Rehabilitation system

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.

Claims

1. A rehabilitation system for lower limb rehabilitation of a person with a neurological condition, the system including: exercise equipment for enabling the person to exercise using at least one lower limb; a human machine interface including one or more body sensors for sensing information from the person during exercise; one or more equipment sensors for sensing information of the exercise equipment; and a personalized computer model which is a neuromusculoskeletal model including a digital twin of the person exercising using the exercise equipment, wherein the personalized computer model is configured to; receive the sensed information from the one or more body sensors and from the one or more equipment sensors, generate electrical stimulation and actuation assistance for the person exercising with the exercise equipment using the at least one lower limb, and generate somatosensory data to be provided to the person through extended reality.

2. The rehabilitation system as claimed in claim 1, wherein the personalized computer model generates suitable electrical stimulation while avoiding-avoids excessive stresses on the person-which can lead to fracturing of bones.

3. The rehabilitation system as claimed in claim 1, wherein the one or more body sensors include biomechanical and/or physiological biosensors.

4. The rehabilitation system as claimed in claim 1, wherein the sensed information relates to one or more of electromyography, inertial measurement units, heart rate, electrocardiogram, and respiration.

5. The rehabilitation system as claimed in claim 1, wherein the one or more equipment sensors include speed or torque sensors.

6. The rehabilitation system as claimed in claim 1, wherein the somatosensory data is further provided via visual, auditory and/or haptic feedback.

7. The rehabilitation system as claimed in claim 1, wherein the personalized computer model is configured to receive the person's intention of movement data from a brain-computer interface.

8. The rehabilitation system as claimed in claim 7, wherein the human machine interface provides somatosensory feedback enabled by extended reality feedback, wherein the extended reality feedback includes a virtual or augmented reality headset with visual, auditory and/or tactile feedback.

9. The rehabilitation system as claimed in claim 1, wherein the one or more body sensors include wearable sensors configured to be worn by the person.

10. The rehabilitation system as claimed in claim 1, wherein the exercise equipment includes a recumbent ergometer.

11. The rehabilitation system as claimed in claim 1, further including an actuator for actuating the exercise equipment.

12. The rehabilitation system as claimed in claim 11, wherein the actuator includes a motor.

13. The rehabilitation system as claimed in claim 12, wherein the extended reality feedback includes virtual reality, tactile via haptic feedback, or auditory by ear phones.

14. A rehabilitation system for lower limb rehabilitation of a person with a neurological condition, the system including: exercise equipment for enabling the person to exercise using at least one lower limb; a human machine interface with one or more body sensors for sensing information from the person during exercise, the human machine interface being configured to provide somatosensory feedback enabled by extended reality feedback; one or more equipment sensors for sensing information of the exercise equipment; and a personalized computer model which is a neuromusculoskeletal model including a digital twin of the person exercising using the exercise equipment, wherein the personalized computer model is configured to: receive the sensed information from the one or more body sensors and from the one or more equipment sensors, generate electrical stimulation and actuation assistance, assisted by actuators, for the person exercising with the exercise equipment using the at least one lower limb, and synthesize and generate somatosensory data to be provided to the person through the extended reality feedback.

15. A rehabilitation method for lower limb rehabilitation of a person with a neurological condition, the method including: exercising the person with exercise equipment using at least one lower limb; sensing information from the person exercising using one or more body sensors of a human machine interface; sensing information of the exercise equipment using one or more equipment sensors; and providing a personalized computer model which is a neuromusculoskeletal model including a digital twin of the person exercising using the exercise equipment, wherein the personalized computer model is configured to: receive the sensed information from the exercising person and of the exercise equipment, generate electrical stimulation and actuation assistance for the person exercising with the exercise equipment using the at least one lower limb, and generate somatosensory data for provision to the person through extended reality.

16. The rehabilitation method as claimed in claim 15, wherein the personalized computer model generates suitable electrical stimulation while avoiding excessive stresses on the person which can lead to fracturing of bones.

17. The rehabilitation method as claimed in claim 15, wherein the sensing information from the exercising person includes one or more of somatosensory, visual feedback, tactile and auditory sensing.

18. The rehabilitation method as claimed in claim 15, further including actuating the exercise equipment.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) 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:

(2) FIG. 1 is a schematic view of a rehabilitation system in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

(3) 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.

(4) The system 100 further includes a personalized computer model 110 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 112 for the person. Advantageously, the personalized model 110 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.

(5) The rehabilitation system 100 further includes equipment sensors (e.g. speed, torque, etc.) for sensing equipment information 114 of the equipment 104. The equipment information 114 is also provided to the model 110 and used in generating the electrical stimulation and actuation assistance 112.

(6) The model 110 is configured to generate sensory data 116 to be provided to the person 102 through extended reality feedback 118 in the HMI 119. The extended reality 118 in HMI 119 includes a virtual or augmented reality headset 122 with visual 123, auditory 120 and tactile feedback 121. The HMI 119 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 110 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 110 is typically stored in the cloud 126 and IoT enabled.

(7) The functionality of the system 100 is now described in greater detail below.

(8) The neuromusculoskeletal model 110 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.

(9) The model 110 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 119, wearable biosensors 106 and 107, electrical stimulation 112 of lower limb muscles of the person 102, motor-assisted leg cycling in the case presented, augmented somatosensory signals 116 transformed extended reality 118 with visual 123, auditory 120 and haptic 121 biofeedback.

(10) The system 100 is intuitively and automatically controlled by the personalised Digital Twin of the patient 102.

(11) 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 AI methods to discriminate whether the patient wishes to perform, and how intensely they wish to do, in this example case, the cycling exercise.

(12) 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 112 and provide appropriate motorised assistance 112 to achieve cycling. Importantly, the Digital Twin coordinates the electrical stimulation 112 and motorised assistance 112 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].

(13) Finally, the patient's Digital Twin can synthesise somatosensory information 116 that will be redirected to higher somatosensory areas via extended reality 118, visual 123, auditory 120 and/or haptic 121 feedback [4, 5].

(14) 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.

(15) 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.

(16) 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 110 actuates the actuator to some extent to assist the person 102.

(17) 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.

(18) 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.

REFERENCES

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