Rehabilitation system

A rehabilitation system includes: a brain activity measuring device which measures brain activity of a patient who carries out training based on a set amount of training; a motion measuring device which measures a motion state of a paralyzed site of the patient; a spasticity state determiner which determines a spasticity state based on the brain activity and the motion state; an updater which updates the amount of training based on a result of determination by the spasticity state determiner; and a presentation device which presents the updated amount of training to the patient.

BACKGROUND

1. Technical Field

The present invention relates to a rehabilitation system.

2. Related Art

According to the related art, a rehabilitation system which decides the amount of training based on the result of analysis of brain waves before the start of rehabilitation training is known, for example, as disclosed in WO2016/002207. This can provide a rehabilitation program suitable for the patient, and improved effects of the rehabilitation can be expected.

However, the rehabilitation system disclosed in WO2016/002207 has a problem that it is difficult to cope with changes in the condition of the patient during training. For example, if the condition of the patient deteriorates during training, it may be difficult to continue training. Meanwhile, if the condition of the patient becomes better during training, the patient can accept training with higher load. With the rehabilitation system disclosed in WO2016/002207, it is difficult to cope with changes in the condition of the patient when such changes take place during training.

SUMMARY

APPLICATION EXAMPLE 1

A rehabilitation system according to this application example includes: a brain activity measuring device which measures brain activity of a patient who carries out training based on a set amount of training; a motion measuring device which measures a motion state of a paralyzed site of the patient; a spasticity state determiner which determines a spasticity state based on the brain activity and the motion state; an updater which updates the amount of training based on a result of determination by the spasticity state determiner; and a presentation device which presents the updated amount of training to the patient.

According to this application example, the spasticity state is determined based on the result of measuring the brain activity and the motion state, and the amount of training can be updated based on the determined spasticity state. The updated amount of training can be presented to the patient during training. With this rehabilitation system, the amount of training updated in response to changes in the condition of the patient during training can be presented to the patient. Therefore, it is possible to cope with changes in the condition of the patient during training.

APPLICATION EXAMPLE 2

In the rehabilitation system according to the application example, it is preferable that the updater updates the amount of training in such a way as to reduce the amount of training when the result of determination by the spasticity state determiner indicates a spasticity.

According to this application example, the amount of training which overloads the patient having a spasticity can be reduced.

APPLICATION EXAMPLE 3

In the rehabilitation system according to the application example, it is preferable that the spasticity state determiner detects a motion intention from a result of measuring the brain activity, detects muscle activity from a result of measuring the motion state, and determines the spasticity state based on a result of detection of the motion intention and a result of detection of the muscle activity.

According to this application example, the spasticity state can be determined based on the motion intention detected from the result of measuring the brain activity and the muscle activity detected from the result of measuring the motion state.

APPLICATION EXAMPLE 4

In the rehabilitation system according to the application example, it is preferable that the spasticity state determiner determines that it is the spasticity, when the motion intention is not detected from the result of measuring the brain activity and the muscle activity is detected from the result of measuring the motion state.

According to this application example, it can be determined as a spasticity when the motion intention is not detected and the muscle activity is detected.

APPLICATION EXAMPLE 5

In the rehabilitation system according to the application example, it is preferable that the spasticity state determiner determines that a motion intention is detected when the result of measuring the brain activity exceeds a predetermined threshold.

According to this application example, it can be detected that a motion intention is detected, based on that the result of measuring the brain activity exceeds the predetermined threshold.

APPLICATION EXAMPLE 6

In the rehabilitation system according to the application example, it is preferable that the spasticity state determiner determines that muscle activity is detected when the result of measuring the motion state exceeds a predetermined threshold.

According to this application example, it can be determined that muscle activity is detected, based on that the result of measuring the motion state exceeds the predetermined threshold.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the invention will be described with reference to the drawings. In the illustrations below, individual members are not to scale in order to show these members in recognizable sizes.

FIG. 1is a schematic view showing the configuration of a rehabilitation system1according to Embodiment 1. The rehabilitation system1includes a brain activity measuring device100, a motion measuring device200, a presentation device300, and a control device400. As illustrated, the rehabilitation system1is a system for supporting rehabilitation of a patient with a paralyzed site. In the description below, the term “rehabilitation” may be abbreviated as “rehab”.

