WO2020243781A1 - Biospine: a digtial twin neurorehabilitation system - Google Patents

Biospine: a digtial twin neurorehabilitation system Download PDF

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Publication number
WO2020243781A1
WO2020243781A1 PCT/AU2020/050566 AU2020050566W WO2020243781A1 WO 2020243781 A1 WO2020243781 A1 WO 2020243781A1 AU 2020050566 W AU2020050566 W AU 2020050566W WO 2020243781 A1 WO2020243781 A1 WO 2020243781A1
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WO
WIPO (PCT)
Prior art keywords
person
sensors
model
rehabilitation
rehabilitation system
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
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PCT/AU2020/050566
Other languages
French (fr)
Inventor
David Lloyd
Claudio PIZZOLATO
Dinesh Palipana
David John Saxby
Laura Elizabeth Diamond
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Griffith University
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Griffith University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Priority claimed from AU2019901921A external-priority patent/AU2019901921A0/en
Application filed by Griffith University filed Critical Griffith University
Priority to SG11202113224XA priority Critical patent/SG11202113224XA/en
Priority to EP20817609.9A priority patent/EP3980112A4/en
Priority to AU2020287071A priority patent/AU2020287071B2/en
Priority to US17/614,208 priority patent/US12330022B2/en
Priority to JP2021572105A priority patent/JP2022535563A/en
Priority to CN202080041275.3A priority patent/CN113905781A/en
Publication of WO2020243781A1 publication Critical patent/WO2020243781A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • A63B2071/0658—Position or arrangement of display
    • A63B2071/0661—Position or arrangement of display arranged on the user
    • A63B2071/0666—Position or arrangement of display arranged on the user worn on the head or face, e.g. combined with goggles or glasses
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    • A63B2213/00—Exercising combined with therapy
    • A63B2213/004—Exercising combined with therapy with electrotherapy
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    • A63B2220/30—Speed
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    • A63B2220/80—Special sensors, transducers or devices therefor
    • A63B2220/83—Special sensors, transducers or devices therefor characterised by the position of the sensor
    • A63B2220/836—Sensors arranged on the body of the user
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    • A63B2230/00—Measuring physiological parameters of the user
    • A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
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    • A63B2230/00—Measuring physiological parameters of the user
    • A63B2230/04—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations
    • A63B2230/06—Measuring physiological parameters of the user heartbeat characteristics, e.g. ECG, blood pressure modulations heartbeat rate only
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    • A63B2230/08—Measuring physiological parameters of the user other bio-electrical signals
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    • A63B2230/40—Measuring physiological parameters of the user respiratory characteristics

Definitions

  • BIOSPINE A DIGTIAL TWIN NEUROREHABILITATION SYSTEM
  • the present invention generally relates to a neurorehabilitation system for a person with an acquired or developmental neurological condition such as spinal cord injury, brain injury, cerebral palsy, or spasticity.
  • an acquired or developmental neurological condition such as spinal cord injury, brain injury, cerebral palsy, or spasticity.
  • the preferred embodiment of the invention will be presented for application to spinal cord injury (SCI).
  • exoskeletons can help regain voluntary movement in SCI. Since the motors, and not the stimulated muscles’ activation, generate the SCI patient’s movement this form of rehabilitation can protect against over loading. Flowever, motor-driven rehabilitation does not facilitate use of stimulated muscle activation and action to generate
  • the preferred embodiment provides an improved rehabilitation system for rehabilitating a person with a SCI.
  • the preferred system integrates all aforementioned aspects of rehabilitation to help safely regain voluntary movement in SCI.
  • the preferred system through careful design and use of computer modelling will facilitate the systems ease of use in a clinical setting.
  • a rehabilitation system for rehabilitating a person with a neurological condition including:
  • a human machine interface with one or more sensors for sensing information from the person’s head, neck and body during exercise;
  • a human machine interface that has somatosensory feedback enabled by visual feedback, preferably via virtual/augmented reality, tactile via haptic feedback, or auditory by ear phones;
  • a rehabilitation system for rehabilitating a person with a neurological condition including:
  • one or more sensors for sensing information from the person during exercise; and a model of the exercising person configured to receive the sensed information from the sensors and generate (functional or spinal cord) electrical stimulation for the person.
