks, laptops, distributed systems, gaming consoles, watches, glasses, key fobs, radio frequency identification (RFID) tags, earpieces, scanners, cameras, wristbands, kiosks, input terminals, servers or server networks, blades, gateways, switches, processing devices, processing entities, set-top boxes, relays, routers, network access points, base stations, the like, and/or any combination of devices or entities adapted to perform the functions, operations, and/or processes described herein. User computing entities (204) can be operated by various parties. As shown in FIG. 14, the user computing entity (204) can include an antenna (1408), a transmitter (1402) (e.g., radio), a receiver (1404) (e.g., radio), and a processing element (1406) (e.g., CPLDs, microprocessors, multi core processors, coprocessing entities, ASIPs, microcontrollers, and/or controllers) that provides signals to and receives signals from the transmitter (1402) and receiver (1404), respectively.

The signals provided to and received from the transmitter (1402) and the receiver (1404), respectively, may include signaling information in accordance with air interface standards of applicable wireless systems. In this regard, the user computing entity (204) may be capable of operating with one or more air interface standards, communication protocols, modulation types, and access types. More particularly, the user computing entity (204) may operate in accordance with any of a number of wireless communication standards and protocols, such as those described above with regard to the management computing entity. In a particular embodiment, the user computing entity (204) may operate in accordance with multiple wireless communication standards and protocols, such as UMTS, CDMA2000, lxRTT, WCDMA, TD-SCDMA, UTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, Wi Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth, USB, and/or the like. Similarly, the user computing entity (204) may operate in accordance with multiple wired communication standards and protocols, such as those described above with regard to the management computing entity via a network interface (1414).

Via these communication standards and protocols, the user computing entity (204) can communicate with various other entities using concepts such as Unstructured Supplementary Service Data (USSD), Short Message Service (SMS), Multimedia Messaging Service (MMS), Dual-Tone Multi-Frequency Signaling (DTMF), and/or Subscriber Identity Module Dialer (SIM dialer). The user computing entity (204) can also download changes, add-ons, and updates, for instance, to its firmware, software (e.g., including executable instructions, applications, program modules), and operating system.

According to one embodiment, the user computing entity (204) may include location determining aspects, devices, modules, functionalities, and/or similar words used herein interchangeably. For example, the user computing entity (204) may include outdoor positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, universal time (UTC), date, and/or various other information/data. In one embodiment, the location module can acquire data, sometimes known as ephemeris data, by identifying the number of satellites in view and the relative positions of those satellites. The satellites may be a variety of different satellites, including Low Earth Orbit (LEO) satellite systems, Department of Defense (DOD) satellite systems, the European Union Galileo positioning systems, the Chinese Compass navigation systems, Indian Regional Navigational satellite systems, and/or the like. Alternatively, the location information can be determined by triangulating the user computing entity’s (204) position in connection with a variety of other systems, including cellular towers, Wi-Fi access points, and/or the like. Similarly, the user computing entity (204) may include indoor positioning aspects, such as a location module adapted to acquire, for example, latitude, longitude, altitude, geocode, course, direction, heading, speed, time, date, and/or various other information/data. Some of the indoor systems may use various position or location technologies including RFID tags, indoor beacons or transmitters, Wi-Fi access points, cellular towers, nearby computing devices (e.g., smartphones, laptops), and/or the like. For instance, such technologies may include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy (BLE) transmitters, NFC transmitters, and/or the like. These indoor positioning aspects can be used in a variety of settings to determine the location of someone or something within inches or centimeters.

The user computing entity (204) may also comprise a user interface (that can include a display (1410) coupled to a processing element (1406) and/or a user input interface (coupled to a processing element (1406). For example, the user interface may be a user application, browser, user interface, and/or similar words used herein interchangeably executing on and/or accessible via the user computing entity (204) to interact with and/or cause display of information from the management computing entity as described herein. The user input interface can comprise any of a number of devices or interfaces allowing the user computing entity (204) to receive data, such as a keypad (1412) (hard or soft), a touch display, voice/speech or motion interfaces, or other input device. In embodiments including a keypad (1412), the keypad (1412) can include (or cause display of) the conventional numeric (0-9) and related keys (#, *), and other keys used for operating the user computing entity (204) and may include a full set of alphabetic keys or set of keys that may be activated to provide a full set of alphanumeric keys. In addition to providing input, the user input interface can be used, for example, to activate or deactivate certain functions, such as screen savers and/or sleep modes.