First, the schematic configurations of the brain activity measuring device100, the motion measuring device200, the presentation device300, and the control device400according to Embodiment 1 will be described. The brain activity measuring device100is installed on the head of a patient and measures the brain activity of the patient. The motion measuring device200is stalled at a paralyzed site of the patient and measures the motion state of the patient. The control device400receives the results of measurement by the brain activity measuring device100and the motion measuring device200, determines a motion intention and muscle activity, and makes a determination on the spasticity state according to the result of determining the motion intention and muscle activity. The control device400updates the amount of training for rehabilitation, based on the result of the determination on the spasticity state. The control device400transmits the updated amount of training to the presentation device300, which presents the amount of training to the patient.

In this embodiment, the brain activity measuring device100is a near-infrared spectroscopy (NIRS) device which acquires the hemoglobin concentration in the cerebral blood flow and includes a light source11, a light receiver12, and a cerebral blood flow detection circuit13.

The light source11and the light receiver12are installed in contact with the scalp of the patient. In this embodiment, the brain activity measuring device100includes a plurality of light sources11and a plurality of light receivers12. The light outputted from each light source11is absorbed by the cerebral blood flow. Each light receiver12acquires the light intensity after the absorption, on the scalp surface. The cerebral blood flow detection circuit13calculates the absorbance from the acquired light intensity and the light intensity of the light source. The absorbance of hemoglobin in the cerebral blood flow is decided by the wavelength of the light. Therefore, the cerebral blood flow detection circuit13processes variations in the calculated absorbance and thus calculates the amount of change in the hemoglobin concentration in the brain region situated between the light source11and the light receiver12. The brain activity measuring device100measures the brain activity of the patient, based on the calculated amount of change in the hemoglobin concentration. That is, in this embodiment, the brain activity measuring device100measures the brain activity, based on the hemoglobin concentration in the cerebral blood flow. The brain activity measuring device100transmits the result of measuring the brain activity to the control device400as a result of brain activity measurement. The cerebral blood flow detection circuit13has a transmission function. The result of brain activity measurement is transmitted to the control device400via wired communication by the transmission function of the cerebral blood flow detection circuit13. The transmission measure may be wired or wireless.

In this embodiment, the motion measuring device200is a device (electromyography or EMG) which acquires surface potential and includes an electrode14and an electromyography detection circuit15.

The electrode14is installed on the skin of the paralyzed site of the patient. In this embodiment, the motion measuring device200includes a plurality of electrodes14. When the patient moves muscles of the paralyzed site, motor neurons of neural cells present in the muscles become active. In response to the activity of the motor neurons, the electrode14acquires surface potential on the skin situated in the muscle region where the motor neurons are active. The electromyography detection circuit15processes variations in the acquired surface potential and thus calculates the amount of change in the surface potential at the electrode14. The motion measuring device200measures the motion state of the paralyzed site of the patient, based on the amount of change in the surface potential detected via the electrode14. That is, in this embodiment, the motion measuring device200measures the motion state, based on the surface potential of the paralyzed site of the patient. The motion measuring device200transmits the result of measuring the motion state to the control device400as a result of motion state measurement. The electromyography detection circuit15has a transmission function. The result of motion state measurement is transmitted to the control device400via wired communication by the transmission function of the electromyography detection circuit15. The transmission measure may be wired or wireless.

The presentation device300is a head-mounted display device (HMD). The control device400is an HMD controller. The presentation device300includes a display16. The control device400includes a touch pad17and an operation button set18.

The display16can allow the patient to visually recognize a virtual image. The display16is an optical see-through display which enables direct visual recognition of the real space. The presentation device300can present the amount of training to the patient via the display16. The touch pad17detects a contact operation on the operation surface of the touch pad17and outputs a signal corresponding to the detection content. As the touch pad17, various touch pads such as electrostatic, pressure detection-type, and optical touch pads can be employed. The operation button set18includes various operation buttons. The operation button set18detects an operation on each operation button and outputs a signal corresponding to the detection content. The touch pad17and the operation button set18are operated by the user. The user may be not only the patient but also an attendant such as a doctor or physiotherapist.

FIG. 2is a block diagram functionally showing the configurations of the presentation device300and the control device400.

The presentation device300has a left-eye display19L and a right-eye display19R and is connected to a CPU21of the control device400.

The right-eye display19R has a configuration symmetrical to the configuration of the left-eye display19L and displays an image similar to the image displayed on the left-eye display19L. As a result, the patient can recognize the images by wearing the presentation device300on the head. Also, since at least a part of the light from the real space is transmitted through the presentation device300, the patient with the presentation device300left on the head can see the real space.