  • the personalized computer model may be used to generate suitable functional or spinal cord electrical stimulation for the person, and avoid excessive stresses on the person which can lead to the fracturing of bones.
  • the personalized computer model may include a neuromusculoskeletal model.
  • the sensors may include biomechanical and/or physiological biosensors on the person’s head, neck and/or body.
  • the sensed information may relate to any one or more of electromyography (EMG), inertial measurement units, electroencephalography (EEG), electrooculography (EOG), eye gaze, heart rate, electrocardiography (ECG), and respiration.
  • EMG electromyography
  • EEG electroencephalography
  • EOG electrooculography
  • EOG eye gaze
  • heart rate electrocardiography
  • respiration respiration
  • the sensors may include wearable sensors worn by the person.
  • the exercise equipment may include a recumbent ergometer, upper-arm ergometer or articulated system, rowing system, or walking system.
  • the system may further include an actuator(s) to assist the person to perform the exercise on the equipment.
  • the actuator may include a motor.
  • the sensors may further include one or more equipment sensors for sensing information of the equipment that is also provided to the model for generating the functional or spinal cord electrical stimulation, and/or actuation assistance .
  • the equipment sensors may include speed, motor current, force and/or torque sensors.
  • the model may be configured to be a human machine interface (HMI) between the person and equipment.
  • HMI my include a headset, in turn, including an EEG system that will provide data to the brain computer interface (BCI) model to classify the person’s exercise intention and intensity.
  • BCI brain computer interface
  • the intent and intensity may trigger the model to generate functional or spinal cord electrical stimulation and actuation assistance to enable the person to perform the desired exercise on the equipment.
  • the model may be further configured to generate sensory data to be feedback to the person via the HMI.
  • the sensory data may include somatosensory, cardiac and/or respiratory data.
  • Sensory data may be provided to person by visual, auditory and/or intact haptic feedback pathways.
  • the HMI headset may provide these extended reality feedback by including virtual or augmented reality, or via ear phones and/or haptic devices.
  • a rehabilitation method for rehabilitating a person with a neurological condition including:
  • sensing information from the exercising person and exercise equipment and receiving the sensed information and generating functional electrical stimulation and actuator assistance for the person using a model of the exercising person using the exercise equipment.
  • Figure 1 is a schematic view of a rehabilitation system in accordance with an embodiment of the present invention.
  • a rehabilitation system 100 for rehabilitating a person 102 with a SCI.
  • the system 100 includes exercise equipment 104, maybe in the form of a recumbent cycle or ergometer, which enables the person 102 to exercise.
  • Exercise equipment 104 maybe in the form of a recumbent cycle or ergometer, which enables the person 102 to exercise.
  • Personal wearable head, neck and body biosensors 106 and 107 are provided for sensing personal information 108 and 109 from the person 102 during exercise.
  • the system 100 further includes a personalized computer model 1 10 of the exercising person 102 on the exercise equipment 104 for receiving the sensed personal information 108 and 109 from the sensors 106 and 107 and generating electrical stimulation and actuation assistance 1 12 for the person.
  • a personalized computer model 1 10 of the exercising person 102 on the exercise equipment 104 for receiving the sensed personal information 108 and 109 from the sensors 106 and 107 and generating electrical stimulation and actuation assistance 1 12 for the person.
  • personalized model 1 10 is used in generating suitable adaptive electrical stimulation for the person 102, and avoids excessive musculoskeletal, cardiac and pulmonary stresses on the person 102 as well as the potential fracturing of bones.
  • the rehabilitation system 100 further includes equipment sensors (e.g.
  • the equipment information 1 14 is also provided to the model 1 10 and used in generating the electrical stimulation and actuation assistance 1 12.
  • the model 1 10 is configured to generate sensory data 1 16 to be provided to the person 102 through extended reality feedback 1 18 in the HMI 1 19.
  • the extended reality 1 18 in HMI 1 19 includes a virtual or augmented reality headset 122 with visual 123, auditory 120 and tactile feedback 121 .
  • the HMI 1 19 will employ a brain computer interface (BCI) 124 that may use EEG, EOG, eye gaze sensor data 109 in the headset 122 worn by the person 102.
