JP2004049608A - Exercise and learning support system, exercise and learning support method and exercise and learning support program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an exercise and learning support system which makes learn the hardness of an actuation part to mainly contribute action in each part of a living body, and also provide an exercise and learning support method, and furhter provide an exercise and learning support program. <P>SOLUTION: The exercise and learning support system 10 is constituted of a display device 25 for indicating a degree of impedance of the actuation part when a standarized action is carried out, and indicates it as a target value for the exercise and learning the impedance of the actuation part of the living body when the standarized action is carried out. <P>COPYRIGHT: (C)2004,JPO

Description

【００１９】
この各平滑筋電信号は、それぞれＬＰＦ３４−１〜３４−１０に入力される。結局、２００Ｈｚでサンプリングされたことになる。ＬＰＦ３４−１〜３４−１０は、各平滑筋電信号を擬似張力にそれぞれ変換して出力する。擬似張力は、各筋肉が発生する張力に対応する値である。ＬＰＦ３４−１〜３４−１０は、２次系のフィルタであり、その遮断周波数は、筋肉が実際に発生している張力と擬似張力との対応を正確にする観点から、数Ｈｚ、より好ましくは１Ｈｚ〜３Ｈｚ、さらにより好ましくは２Ｈｚ〜３Ｈｚに設定される。本実施形態では２Ｈｚに設定された。
【００２０】
随意運動では、上位の中枢から伝達されるインパルスが脊髄のα運動ニューロンを介して各筋肉に伝達され、活動電位が発生して筋肉が収縮し、各関節にトルクを生じさせて所望の動作が起こる。筋電信号を低域通過フィルタで変換した出力信号である擬似張力は、α運動ニューロンの発火頻度を反映していると期待されるため、筋肉が実際に生じている張力と一定の対応関係があると考えられている。時刻ｎにおける擬似張力Ｔ（ｎ）は、式２で与えられる。
【００２１】
【数２】

【００２２】
これは、Ｗｉ　をインパルス応答とするＦＩＲフィルタになっている。本実施形態では、インパルス応答ｈ（ｔ）を式３のようにおいた。
【００２３】
【数３】