The control device400includes a receiver20, a CPU (central processing unit)21, a storage22, a motion model database23, an input information acquirer24, and a power supply25.

The receiver20includes a cerebral blood flow receiver26and an electromyography receiver27. The cerebral blood flow receiver26receives the result of brain activity measurement transmitted from the brain activity measuring device100. The electromyography receiver27receives the result of motion state measurement transmitted from the motion measuring device200. The receiver20outputs the result of brain activity measurement received by the cerebral blood flow receiver26and the result of motion state measurement received by the electromyography receiver27to the CPU21.

The CPU21includes a rehab processor28. The CPU21reads out a computer program stored in the storage22and executes the program, thus realizing various functions. Specifically, when a detection content of an operation is inputted from the input information acquirer24, the CPU21realizes the function of executing processing corresponding to the detection content, the function of reading data from and writing data to the storage22, and the function of controlling the supply of electricity to each component from the power supply25. The rehab processor28carries out the setting of the amount of training and the update of the amount of training, based on inputs from the cerebral blood flow receiver26and the electromyography receiver27.

The CPU21also reads out a rehab program stored in the storage22and executes the rehab program, thus functioning as the rehab processor28executing rehab processing for the recovery of functions of a disabled body part. In this embodiment, the rehab processing is for the recovery of functions of a hand as a disabled body part. The disability may be, for example, paralysis due to cerebral apoplexy.

The storage22is made up of a ROM (read only memory), RAM (random access read only memory), DRAM (dynamic random access memory), HDD (hard disk drive) or the like. In the storage22, various computer programs including an OS (operating system) are stored. In this embodiment, one of the stored computer programs is the rehab program.

The motion model database23is a database in which motion models are accumulated. A motion model is dynamic image data which models a target motion in rehab. The motion model may be a set of still image data instead of dynamic image data. Moreover, the motion model may be data made up of a set of feature point positions on the hand and can be replaced with any data that can construct a dynamic image. Also, the motion model may include a parameter such as the number of times and speed of the motion.

The input information acquirer24includes the touch pad17and the operation button set18which are described above. The input information acquirer24inputs a signal corresponding to the detection content from the touch pad17or the operation button set18.

The power supply25supplies electricity to each component that needs electricity, provided in the control device400and the presentation device300.

FIG. 3is a control block diagram of the rehabilitation system1.

As described above, the brain activity measuring device100has the light receiver12and the cerebral blood flow detection circuit13. The cerebral blood flow detection circuit13has a cerebral blood flow measurer29and a cerebral blood flow transmitter30. An output signal from the light receiver12is subjected to signal processing by the cerebral blood flow measurer29and then transmitted from the cerebral blood flow transmitter30to the control device400.

The motion measuring device200has the electrode14and the electromyography detection circuit15. The electromyography detection circuit15has an electromyography measurer31and an electromyography transmitter32. An output signal from the electrode14is subjected to signal processing by the electromyography measurer31and then transmitted from the electromyography transmitter32to the control device400.

The control device400has the cerebral blood flow receiver26, the electromyography receiver27, the rehab processor28, and a presentation device controller33. The cerebral blood flow receiver26receives the result of brain activity measurement transmitted from the brain activity measuring device100. The electromyography receiver27receives the result of motion state measurement transmitted from the motion measuring device200. The rehab processor28, described later, outputs the amount of training to be presented to the patient, to the presentation device controller33, based on the result of brain activity measurement and the result of motion state measurement. The presentation device controller33prepares a display image based on the amount of training inputted from the rehab processor28.

The presentation device300has the display16. The display16presents the amount of training to the patient, based on an instruction from the control device400.

FIG. 4is a control block diagram of the control device400.

As shown inFIG. 4, the rehab processor28has an amount-of-training setter34and an updater35. The updater35has a spasticity state determiner36.

The amount-of-training setter34sets the amount of training, based on the result of brain activity measurement inputted from the cerebral blood flow receiver26and the result of muscle activity measurement inputted from the electromyography receiver27. In this embodiment, for example, the initial amount of training is set, based on the result of brain activity measurement on the initial stage (hereinafter referred to as the result of initial brain activity measurement) and the result of motion state measurement on the initial stage (hereinafter referred to as the result of initial motion state measurement). The set amount of training is outputted to the presentation device controller33.