  • BCI brain computer interface
  • the model 1 10 is also configured to receive data from the BCI 124 that is the interpretation of the person’s required movement when the person 102 thinks about an action.
  • the model 1 10 is typically stored in the cloud 126 and loT enabled.
  • the neuromusculoskeletal model 1 10 incorporates a Digital Twin, which is a computer representation of the person's bones, muscles, joints, and nervous system.
  • the Digital Twin technology is used in real-time to virtually bypass the site of SCI, again connecting sensory and motor pathways between brain, spinal cord, and muscles.
  • the model 1 10 includes BioSpine, which is an innovative application of Digital Twin technology through the HMI headset 122 that is combined with
  • BioSpine integrates a unique set of intelligent rehabilitation assistive technologies controlled by the Digital Twin to restore the interrupted motor and sensory connections in the spine. BioSpine integrates the following discrete technologies into the seamless system 100: HMI 1 19, wearable biosensors 106 and 107, electrical stimulation 1 12 of lower limb muscles of the person 102, motor-assisted leg cycling in the case presented, augmented somatosensory signals 1 16 transformed extended reality 1 18 with visual 123, auditory 120 and haptic 121 biofeedback.
  • the system 100 is intuitively and automatically controlled by the personalised Digital Twin of the patient 102.
  • Personalised Digital Twins of each participant 102 can be developed combining magnetic resonance imaging (MRI) [2] and artificial intelligence methods.
  • Electroencephalograms (EEG) can be captured via a portable wireless headset (e.g. Wearable Sensing DSI7 or DSI-VR300, Switzerland) 122 and processed in the BCI 124 using Al methods to discriminate whether the patient wishes to perform, and how intensely they wish to do, in this example case, the cycling exercise.
  • the patient’s motor intention to cycle data 124 will control the Digital Twin, which in turn will optimally stimulate muscles via electrical stimulation 1 12 and provide appropriate motorised assistance 1 12 to achieve cycling.
  • the Digital Twin coordinates the electrical stimulation 1 12 and motorised assistance 1 12 to ensure that stimulated muscle activation assists, rather than opposes, the motorised actuation to perform the movement.
  • Biomechanical and physiological information 108 from multiple wearable biosensors 106 can be interpreted by the patient’s Digital Twin to progressively adapt the amount of ergometer pedal- assistance in order to maximally engage the patient 102, while also maintaining musculoskeletal tissue loads and cardiovascular demand within safe levels [3, 4]
  • the patient’s Digital Twin can synthesise somatosensory information 1 16 that will be redirected to higher somatosensory areas via extended reality 1 18, visual 123, auditory 120 and/or haptic 121 feedback [4, 5].
  • the system 100 may further include an actuator, such as a robotic motor coupled to the drive crank, for actuating the exercise equipment 104.
  • an actuator such as a robotic motor coupled to the drive crank, for actuating the exercise equipment 104.
  • the model 1 10 actuates the actuator to some extent to assist the person 102.

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Abstract

The present invention relates to a rehabilitation system for rehabilitating a person with a neurological condition, such as a spinal cord injury (SCI). The system includes exercise equipment for enabling the person to exercise. One or more sensors are provided for sensing information from the person during exercise. The system also includes a model of the exercising person configured to receive the sensed information from the sensors and generate electrical stimulation for the person. Advantageously, the personalized computer model may be used to generate suitable electrical stimulation for the person, and avoid excessive stresses on the person which can lead to the fracturing of bones.

Description

BIOSPINE: A DIGTIAL TWIN NEUROREHABILITATION SYSTEM
TECHNICAL FIELD
[0001] The present invention generally relates to a neurorehabilitation system for a person with an acquired or developmental neurological condition such as spinal cord injury, brain injury, cerebral palsy, or spasticity. The preferred embodiment of the invention will be presented for application to spinal cord injury (SCI).
BACKGROUND
[0002] The reference to any prior art in this specification is not, and should not be taken as an acknowledgement or any form of suggestion that the prior art forms part of the common general knowledge.
[0003] For thousands of years SCI was thought to be irreversible. This view is now changing. Recent scientific evidence has shown that thought-controlled rehabilitation robots can restore some voluntary movement in SCI. Further evidence suggests that providing motor sensory feedback can restore damaged spinal pathways.