The user computing entity (204) can also include volatile storage or memory (1416) and/or non-volatile storage or memory (1418), which can be embedded and/or may be removable. For example, the non-volatile memory may be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM, Millipede memory, racetrack memory, and/or the like. The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory, register memory, and/or the like. The volatile and non-volatile storage or memory can store databases, database instances, database management systems, data, applications, programs, program modules, scripts, source code, object code, byte code, compiled code, interpreted code, machine code, executable instructions, and/or the like to implement the functions of the user computing entity (204). As indicated, this may include a user application that is resident on the entity or accessible through a browser or other user interface for communicating with the management computing entity and/or various other computing entities.

In another embodiment, the user computing entity (204) may include one or more components or functionality that are the same or similar to those of the management computing entity, as described in greater detail above. As will be recognized, these architectures and descriptions are provided for exemplary purposes only and are not limiting to the various embodiments.

With reference to FIG. 15, shows an illustrative system architecture (1500) for implementing one embodiment of the present invention in a client server environment. User devices (e.g., image capturing device) (1502) on the client side may include smart phones (1504), laptops (1506), desktop PCs (1508), tablets (1510), or other devices. Such user devices (1502) access the service of the system server (1514) through some network connection (1512), such as the Internet.

In some embodiments of the present invention, the entire system can be implemented and offered to the end-users and operators over the Internet, in a so-called cloud implementation. No local installation of software or hardware would be needed, and the end-users and operators would be allowed access to the systems of the present invention directly over the Internet, using either a web browser or similar software on a client, which client could be a desktop, laptop, mobile device, and so on. This eliminates any need for custom software installation on the client side and increases the flexibility of delivery of the service (software-as-a-service) and increases user satisfaction and ease of use. Various business models, revenue models, and delivery mechanisms for the present invention are envisioned, and are all to be considered within the scope of the present invention.

Illustrative Gait Analysis Algorithms and Methods

In one exemplary embodiment of the present invention, the method for detecting freezing of gait in real-time for Parkinson’s is provided. The method comprises four steps. Step one is to label data. The first sensor and the second sensor are configured for measuring gait data associated with the first leg and the second leg of the user. The method is configured for converting a time series representation of the measured gait data into a frequency domain using a Fast Fourier Transform. When the user is suffering from the freezing of the gait, the frequency domain representation of this time series gait data is of a very high-frequency domain. The method of converting the received sensor data (e.g., measured gait data) which is in the time domain into the frequency domain using the Fast Fourier Transform is used to label the gait data as freeze or no freeze. Step two is the training and validation. The method is configured for performing a deep learning algorithm on the labeled gait data to determine an output label. Further, the method is configured for validation e.g., whether the output label is matching the input label and retrain deep learning algorithm. The deep learning algorithm is an example of an Artificial Intelligence (AI) program. Examples of the deep learning algorithm is a recurrent neural network (RNN), a convolutional neural network (CNN), or another neural network architecture. Step three involves the use of sensor data in real-time. The first and second ankle wearable device collects the sensor data and sends the sensor data to the network cloud. The network cloud is configured for processing of the sensor data to determine vibration intensities, vibration frequencies, vibration length (i.e., time period) that need to be given to the user to reduce the risk of fall. The network cloud sends the vibration intensities and vibration frequencies data to the first and second ankle wearable device to adjust a vibration motor accordingly. In the network cloud, the deep learning algorithm is applied in step two to decide the freeze or no freeze of the gait. Further, depending on the outcome of the deep learning algorithm, the vibrational input changes to reduce the length and severity of the freezing episode and reduce the risk of falls. Step four involves using newly collected real-time sensor data and repeating step one and step two to maximize the efficiency, accuracy, sensitivity, precision and negative prediction value of the method.