The updater35updates the amount of training, based on the result of brain activity measurement inputted from the cerebral blood flow receiver26and the result of muscle activity measurement inputted from the electromyography receiver27during training. The spasticity state determiner36detects a motion intention, based on the result of brain activity measurement received by the cerebral blood flow receiver26. The spasticity state determiner36also detects muscle activity, based on the result of motion state measurement received by the electromyography receiver27. The spasticity state determiner36determines whether the patient is in a spasticity state or not, based on the result of the detection of a motion intention and the result of the detection of muscle activity. The updater35updates the amount of training for rehab, based on the result of the determination by the spasticity state determiner36. The updated amount of training is outputted to the presentation device controller33.

FIG. 5is a flowchart showing training processing for rehabilitation with the rehabilitation system1. This training processing is executed by the rehab processor28and is started by the CPU21when a predetermined operation using the touch pad17or the operation button set18of the input information acquirer24is accepted.

As the training processing is started, the CPU21in Step S1acquires the result of brain activity measurement and the result of motion state measurement. In Step S1, the result of brain activity measurement and the result of motion state measurement when the patient is in a resting state are acquired. At this point, the brain activity measuring device100outputs the result of measuring the brain activity of the patient in the resting state to the control device400as the result of initial brain activity measurement, based on an instruction from the CPU21. The motion measuring device200outputs the result of measuring the motion state of the patient in the resting state to the control device400as the result of initial motion state measurement, based on an instruction from the CPU21. At this time, a display to instruct the patient to keep resting is presented on the presentation device300. The term “initial” refers to a reference state for calculating the amount of change in the brain activity and the motion state, and in this embodiment, refers to the resting state where the patient is not moving. The result of initial brain activity measurement and the result of initial motion state measurement are saved in the storage22of the control device400.

In Step S2, the CPU21reads the initially set amount of training from the storage22. The amount of training is set according to the condition of the patient and includes a training intensity and a training content. The training content indicates the content of a motion in training that involves a motion from among different kinds of training for rehab.

The training content includes specifying of a site as a training target (hereinafter referred to as a training target site) and a movement to be made by the training target site. For example, in the case of a patient having a paralysis in the right forearm as an example, the right forearm is specified as the training target site. In this example, as the movement to be made by the training target site, the movement of closing and opening the right hand repeatedly is specified.

The training intensity is a parameter of training that involves a motion, and includes a motion speed, a motion time, a motion interval, and an update time. The motion speed indicates the speed of the movement to be made by the training target site. In the example where the right arm is specified as the training target site, the motion speed indicates the speed of closing and opening the hand. The motion time indicates the time for which the motion is carried out, and here indicates the time for which the movement of closing and opening the hand repeatedly is made. Instead of the motion time, the number of times of the motion may be used as a parameter. The number of times of the motion indicates the number of times the movement to be made repeatedly is repeated. The motion interval indicates the interval (time) between one movement and the next movement, of the movement to be made repeatedly. The update time indicates the time (interval) at which the amount of training is updated. The update of the amount of training will be described later.

As the initial setting of the amount of training, a paralyzed site and a plurality of amounts of training corresponding to the degrees of seriousness of the paralysis of the patient are saved in the storage22. The initial setting is selected by the CPU21reading the content in the storage22, based on an output from the input information acquirer24. As a selection method, the initial setting may be selected by the patient or a physiotherapist, or may be selected based on the result of analysis of the brain activity of the patient. In the case of selecting the initial setting based on the result of analysis of the brain activity of the patient, for example, a method of deciding the amount of training according to the activity intensity in the alpha range (8 to 13 hertz) of the brain waves of the patient can be employed.

In Step S3, the CPU21causes the display16to display a display image based on the amount of training. This is carried out by the CPU21outputting an instruction to the presentation device controller33. The presentation device300causes the display16to display the display image based on the amount of training in response to the instruction from the CPU21. Thus, the amount of training is presented to the patient via the presentation device300. The content to be presented may be a video showing the movement of the paralyzed site which the patient is to train, or may be an image showing the entire amount of training. As the video of the movement of the paralyzed site, for example, in the case of the example where the right arm is specified as the training target site, a video (dynamic image) of a hand closing and opening may be employed. The patient can make the movement of closing and opening the hand with this video. That is, the presentation of the amount of training can function as an instruction for training. Particularly in this embodiment, since a see-through HMD is employed as the presentation device300, the patient can be allowed to visually recognize his/her own right art in such away as to overlap the video of the movement of closing and opening the hand. This can facilitate the enhancement of the effects of training through the video. As the presentation device300, a non-see-through HMD may be employed as well. In this case, the patient cannot visually recognize any other scenes but the display content and therefore can concentrate on the video of the movement more easily.