[0004] Other studies have also shown that electrically stimulating spinal cord or muscles can help. Flowever, such stimulation has been known to activate muscles to fracture bones that have been weakened through non-use over time.
[0005] Further studies have shown that rehabilitation using motor-driven
exoskeletons can help regain voluntary movement in SCI. Since the motors, and not the stimulated muscles’ activation, generate the SCI patient’s movement this form of rehabilitation can protect against over loading. Flowever, motor-driven rehabilitation does not facilitate use of stimulated muscle activation and action to generate
movement, as enabled by electrical stimulation.
[0006] Numerous studies have also explored the use of biofeedback in
rehabilitation, using visual, haptic, or auditory monitors to provide information to the user and improve their training. Commonly this involves directly providing visual information regarding joint angles or body position. However, our brain does not directly receive information regarding the position of our joints during movement, instead it
subconsciously interprets the electrical signals from mechanical sensors in our muscles and tendons (i.e. muscle spindles and golgi tendon organs, respectively), which generate electrical signals that are associated with length, velocity and tension of the muscles in which the sensors are embedded.
[0007] The use of all mentioned methods for SCI rehabilitation can be complex and need considerable training. Health professionals are therefore reticent to use these approaches, relying on more time-consuming and less effective hands-on rehabilitation methods.
[0008] The preferred embodiment provides an improved rehabilitation system for rehabilitating a person with a SCI. The preferred system integrates all aforementioned aspects of rehabilitation to help safely regain voluntary movement in SCI. Furthermore, the preferred system, through careful design and use of computer modelling will facilitate the systems ease of use in a clinical setting.
SUMMARY OF THE INVENTION
[0009] According to one aspect of the present invention, there is provided a rehabilitation system for rehabilitating a person with a neurological condition, the system including:
exercise equipment for enabling the person to exercise;
a human machine interface with one or more sensors for sensing information from the person’s head, neck and body during exercise;
a human machine interface that has somatosensory feedback enabled by visual feedback, preferably via virtual/augmented reality, tactile via haptic feedback, or auditory by ear phones;
a personalized computer model of the exercising person on the exercise
equipment configured to receive the information flow from the sensors and generate functional or spinal cord electrical stimulation and motorised assistance, for the person; and
a personalized computer model that is able to synthesize and generate
somatosensory feedback. [00010] According to another aspect of the present invention, there is provided a rehabilitation system for rehabilitating a person with a neurological condition, the system including:
exercise equipment for enabling the person to exercise;
one or more sensors for sensing information from the person during exercise; and a model of the exercising person configured to receive the sensed information from the sensors and generate (functional or spinal cord) electrical stimulation for the person.
[00011] Advantageously, the personalized computer model may be used to generate suitable functional or spinal cord electrical stimulation for the person, and avoid excessive stresses on the person which can lead to the fracturing of bones.
[00012] The personalized computer model may include a neuromusculoskeletal model.
[00013] The sensors may include biomechanical and/or physiological biosensors on the person’s head, neck and/or body. The sensed information may relate to any one or more of electromyography (EMG), inertial measurement units, electroencephalography (EEG), electrooculography (EOG), eye gaze, heart rate, electrocardiography (ECG), and respiration.
[00014] The sensors may include wearable sensors worn by the person.
[00015] The exercise equipment may include a recumbent ergometer, upper-arm ergometer or articulated system, rowing system, or walking system. The system may further include an actuator(s) to assist the person to perform the exercise on the equipment. The actuator may include a motor.
[00016] The sensors may further include one or more equipment sensors for sensing information of the equipment that is also provided to the model for generating the functional or spinal cord electrical stimulation, and/or actuation assistance . The equipment sensors may include speed, motor current, force and/or torque sensors. [00017] The model may be configured to be a human machine interface (HMI) between the person and equipment. The HMI my include a headset, in turn, including an EEG system that will provide data to the brain computer interface (BCI) model to classify the person’s exercise intention and intensity. The intent and intensity may trigger the model to generate functional or spinal cord electrical stimulation and actuation assistance to enable the person to perform the desired exercise on the equipment.