In one exemplary embodiment, the calculation of gait metrics involves one among the following: 1. Step time asymmetry, step length asymmetry, cadence, festination, bradykinesia, freezing, left vs. right strength, the fluidness of movement, step length, push-off force, swing time, double limb support, gait score, etc.

In another exemplary embodiment, the method for calculating gait metrics is provided using a deep learning algorithm, wherein the deep learning algorithm is at least one of a recurrent neural network, a convolutional neural network, or another neural network architecture. The method is configured for combining a first and second sensor signals with additional clinical data (manually inputted) and applying a deep learning algorithm on the first and second sensor signals and the additional clinical data for obtaining at least one gait metric.

In another exemplary embodiment, the method for calculating stimulation metrics is provided. The stimulation metrics are at least of the but not limited to a vibrational frequency, vibration intensity for the first leg and the second leg. The method is configured for applying a deep learning algorithm on a first and second sensor signals for outputting a first stimulation metrics. The deep learning algorithm is at least one of, but not limited to, a recurrent neural network, a convolutional neural network or another neural network architecture. Further, the method is configured for collecting again the first and second sensor signals and applying the deep learning algorithm for outputting a second stimulation metrics. Further, the method is configured for determining whether the second stimulation metrics are better or improved than the first stimulation metrics. The method is configured to repeat the above steps for training the deep learning algorithm for calculation correct or improved stimulation metrics.

In another exemplary embodiment, the method for signal processing to calculate stimulation metrics is provided. The method is configured for receiving at least one gait metrics. The gait metric is at least one of the but not limited to a step time asymmetry, step length asymmetry, cadence, festination, bradykinesia, freezing, left vs. right strength, the fluidness of movement, step length, push-off force, swing time, double limb support, gait score. The method is further configured for applying a deep learning algorithm for outputting vibration input (e.g., vibrational frequency and intensity for the first leg and the second leg). The deep learning algorithm is at least one of a recurrent neural network or a convolutional neural network.

In another exemplary embodiment, the method for signal processing to calculate gait metrics is provided. Many direct signal processing techniques can be used to calculate the gait metrics, for example, fdters, pattern recognition, adding threshold, taking derivatives, etc. In all the above methods, it can consist of supervised or unsupervised machine learning. For supervised learning, the data can be labeled by a clinician or other signal processing methods.

In another exemplary embodiment, a method for generating gait data is provided, wherein the method comprising training two networks (a) generator and (b) discriminator. The method involves four steps. In step one, the method is configured for training discriminator to recognize real sensor data collected by the first ankle wearable device and the second ankle wearable device and a fake sensor data (generated in step two). The method is further configured for outputting real sensor data or fake sensor data.

In step two, the method is configured for training generator network to generate the fake sensor data by using random input vector and output fake sensor data. The fake sensor data is used to train discriminator to recognize the difference between real sensor data and fake sensor data. The method is configured for updating the generator network, without taking into consideration that the discriminator is able to recognize the difference between real sensor data and fake sensor data.

Step three involves a continuous repetition of steps one and two. In this step, the generator will get better at making fake sensor data that looks real and the discriminator will get better and enabled for detecting real sensor data from fake sensor data.

In another exemplary embodiment, a method for characterizing gait metrics through a random vector generator is provided. Each random input vector generated in the above method is tied to a fake data set. The values in the random input vector define the features of the gait sensor data. The method is configured for combining certain vectors and put them through the generator network for outputting a gait data set with a combination of the feature user want to include from the previous data set.

Conclusions

One of ordinary skill in the art knows that the use cases, structures, schematics, and flow diagrams may be performed in other orders or combinations, but the inventive concept of the present invention remains without departing from the broader scope of the invention. Every embodiment may be unique, and methods/steps may be either shortened or lengthened, overlapped with the other activities, postponed, delayed, and continued after a time gap to practice the methods of the present invention.