In Step S4, the CPU21notifies the presentation device controller33of the start of training. This notification is carried out by the CPU21outputting an instruction of notification to the presentation device controller33. The presentation device300causes the display16to display a display image showing a notification of the start of training, based on the instruction from the CPU21. Thus, the patient starts training according to the display image displayed on the display16. At this point, the CPU21saves the start time of training in the storage22.

In Step S5, the CPU21determines whether the update time has passed. The passage of the update time is determined by determining whether the difference between the start time of training and the current time has reached the update time or not. If it is determined that the update time is reached (YES), the processing shifts to Step S6. If it is determined the update time is not reached (NO), the processing waits until the update time passes. The method for measuring the time elapsed is not limited to calculating the difference between the start time of training and the current time. Measuring the time from the start of training to the present with a timer can be employed as well.

In Step S6, the CPU21acquires the result of brain activity measurement and the result of motion state measurement during training. The brain activity measuring device100outputs the result of measuring the brain activity of the patient during training to the control device400as the result of brain activity measurement, based on an instruction from the CPU21. The motion measuring device200outputs the result of measuring the motion state of the patient during training to the control device400as the result of motion state measurement, based on an instruction from the CPU21.

In Step S7, the CPU21carries out update processing, described later, and then shifts to Step S8. In the update processing, the CPU21updates the amount of training, based on the result of brain activity measurement and the result of motion state measurement during training.

In Step S8, the CPU21causes the display16to display a display image, based on the amount of training (amount of training that is updated) outputted from the update processing, described later. This is carried out by the CPU21outputting an instruction to the presentation device controller33. The presentation device300causes the display16to display the display image based on the amount of training that is updated (hereinafter referred to as the updated amount of training) in response to the instruction from the CPU21. Thus, the updated amount of training is presented to the patient via the presentation device300. The content to be presented may be a video showing the movement of the paralyzed site which the patient is to train, or may be an image showing the entirety of the updated amount of training.

In Step S9, the presentation device controller33issues a notification prompting the continuation of training based on the updated amount of training. This notification is carried out by the CPU21outputting an instruction of notification to the presentation device controller33. The presentation device300causes the display16to display a display image showing a notification of the continuation of training in response to the instruction from the CPU21. Thus, the patient continues training based on the updated amount of training according to the display image displayed on the display16.

In Step S10, the CPU21determines whether training with the set amount of training is finished. This can be realized by determining whether the time elapsed from the start of training has reached the motion time or not. The CPU21calculates the difference between the start time of training and the current time and thus calculates the time elapsed. The CPU21then compares the time elapsed with the motion time that is set with the amount of training, and thus determines whether training with the set amount of training is finished or not. If it is determined that training with the set amount of training is finished (YES), the training processing ends. If it is determined that training with the set amount of training is not finished (NO), the processing returns to Step S5to continue the training processing.

FIG. 6is a flowchart of the update processing. This update processing is the processing of the updater35and is carried out by the CPU21every update time.

In the update processing, the CPU21updates the amount of training, based on the result of initial brain activity measurement and the result of brain activity measurement during training and also on the result of initial motion state measurement and the result of motion state measurement during training. As the update processing is started, the CPU21in Step S101determines whether the result of brain activity measurement exceeds a predetermined threshold or not. The result of brain activity measurement during training is the result of brain activity measurement acquired in Step S6inFIG. 5. At this point, if the result of brain activity measurement exceeds the predetermined threshold (YES), the CPU21determines that a motion intention is detected. The motion intention refers to brain activity generated when the training target site moves. In other words, the motion intension refers to brain activity generated when the patient moves intentionally. That is, the motion intention is an indicator indicating whether the motion is based on the intention of the patient or not. In this embodiment, the result of initial brain activity measurement is employed as the predetermined threshold. As the state where the predetermined threshold is exceeded, the state where the result of brain activity measurement during training is greater than the result of initial brain activity measurement and where there is a statistically significant difference between these is employed. As the state where there is a statistically significant difference, for example, the state where the p-value, which is a statistical value, is less than 0.05 can be employed. The CPU21compares the result of initial brain activity measurement saved in the storage22with the result of brain activity measurement during training. At this point, if the result of brain activity measurement during training is greater than the result of initial brain activity measurement (YES), it is determined that a motion intention is detected, and the processing shifts to Step S104. If a motion intention is not detected (NO), the processing shifts to Step S102.