[00018] The model may be further configured to generate sensory data to be feedback to the person via the HMI. The sensory data may include somatosensory, cardiac and/or respiratory data. Sensory data may be provided to person by visual, auditory and/or intact haptic feedback pathways. The HMI headset may provide these extended reality feedback by including virtual or augmented reality, or via ear phones and/or haptic devices.
[00019] According to another aspect of the present invention, there is provided a rehabilitation method for rehabilitating a person with a neurological condition, the method including:
exercising the person with exercise equipment;
sensing information from the exercising person and exercise equipment; and receiving the sensed information and generating functional electrical stimulation and actuator assistance for the person using a model of the exercising person using the exercise equipment.
[00020] Any of the features described herein can be combined in any combination with any one or more of the other features described herein within the scope of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[00021] Preferred features, embodiments and variations of the invention may be discerned from the following Detailed Description which provides sufficient information for those skilled in the art to perform the invention. The Detailed Description is not to be regarded as limiting the scope of the preceding Summary of the Invention in any way. The Detailed Description will make reference to drawing as follows: [00022] Figure 1 is a schematic view of a rehabilitation system in accordance with an embodiment of the present invention.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[00023] According to an embodiment of the present invention, there is provided a rehabilitation system 100 for rehabilitating a person 102 with a SCI. The system 100 includes exercise equipment 104, maybe in the form of a recumbent cycle or ergometer, which enables the person 102 to exercise. Personal wearable head, neck and body biosensors 106 and 107 (biomechanical and physiological biosensors) are provided for sensing personal information 108 and 109 from the person 102 during exercise.
[00024] The system 100 further includes a personalized computer model 1 10 of the exercising person 102 on the exercise equipment 104 for receiving the sensed personal information 108 and 109 from the sensors 106 and 107 and generating electrical stimulation and actuation assistance 1 12 for the person. Advantageously, the
personalized model 1 10 is used in generating suitable adaptive electrical stimulation for the person 102, and avoids excessive musculoskeletal, cardiac and pulmonary stresses on the person 102 as well as the potential fracturing of bones.
[00025] The rehabilitation system 100 further includes equipment sensors (e.g.
speed, torque, etc.) for sensing equipment information 1 14 of the equipment 104. The equipment information 1 14 is also provided to the model 1 10 and used in generating the electrical stimulation and actuation assistance 1 12.
[00026] The model 1 10 is configured to generate sensory data 1 16 to be provided to the person 102 through extended reality feedback 1 18 in the HMI 1 19. The extended reality 1 18 in HMI 1 19 includes a virtual or augmented reality headset 122 with visual 123, auditory 120 and tactile feedback 121 . The HMI 1 19 will employ a brain computer interface (BCI) 124 that may use EEG, EOG, eye gaze sensor data 109 in the headset 122 worn by the person 102. The model 1 10 is also configured to receive data from the BCI 124 that is the interpretation of the person’s required movement when the person 102 thinks about an action. The model 1 10 is typically stored in the cloud 126 and loT enabled. [00027] The functionality of the system 100 is now described in greater detail below.
[00028] The neuromusculoskeletal model 1 10 incorporates a Digital Twin, which is a computer representation of the person's bones, muscles, joints, and nervous system. The Digital Twin technology is used in real-time to virtually bypass the site of SCI, again connecting sensory and motor pathways between brain, spinal cord, and muscles.
[00029] The model 1 10 includes BioSpine, which is an innovative application of Digital Twin technology through the HMI headset 122 that is combined with
virtual/augmented reality 123, auditory 120 and haptic devices 121 , and biosensors 106 and 107. BioSpine integrates a unique set of intelligent rehabilitation assistive technologies controlled by the Digital Twin to restore the interrupted motor and sensory connections in the spine. BioSpine integrates the following discrete technologies into the seamless system 100: HMI 1 19, wearable biosensors 106 and 107, electrical stimulation 1 12 of lower limb muscles of the person 102, motor-assisted leg cycling in the case presented, augmented somatosensory signals 1 16 transformed extended reality 1 18 with visual 123, auditory 120 and haptic 121 biofeedback.
[00030] The system 100 is intuitively and automatically controlled by the personalised Digital Twin of the patient 102.
