JP2015198834A - Determining the recovery stage of patients with cerebrovascular disorders using Microsoft Kinect (registered trademark) - Google Patents
Determining the recovery stage of patients with cerebrovascular disorders using Microsoft Kinect (registered trademark) Download PDFInfo
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- JP2015198834A JP2015198834A JP2014080432A JP2014080432A JP2015198834A JP 2015198834 A JP2015198834 A JP 2015198834A JP 2014080432 A JP2014080432 A JP 2014080432A JP 2014080432 A JP2014080432 A JP 2014080432A JP 2015198834 A JP2015198834 A JP 2015198834A
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本発明は医師、理学療法士、作業療法士による脳血管障害者の回復段階の判定を画像解析で行う方法に関するものである。 The present invention relates to a method for determining the recovery stage of a cerebrovascular disorder person by image analysis by a doctor, a physical therapist, or an occupational therapist.
Microsoft Kinect (登録商標)を用いて脳血管障害者の回復段階を判定する方法は存在していない。 There is no method for determining the recovery stage of patients with cerebrovascular disorders using Microsoft Kinect (registered trademark).
解決しようとする課題は、医師や理学療法士、作業療法士が不在の場合でもMicrosoft Kinect (登録商標)の観測情報を使うことで脳血管障害者の回復段階を判定する方法の提案である。 The problem to be solved is a proposal of a method for determining the recovery stage of a cerebrovascular disorder person by using observation information of Microsoft Kinect (registered trademark) even in the absence of a doctor, a physical therapist, or an occupational therapist.
健常者の動作をMicrosoft Kinect (登録商標)で観測する。その時健常者の特徴点を観測画像から決定する。特徴点の3次元座標をMicrosoft Kinect (登録商標)で決定し、脳血管障害者の3次元座標と比較し一致率を統計処理する。これにより医師や理学療法士、作業療法士が不在でも脳血管障害者の機能回復段階の判定が可能となる。 Observe the behavior of healthy individuals with Microsoft Kinect (registered trademark). At that time, the feature point of the healthy person is determined from the observed image. The three-dimensional coordinates of the feature points are determined by Microsoft Kinect (registered trademark), and compared with the three-dimensional coordinates of the cerebrovascular disorder person, the matching rate is statistically processed. This makes it possible to determine the stage of functional recovery for persons with cerebrovascular disorders even in the absence of a doctor, physical therapist, or occupational therapist.
Microsoft Kinect (登録商標)とコンピュータ、モニタを組み合わせた数万円程度のシステムで脳血管障害者の回復段階を判定することができる。このことにより、医師や理学療法士、作業療法士が不足している地域において機能回復評価が可能となる。例えば、日本の農村地域、海外のリハビリテーション後進国である。 The recovery stage of people with cerebrovascular disorders can be determined with a system of about tens of thousands of yen that combines Microsoft Kinect (registered trademark), computer, and monitor. This enables functional recovery assessment in areas where doctors, physical therapists, and occupational therapists are lacking. For example, Japan's rural areas and overseas rehabilitation developed countries.
Microsoft Kinect (登録商標)とコンピュータ、モニタで構成される。 It consists of Microsoft Kinect (registered trademark), computer and monitor.
図1は脳血管障害者の回復段階を判定する原理図である。図示のようにモニタに提示された健常者の動きを脳血管障害者がまねようと同じ動きをするときにMicrosoft Kinect (登録商標)を用いてリアルタイムに3次元座標を測定し特徴点を比較することで、健常者に近づく方向を音声、画面表示で教示し、脳血管障害者は健常者の動きに限りなく近づけるように努力する。教示する動作を複数用意することで回復段階を判断する。
回復段階を判断する複数の動作は、医師や理学療法士、作業療法士が脳血管障害の回復段階を判断する指標としているBrunnstrom testや上田敏12段階片麻痺グレード総合判定で使用する12項目の評価動作を用い、各動作の完成度によって回復段階を判定する。
12項目の評価動作は以下の通りである。
(1)背臥位で患側の手を反対側の腰の辺に伸ばす。
(2)患手の先を健側の腰から患側の耳まで持っていく
(3)腰の後方へ手をつける
(4)肘を伸展させて上肢を前方水平へ挙上
(5)肘90°屈曲位での前腕回内動作
(6)肘90°屈曲位での前腕回外動作
(7)肘を伸展させて上肢を横水平へ挙上
(8)肘を伸展させて上肢を前方頭上へ挙上
(9)肘伸展位での前腕回内動作
(10)肘伸展位での前腕回外動作
(11)出来るだけ早く手を肩から頭上に挙上する
(12)出来るだけ早く腕を横水平位に挙上する
*(2)から(12)の動作はすべで腰掛け位で行う。
各動作の完成度に基づく脳血管障害者の判定過程は図2で示している。
FIG. 1 is a principle diagram for determining the recovery stage of a cerebrovascular disorder person. As shown in the figure, when the movement of the healthy person presented on the monitor is the same as that of a person with cerebrovascular disorder, measure the 3D coordinates in real time using Microsoft Kinect (registered trademark) and compare the feature points Thus, the direction of approaching the healthy person is taught by voice and screen display, and the person with cerebrovascular disorder strives to approach the movement of the healthy person as much as possible. The recovery stage is determined by preparing a plurality of teaching operations.