In Step S102, the CPU21determines whether the result of motion state measurement exceeds a predetermined threshold or not. The result of motion state measurement during training is the result of motion state measurement acquired in Step S6inFIG. 5. At this point, if the result of motion state measurement exceeds the predetermined threshold (YES), the CPU21determines that muscle activity is detected. The muscle activity refers to the movement of muscles generated when the training target site moves. In this embodiment, the result of initial motion state measurement is employed as the predetermined threshold. As the state where the predetermined threshold is exceeded, the state where the result of motion state measurement during training is greater than the result of initial motion state measurement and where there is a statistically significant difference between these is employed. As the state where there is a statistically significant difference, for example, the state where the p-value, which is a statistical value, is less than 0.05 can be employed. The CPU21compares the result of initial motion state measurement saved in the storage22with the result of motion state measurement during training. At this point, if the result of motion state measurement during training is greater than the result of initial motion state measurement (YES), it is determined that muscle activity is detected, and the processing shifts to Step S103. If muscle activity is not detected (NO), the processing shifts to Step S104. The processing of Step S101and the processing of Step S102correspond to the processing of the spasticity state determiner36.

In Step S103, the CPU21reduces the amount of training and then the processing returns to Step S8of the training processing (FIG. 5). When the processing shifts to Step S103, it is determined that the patient is in the spasticity state. The spasticity state refers to the state where muscle activity is generated without any motion intention of the patient. If the patient during training has a spasticity, it is conceivable that the amount of training is overloading the patient. Thus, in Step S103, the CPU21reduces the amount of training. The reduction in the amount of training is realized by the CPU21selecting an amount of training from data of a plurality of reduced amounts of training saved in advance in the storage22. The reduction in the amount of training may be, for example, reducing the motion speed, making the motion easier, or the like. In this embodiment, for example, slowing down the speed of the training movement, employing a movement of stretching the paralyzed hand with the normal hand as the training movement, or the like, may be employed.

In Step S104, the CPU21maintains the amount of training or increase the amount of training and then the processing returns to Step S8of the training processing (FIG. 5). The increase in the amount of training is realized by the CPU21selecting an amount of training from data of a plurality of increased amounts of training saved in advance in the storage22. The increase in the amount of training may be, for example, increasing the motion speed, making the motion more difficult, or the like. In this embodiment, for example, increasing the speed of the training motion, employing a movement of bending and stretching each finger as the training movement, or the like, may be employed.

As described above, the rehabilitation system1according this embodiment can achieve the following effects.

The rehabilitation system1can cope with changes in the condition of the patient during training. In the rehabilitation system1, the spasticity state is determined based on the results of measuring the brain activity and the motion state, and the amount of training can be updated based on the determined spasticity state. The updated amount of training can be presented to the patient during training. With the rehabilitation system1, the amount of training updated according to changes in the condition of the patient during training can be presented to the patient. Therefore, it is possible to cope with changes in the condition of the patient during training. For example, when the condition of the patient deteriorates during training, the patient can continue training based on the reduced updated amount of training. Meanwhile, when the condition of the patient gets better during training, the patient can continue training based on the updated amount of training which is an amount of training with higher load.

In this embodiment, as the CPU21executes a program, each functional component in the control device400is realized by the operation of software. However, each functional component in the control device400can also be realized, for example, by hardware such as an integrated circuit or by the collaboration of software and hardware.

FIG. 7is a schematic view showing the configuration of a rehabilitation system2according to Embodiment 2. The rehabilitation system2according to this embodiment will be described with reference toFIG. 7. The same components as in Embodiment 1 are denoted by the same reference numbers and will not be described further.

InFIG. 7, a brain activity measuring device100according to Embodiment 2 is an electroencephalograph (EEG) which acquires cranial nerve potential, and includes a brain wave electrode37and a brain wave measuring circuit38.