[00031] Personalised Digital Twins of each participant 102 can be developed combining magnetic resonance imaging (MRI) [2] and artificial intelligence methods.. Electroencephalograms (EEG) can be captured via a portable wireless headset (e.g. Wearable Sensing DSI7 or DSI-VR300, Switzerland) 122 and processed in the BCI 124 using Al methods to discriminate whether the patient wishes to perform, and how intensely they wish to do, in this example case, the cycling exercise.
[00032] In the example case of cycling, the patient’s motor intention to cycle data 124 will control the Digital Twin, which in turn will optimally stimulate muscles via electrical stimulation 1 12 and provide appropriate motorised assistance 1 12 to achieve cycling. Importantly, the Digital Twin coordinates the electrical stimulation 1 12 and motorised assistance 1 12 to ensure that stimulated muscle activation assists, rather than opposes, the motorised actuation to perform the movement. Biomechanical and physiological information 108 from multiple wearable biosensors 106 (e.g., electromyography, inertial measurement units, heart rate, electrocardiogram, and respiration) can be interpreted by the patient’s Digital Twin to progressively adapt the amount of ergometer pedal- assistance in order to maximally engage the patient 102, while also maintaining musculoskeletal tissue loads and cardiovascular demand within safe levels [3, 4]
[00033] Finally, the patient’s Digital Twin can synthesise somatosensory information 1 16 that will be redirected to higher somatosensory areas via extended reality 1 18, visual 123, auditory 120 and/or haptic 121 feedback [4, 5].
[00034] Off-the-shelf known pharmacological adjuncts (e.g., buspirone) with an established safety profile utilised in prior studies [6-10] can be added to measure their additive effect on neural plasticity. The system 100 is designed to retrofit and update existing, commercially available equipment 104.
[00035] A person skilled in the art will appreciate that many embodiments and variations can be made without departing from the ambit of the present invention.
[00036] The system 100 may further include an actuator, such as a robotic motor coupled to the drive crank, for actuating the exercise equipment 104. In use, the model 1 10 actuates the actuator to some extent to assist the person 102.
[00037] In compliance with the statute, the invention has been described in language more or less specific to structural or methodical features. It is to be understood that the invention is not limited to specific features shown or described since the means herein described comprises preferred forms of putting the invention into effect.
[00038] Reference throughout this specification to‘one embodiment’ or‘an embodiment’ means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearance of the phrases‘in one embodiment’ or‘in an embodiment’ in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more combinations.
[00039] References 1 . [Intentionally left blank]2. Brito da Luz S, Modenese L, Sancisi N, Mills PM, Kennedy B, Beck BR, et al. Feasibility of using MRIs to create subject-specific parallel- mechanism joint models. J Biomech 2017; 53: 45-55.
3. Pizzolato C, Lloyd DG, Zheng MH, Besier TF, Shim VB, Obst SJ, et al. Finding the sweet spot via personalised Achilles tendon training: the future is within reach. British Journal of Sports Medicine 2019; 53: 1 1 -12.
4. Pizzolato C, Lloyd DG, Barrett RS, Cook JL, Zheng MH, Besier TF, et al.
Bioinspired Technologies to Connect Musculoskeletal Mechanobiology to the Person for Training and Rehabilitation. Front Comput Neurosci 2017; 1 1 : 96.
5. Pizzolato C, Reggiani M, Modenese L, Lloyd DG. Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim. Comput Methods Biomech Biomed Engin 2017; 20: 436-445.
6. van den Brand R, Heutschi J, Barraud Q, DiGiovanna J, Bartholdi K, Huerlimann M, et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 2012; 336: 1 182-1 185.
7. Gerasimenko YP, Lu DC, Modaber M, Zdunowski S, Gad P, Sayenko DG, et al. Noninvasive Reactivation of Motor Descending Control after Paralysis. J Neurotrauma 2015; 32: 1968-1980.
8. Sayenko D, Rath M, Ferguson AR, Burdick J, Havton L, Edgerton VRPD, et al. Self-assisted standing enabled by non-invasive spinal stimulation after spinal cord injury. J Neurotrauma 2018.
9. Gill ML, Grahn PJ, Calvert JS, Linde MB, Lavrov IA, Strommen JA, et al.
Neuromodulation of lumbosacral spinal networks enables independent stepping after complete paraplegia. Nat Med 2018; 24: 1677-1682.