Many modifications and other embodiments of the disclosure set forth herein will come to mind to one skilled in the art to which these embodiments pertain having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is to be understood that the embodiments are not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation. Although the present invention has been described with reference to specific exemplary embodiments, it will be evident that the various modification and changes can be made to these embodiments without departing from the broader scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than in a restrictive sense. It will also be apparent to the skilled artisan that the embodiments described above are specific examples of a single broader invention which may have greater scope than any of the singular descriptions taught. There may be many alterations made in the descriptions without departing from the scope of the present invention.

Claims

CLAIMS What is claimed is:

1. A stimulation therapy system, comprising: a first ankle wearable device, enabled to be worn on a lower limb of a first leg, comprising: a first stimulation module, enabled to provide stimulation therapy to the lower limb of the first leg; a first sensor enabled to measure gait data associated with the first leg; and a first wireless module; and a second ankle wearable device, enabled to be worn on a lower limb of a second leg, comprising: a second stimulation module, enabled to provide stimulation therapy to the lower limb of the second leg; a second sensor enabled to measure gait data associated with the second leg; and a second wireless module; and a processing element, enabled to determine a first input for the first stimulation module and a second input for the second stimulation module in response to a computation of the gait data associated with the first leg, received from the first wireless module, and the gait data associated with the second leg, received from the second wireless module, wherein the processing element is enabled to transmit the first input for the first stimulation module and the second input for the second stimulation module to at least one of the first wireless module and the second wireless module.

2. The stimulation therapy system of claim 1, wherein the second wireless module is enabled to transfer the gait data, associated with the second leg, to the first wireless module.

3. The stimulation therapy system of claim 2, wherein the processing element receives the gait data associated with the second leg from the second wireless module via the first wireless module.

4. The stimulation therapy system of claim 2, wherein the processing element is enabled to transmit the second input for the second stimulation module to the first wireless module.

5. The stimulation therapy system of claim 4, wherein the first wireless module is enabled to transmit the second input for the second stimulation module to the second wireless module.

6. The stimulation therapy system of claim 1, wherein the first ankle wearable device and the second ankle wearable device comprises at least one battery.

7. The stimulation therapy system of claim 1, wherein the first stimulation module and the second stimulation module are selected from the group consisting of a vibrational stimulation module and an electrical stimulation module.

8. The stimulation therapy system of claim 1, wherein the first sensor and the second sensor are selected from the group consisting of an accelerometer sensor, a gyroscope sensor, a magnetometer sensor, and a GPS (global positioning system) sensor.

9. The stimulation therapy system of claim 1, wherein the stimulation therapy system is an open loop system, and wherein the first input and the second input are generated in response to the computation of the gait data associated with the first leg and the gait data associated with the second leg with a predefined criterion.

10. The stimulation therapy system of claim 1, wherein the stimulation therapy system is a closed-loop system, wherein the processing element is enabled to adjust the first input for the first stimulation module, and the second input for the second stimulation module in response to computation of the gait data associated with the first leg, measured by the first sensor after receiving stimulation therapy from the first stimulation module in accordance with the first input, and the gait data associated with the second leg, measured by the second sensor after receiving stimulation therapy from the second stimulation module in accordance with the second input, and wherein the first stimulation module is enabled to provide stimulation therapy in accordance with the adjusted first input and the second stimulation module is enabled to provide stimulation therapy in accordance with the adjusted second input.

11. The stimulation therapy system of claim 1, wherein the first ankle wearable device and the second ankle wearable device are selected from the group consisting of an ankle wearable device, a medium length sock device, a long sock device and a warmer sock device.