The brain wave electrode37is installed in contact with the scalp of the patient. In this embodiment, the brain activity measuring device100includes a plurality of brain wave electrodes37. When the patient intends to move, brain neurons work. The brain wave electrode37acquires the surface potential on the skin situated in an active brain region in response to the activity of brain neurons. A part of the plurality of brain wave electrodes37may be set as a reference electrode, and a reference surface potential at the time of detecting a motion intention may be acquired at the reference electrode. The brain wave measuring circuit38processes variations in the acquired surface potential and thus calculates the amount of change in the surface potential detected via the brain wave electrodes37. The brain activity measuring device100measures the brain activity of the patient, based on the amount of change in the surface potential detected via the brain wave electrodes37. That is, in this embodiment, the brain activity measuring device100measures the brain activity based on the surface potential of the scalp of the patient. The brain activity measuring device100transmits the result of measuring the brain activity to the control device400as a result of brain activity measurement. The brain wave measuring circuit38has a transmission function. The result of brain activity measurement is transmitted to the control device400via wired communication by the transmission function of the brain wave measuring circuit38. The transmission measure may be wired or wireless.

In Embodiment 2, in Step S101shown inFIG. 6, the result of measurement by the reference electrode of the brain wave electrodes37may be used as the predetermined threshold. The CPU21compares the result of measurement by the reference electrode with the result of brain activity measurement by the electrodes other than the reference electrode, of the brain wave electrodes37. If the result of brain activity measurement by the other electrodes is greater than the result of measurement by the reference electrode, it is determined that a motion intention is detected and the processing of Step S104is carried out. If a motion intention is not detected, the processing of Step S102is carried out.

Embodiment 2 has effects similar to those of Embodiment 1. Also, as described above, the rehabilitation system2according to this embodiment can achieve the following effects in addition to the effects of Embodiment 1.

The rehabilitation system can be applied to the case when the cerebral blood flow of a patient is difficult to measure. For example, with a patient who has experienced cerebral apoplexy, it may be difficult to stably acquire a cerebral blood flow because of damage to blood vessels or swelling of a brain site. Brain waves are acquired by measuring the surface potential of neural cells and therefore can be easily measured with a patient who has experienced cerebral apoplexy.

The invention is not limited to the foregoing embodiments. Various changes and improvements can be added to the embodiments. Modifications will be described below.

In Embodiment 1, a NIRS device which measures the cerebral blood flow is used as the brain activity measuring device100. As a modification to this, an fMRI (functional magnetic resonance imaging) device may be used as the brain activity measuring device100. The fMRI device is a device which measures the magnetic susceptibility of hemoglobin in the cerebral blood flow via a magnetic field. The brain activity of the patient is measured, based on the amount of change in the magnetic susceptibility of hemoglobin.

In Embodiment 2, an EEG which measures the surface potential of cranial nerves is used as the brain activity measuring device100. As a modification to this, an MEG (magnetoencephalograph) may be used as the brain activity measuring device100. The MEG is a device which measures a magnetic field based on the surface potential on the scalp, generated by the activity of brain neurons. The brain activity of the patient is measured, based on the magnetic field generated by the brain activity.

In the foregoing embodiments and modifications, an electromyograph which measures the surface potential of muscles is used as the motion measuring device200. As a modification to this, MMG (mechanomyogram) may be used. The MMG measures movements of muscles as vibrations on the skin surface. Also, a joint movement angle may be used. As this joint movement angle, the joint movement angle at a paralyzed site is measured by an acceleration sensor or goniometer.

In the foregoing embodiments and modifications, the HMD is a see-through display device which does not shut off the field of view of the user when the user wears the HMD. As a modification to this, the HMD may be a non-see-through display device which shuts off the field of view of the user. Also, while the HMD is described as having the left-eye display19L and the right-eye display19R, the HMD may instead have a display for one eye only. Moreover, not only the HMD but also a liquid crystal display or projector may used for display.

In the foregoing embodiments and modifications, examples where functions of a hand are to be recovered with the rehabilitation system1or the rehabilitation system2are described. As a modification of the rehabilitation system1or the rehabilitation system2, a rehabilitation system for recovering functions of a wrist or arm joint may be employed. Also, a rehabilitation system for recovering functions of a toe, ankle or knee may be employed.

In the foregoing embodiments and modifications, the control device400is an HMD controller. As a modification to this, the control device400may be integrated with a device such as the presentation device300, the brain activity measuring device100, or the motion measuring device200.

The entire disclosure of Japanese Patent Application No. 2016-204929 filed Oct. 19, 2016 is expressly incorporated by reference herein.