10. Angeli CA, Boakye M, Morton RA, Vogt J, Benton K, Chen Y, et al. Recovery of Over-Ground Walking after Chronic Motor Complete Spinal Cord Injury. N Engl J Med 2018; 379: 1244-1250.

Claims

The claims defining the invention are as follows:
1 . A rehabilitation system for rehabilitating a person with a neurological condition, the system including:
exercise equipment for enabling the person to exercise;
one or more sensors for sensing information from the person during exercise; and
a model of the exercising person configured to receive the sensed information from the sensors and generate electrical stimulation for the person.
2. A rehabilitation system as claimed in claim 1 , wherein model is personalized and generates suitable electrical stimulation for the person, and avoids excessive stresses on the person which can lead to the fracturing of bones.
3. A rehabilitation system as claimed in claim 1 , wherein the sensors include biomechanical and/or physiological biosensors.
4. A rehabilitation system as claimed in claim 1 , wherein the sensed information relates to any one or more of electromyography, inertial measurement units, heart rate, electrocardiogram, and respiration.
5. A rehabilitation system as claimed in claim 1 , wherein he sensors further include one or more equipment sensors for sensing information of the equipment that is also provided to the model for generating the electrical stimulation.
6. A rehabilitation system as claimed in claim 5, wherein the equipment sensors include speed or torque sensors.
7. A rehabilitation system as claimed in claim 1 , wherein the model is further configured to generate somatosensory data to be provided to the person through extended reality.
8. A rehabilitation system as claimed in claim 7, wherein the somatosensory data may be further provided via visual, auditory and/or haptic feedback.
9. A rehabilitation system as claimed in claim 7, wherein the model is configured to receive intention of movement data from a brain-computer interface (BCI).
10. A rehabilitation system as claimed in claim 7, wherein the BCI includes a virtual or augmented reality headset with visual, auditory and/or tactile feedback
1 1 . A rehabilitation system as claimed in claim 1 , wherein the sensors include wearable sensors worn by the person.
12. A rehabilitation system as claimed in claim 1 , wherein the model is personalized and includes a neuromusculoskeletal model or a digital twin.
13. A rehabilitation system as claimed in claim 1 , wherein the exercise equipment includes a recumbent ergometer.
14. A rehabilitation system as claimed in claim 1 , further including an actuator for actuating the exercise equipment.
15. A rehabilitation system as claimed in claim 14, wherein the actuator includes a motor.
16. A rehabilitation system for rehabilitating a person with a neurological condition, the system including:
exercise equipment for enabling the person to exercise;
a human machine interface with one or more sensors for sensing information from the person during exercise, the human machine interface having somatosensory feedback enabled by extended reality feedback, preferably via virtual reality, tactile via haptic feedback, or auditory by ear phones; and
a personalized computer model of the exercising person on the exercise equipment configured to receive the sensed information from the sensors and generate electrical stimulation, assisted by actuators, for the person, the personalized computer model able to synthesize and generate somatosensory feedback.
17. A rehabilitation method for rehabilitating a person with a neurological condition, the method including:
exercising the person with exercise equipment; sensing information from the exercising person; and
receiving the sensed information and generating electrical stimulation for the person using a model of the exercising person.
18. A rehabilitation method as claimed in claim 17, involving generating the electrical stimulation for whilst avoiding excessive stresses on the person which can lead to the fracturing of bones.
19. A rehabilitation method as claimed in claim 17, wherein the sensing involves any one or more of somatosensory, visual feedback, tactile and auditory sensing.
20. A rehabilitation method as claimed in claim 17, further involving actuating the exercise equipment.
PCT/AU2020/050566 2019-06-04 2020-06-04 Biospine: a digtial twin neurorehabilitation system Ceased WO2020243781A1 (en)

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EP20817609.9A EP3980112A4 (en) 2019-06-04 2020-06-04 BIOCOLUMN: NEUROREEDUCATION SYSTEM BASED ON A DIGITAL TWIN
AU2020287071A AU2020287071B2 (en) 2019-06-04 2020-06-04 Rehabilitation system
US17/614,208 US12330022B2 (en) 2019-06-04 2020-06-04 Rehabilitation system
JP2021572105A JP2022535563A (en) 2019-06-04 2020-06-04 Digital twin neurorehabilitation system
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