The multiple actions to determine the recovery stage are the 12 items used in the Brunnstrom test and Satoshi Ueda 12-step hemiplegia grade overall judgment, which are used as an index for doctors, physical therapists, and occupational therapists to determine the recovery stage of cerebrovascular disorders. Using the evaluation operation, the recovery stage is determined by the completeness of each operation.
The evaluation operations for the 12 items are as follows.
(1) In the supine position, extend the affected hand to the waist on the opposite side.
(2) Take the tip of the affected hand from the waist on the healthy side to the ear on the affected side (3) Place the hand behind the waist (4) Extend the elbow and raise the upper limb forward horizontally (5) Elbow 90 ° Forearm pronation in flexion position (6) Elbow 90 ° forearm pronation movement in flexion position (7) Raise the elbow and raise the upper limb horizontally (8) Extend the elbow and move the upper limb forward (9) Forearm pronation in elbow extension position (10) Forearm pronation in elbow extension position (11) Raise hand from shoulder to head as soon as possible (12) Move arm as soon as possible Raise horizontally
* (2) to (12) are all performed in a sitting position.
The determination process of the cerebrovascular disorder person based on the completeness of each operation is shown in FIG.
医療分野において脳血管障害者の回復段階を判定するためには医師や理学療法士、作業療法士といったリハビリテーション専門職の高度な技術が必要である。Microsoft Kinect (登録商標)の観測情報に基づいて脳血管障害者の回復段階を判定する技術は、リハビリテーション専門職が不足している地域において高い利用可能性がある。また、Microsoft Kinect (登録商標)の観測情報を使うことにより再現性の高い評価が可能となり医療の質、効率の向上に有用である。 In order to determine the recovery stage of patients with cerebrovascular disorders in the medical field, advanced techniques of rehabilitation professionals such as doctors, physical therapists, and occupational therapists are necessary. The technology for determining the recovery stage of patients with cerebrovascular disorders based on the observation information of Microsoft Kinect (registered trademark) has high potential in areas where there is a lack of rehabilitation professionals. Also, by using observation information of Microsoft Kinect (registered trademark), highly reproducible evaluation becomes possible, which is useful for improving medical quality and efficiency.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919983A (en) * | 2019-03-16 | 2019-06-21 | 哈尔滨理工大学 | A Kalman Filter for Kinect Doctor Perspective Tracking |
| CN115346664A (en) * | 2022-08-30 | 2022-11-15 | 青岛大学 | Facial paralysis diagnosis and rating method and system based on deep learning |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012039467A1 (en) * | 2010-09-22 | 2012-03-29 | パナソニック株式会社 | Exercise assistance system |
| WO2013022890A1 (en) * | 2011-08-08 | 2013-02-14 | Gary And Mary West Wireless Health Institute | Systems, apparatus and methods for non-invasive motion tracking to augment patient administered physical rehabilitation |
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- 2014-04-09 JP JP2014080432A patent/JP2015198834A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012039467A1 (en) * | 2010-09-22 | 2012-03-29 | パナソニック株式会社 | Exercise assistance system |
| WO2013022890A1 (en) * | 2011-08-08 | 2013-02-14 | Gary And Mary West Wireless Health Institute | Systems, apparatus and methods for non-invasive motion tracking to augment patient administered physical rehabilitation |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109919983A (en) * | 2019-03-16 | 2019-06-21 | 哈尔滨理工大学 | A Kalman Filter for Kinect Doctor Perspective Tracking |
| CN109919983B (en) * | 2019-03-16 | 2021-05-14 | 哈尔滨理工大学 | Kinect doctor visual angle tracking-oriented Kalman filter |
| CN115346664A (en) * | 2022-08-30 | 2022-11-15 | 青岛大学 | Facial paralysis diagnosis and rating method and system based on deep learning |
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