12. A method for providing stimulation therapy, comprising: measuring gait data associated with a first leg by a first sensor of a first ankle wearable device, enabled to be worn on a lower limb of the first leg; transmitting the gait data associated with the first leg by a first wireless module of the first ankle wearable device to a processing element; measuring gait data associated with a second leg by a second sensor of a second ankle wearable device, enabled to be worn on a lower limb of the second leg; transmitting the gait data associated with the second leg by a second wireless module of the second ankle wearable device to the processing element; determining a first input, by a processing element, for a first stimulation module of the first ankle wearable device and a second input for a second stimulation module of the second ankle wearable device in response to computation of the gait data associated with the first leg, received from the first wireless module, and the gait data associated with the second leg, received from the second wireless module; and providing stimulation therapy to the first leg by the first stimulation module of the first ankle wearable device in accordance with the received first input from the processing element by the first wireless module and to the second leg by the second stimulation module of the second ankle wearable device in accordance with the received second input from the processing element by the second wireless module.

13. The method of claim 12, further comprising: transferring the gait data associated with the second leg by the second wireless module to the first wireless module.

14. The method of claim 13, further comprising: receiving the gait data, associated with the second leg, from the second wireless module via the first wireless module, by the processing element.

15. The method of claim 13, further comprising: transmitting the second input for the second stimulation module to the first wireless module by the processing element.

16. The method of claim 15, further comprising: transmitting the second input for the second stimulation module to the second wireless module by the first wireless module.

17. The method of claim 12, wherein the first ankle wearable device and the second ankle wearable device comprises at least one battery.

18. The method of claim 12, wherein the first stimulation module and the second stimulation module are selected from the group consisting of a vibrational stimulation module and an electrical stimulation module.

19. The method of claim 12, wherein the first sensor and the second sensor are selected from the group consisting of an accelerometer sensor, a gyroscope sensor, a magnetometer sensor, and a GPS (global positioning system) sensor.

20. The method of claim 12, further comprising: generating the first input and the second input in response to the computation of the gait data associated with the first leg and the gait data associated with the second leg with a predefined criterion.

21. The method of claim 12, further comprising: adjusting the first input, for the first stimulation module, and the second input, for the second stimulation module, by the processing element, in response to computation of the gait data associated with the first leg, measured by the first sensor after receiving stimulation therapy from the first stimulation module in accordance with the first input and the gait data associated with the second leg, measured by the second sensor after receiving stimulation therapy from the second stimulation module in accordance with the second input; and providing stimulation therapy to the first leg by the first stimulation module in accordance with the adjusted first input and to the second leg by the second stimulation module in accordance with the adjusted second input.

22. The method of claim 12, wherein the first ankle wearable device and the second ankle wearable device are selected from the group consisting of an ankle wearable device, a medium length sock device, a long sock device, and a warmer sock device.

23. An ankle wearable device, enabled to be worn on a lower limb of a leg for providing stimulation therapy, the ankle wearable device comprising: a first stimulation module, enabled to provide stimulation therapy to the lower limb of the leg; a first sensor enabled to measure gait data associated with the leg; and a first wireless module, enabled to transmit the gait data associated with the leg to a processing element, located at a remote location, wherein the first stimulation module is enabled to provide stimulation therapy in response to a first input, received by the first wireless module from the processing element, wherein the first input is generated in response to computation by the processing element in accordance with the received gait data associated with the leg.

24. The ankle wearable device of claim 23, wherein the ankle wearable device comprises a battery.

25. The ankle wearable device of claim 23, wherein the first stimulation module is selected from the group consisting of a vibrational stimulation module and an electrical stimulation module.

26. The ankle wearable device of claim 23, wherein the first sensor is selected from the group consisting of an accelerometer, a gyroscope sensor, a magnetometer sensor and a GPS (global positioning system) sensor.

27. The ankle wearable device of claim 23, wherein the first input is generated in response to the computation of the gait data associated with the leg with a predefined criterion.

28. The ankle wearable device of claim 23, wherein the processing element is enabled to adjust the first input for the first stimulation module in response to computation of the gait data associated with the leg, measured by the first sensor after receiving stimulation therapy from the first stimulation module in accordance with the first input, and wherein the first stimulation module is enabled to provide stimulation therapy in accordance with the adjusted first input.

29. The ankle wearable device of claim 23, wherein the ankle wearable device is selected from the group consisting of an ankle wearable device, a medium length sock device, a long sock device, and a warmer sock device.