【００２４】
ＬＰＦ３４−１〜３４−１０の出力信号（擬似張力）は、ＣＰＵ３５に出力される。ＣＰＵ３５は、正規化基準値演算部４１、係数行列演算部４２、筋活性化レベル演算部４３、同時活性化レベル演算部４４及び運動学習目標値設定部４５を備えて構成され、例えば、マイクロプロセッサなどである。正規化基準値演算部４１、係数行列演算部４２、筋活性化レベル演算部４３及び同時活性化レベル演算部４４は、正規化基準値、係数行列、筋活性化レベル及び同時活性化レベルをそれぞれ後述のように演算し、運動学習目標値設定部４５は、後述のように、基準とする動作を行った場合における動作部分の硬さを運動学習目標値として設定する。さらに、ＣＰＵ３５は、各種プログラムに従い、データを演算処理し、６軸力覚センサ２３、外部記憶装置２４、表示装置２５、記録装置２６、入力装置２７、ＤＩＶ３２及びメモリ３６を制御すると共にデータを交換する。メモリ３６は、ＣＰＵ３５に接続され、ＲＡＭ（Ｒａｎｄｏｍ　Ａｃｃｅｓｓ　ｍｅｍｏｒｙ）及びＲＯＭ（ＲｅａｄＯｎｌｙ　Ｍｅｍｏｒｙ）を備えて構成される。メモリ３６は、正規化基準値を計測するための正規化基準値計測プログラム、係数行列を演算するための係数行列演算プログラム、運動学習目標値を入力するための運動学習目標値入力プログラム、運動学習を支援するための運動学習プログラム及び各装置を制御するための制御プログラムなど各種のプログラム、これらプログラム実行中の各種値、表面電極２１−１〜２１−１０　からの出力並びに６軸力覚センサ２３からの出力などを格納する。
【００２５】
６軸力覚センサ２３は、棒状の握部２３−１が基部２３−２に固定的に突設され、握部２３−１の底部に備えられた圧電素子によって握部２３−１に加えられた力が検出される。６軸力覚センサ２３は、高さを調節することができる作業台２８に配置される。外部記憶装置２４は、フレキシブルディスク、ＣＤ−Ｒ（Ｃｏｍｐａｃｔ　Ｄｉｓｃ　Ｒｅｃｏｒｄａｂｌｅ）及びＤＶＤ−Ｒ（Ｄｉｇｉｔａｌ　Ｖｅｒｓａｔｉｌｅ　Ｄｉｓｃ　Ｒｅｃｏｒｄａｂｌｅ）などの記憶媒体とデータを読み書きする補助記憶装置であり、例えば、フレキシブルディスクドライブ、ＣＤ−Ｒドライブ及びＤＶＤ−Ｒドライブなどである。メモリ３６に運動学習支援装置１０を動作させるために必要な正規化基準値計測プログラム、係数行列演算プログラム、運動学習目標値入力プログラム及び運動学習プログラムなどの各プログラムが格納されていない場合には、これらを記録した記録媒体を外部記憶装置２４を介してメモリ３６に読み込む。
【００２６】
表示装置２５は、ＣＲＴやＬＣＤなどであり、ＬＰＦ３４−１〜３４−１０の出力信号（擬似張力）、６軸力覚センサ２３の出力信号及び入力装置２７の出力信号などのＣＰＵ３５の処理結果を表示する。記録装置２６は、紙などの媒体にデータを記憶するプリンタなどの出力機器であり、ＬＰＦ３４−１〜３４−１０の出力信号（擬似張力）、６軸力覚センサ２３の出力信号及び入力装置２７の出力信号などのＣＰＵ３５の処理結果を記録する。そして、入力装置２７は、起動プログラムの指定など、ＣＰＵ３５に与える各種コマンドやデータなどを入力する装置であり、例えば、キーボードやマウスなどである。
【００２７】
（本実施形態の動作）
運動学習支援装置１０は、正規化基準値計測モード、係数行列演算モード、運動学習目標値入力モード及び運動学習モードの各モードが備えられ、これら各モードを使用することにより、学習者は、動作部分の硬さの程度を含めて動作を習得することができる。以下、各モードについて説明する。
【００２８】
（正規化基準値計測モード）
本正規化基準値計測モードは、正規化基準値を計測する場合に使用されるモードである。
【００２９】
動物の運動は、運動神経に支配される筋肉の収縮によって生じ、多くの骨格筋の協調によって行われる。運動神経は、多数の運動神経繊維の束からなり、個々の運動神経繊維は、数本から１００本以上の筋繊維を支配している。１本の運動神経繊維の支配下にある筋繊維は、神経繊維のインパルスにより同時に活動電位を発生して収縮するので、骨格筋の運動の単位と見なすことができ、運動単位と呼ばれる。骨格筋が生体内にある状態でその活動電位を示すものに筋電信号がある。筋電信号は、筋肉が全く弛緩している場合では活動電位は全く記録されず、運動神経のインパルスにより筋肉の収縮が起こると活動電位があらわれ記録される。活動電位の頻度は、収縮が強くなるにつれて増大するが、これは、単一の運動単位に伝えられる神経インパルス頻度の増大や、収縮にあずかる運動単位の数の増加によるものである。このような筋電信号は、観察される電極の電位と筋肉の張力のレベルとに直接的な対応関係がない。即ち、筋電信号から得られる擬似張力においても、擬似張力のレベルと筋肉の張力のレベルとの間に直接的な対応関係はない。そのため、擬似張力の或るレベルが筋肉の張力の何れのレベルに対応するか関係付けるために、正規化基準値の計測を行い、この結果によって、ＤＩＶ３２−１〜３２−１０における所定の値を決定する。
【００３０】
正規化方法は、最大随意収縮力を１として相対的に正規化する方法がある。この方法は、最大随意収縮力が被験者の最大であると考えて出す筋肉の張力であるので被験者の恣意によると考えられ、同じ筋肉であっても時々で相違してしまう。以下の正規化方法は、被験者の恣意によらない点で優れている。
【００３１】
筋電信号の正規化は、第１ステップとして、動作部分に所定の力を発生させ、この場合における各筋肉の筋電信号を表面電極２１−１〜２１−１０　によって計測する。第２ステップとして、各筋肉毎に擬似張力の最大値を求める。この最大値が、各筋肉毎に求められた、各筋肉の筋電信号を正規化するための正規化基準値となる。そして、この正規化基準値に応じてＤＩＶ３２−１〜３２−１０における所定の値が決定される。
【００３２】
次に、より具体的に説明するが、ゴルフパッドの成否は、両腕の硬さによるので、左腕及び右腕について正規化基準値を計測する必要がある。両腕の正規化基準値は、それぞれ同様に計測されるので、以下に左腕の場合について説明し、右腕の場合についてはその説明を省略する。
【００３３】
図２は、肘関節角度１８０度・肩関節角度９０度の状態で手首関節に対する正規化データ取得の状況を示す図である。図３は、表面電極のだいたいの装着位置と筋肉との関係を示す図である。図３（ａ）は左前腕の腹側であり、図３（ｂ）は左前腕の背側である。
【００３４】
図１及び図２（ａ）において、運動学習支援装置１０を利用して動作を学習しようとする学習者は、椅子２９に座り、腕を肩と水平な状態になるようにして左腕前腕部を作業台２８に固定する。本実施形態における筋電信号を計測する前腕内部には、深指屈筋などのように指の運動に関わる筋肉も存在するのでこれらの筋肉の影響を避けるため、熱可塑性の素材を用いて掌の型を取ってそれを用い、握部２３−１の上部に親指が掛かる状態で６軸力覚センサ２３に掌を固定した。これによって手指に余分な力を加えることなく、手首関節に生じるトルクを６軸力覚センサ２３で計測することができる。
【００３５】
複数の表面電極２１−１〜２１−５は、図３に示すように手首関節の伸筋・屈筋として、左前腕の▲１▼橈側手根屈筋、▲２▼尺側手根伸筋、▲３▼指伸筋、▲４▼尺側手根屈筋及び▲５▼方形回内筋の５個の筋肉で生じる筋電信号をそれぞれ測定可能なように装着される。医学において一般に知られていることであるが、橈側手根屈筋は、肘関節における屈筋及び回内筋としての働きは弱いが、手根の関節における掌側屈曲の場合や長橈側手根伸筋と協働して橈側偏位の場合に主に働く。尺側手根伸筋は、外転筋であり、手の尺側変位の場合に主に働く。指伸筋は、指の伸展及び手首の伸展の補助として主に働く。尺側手根屈筋は、掌側屈曲及び手の尺側偏位の場合に主に働く。方形回内筋は、前腕を回内する場合に主に働く。回内及び回外は、回旋運動であって、橈骨頭関節窩から尺骨の茎状突起に至る軸のまわりで行われる。
【００３６】
学習者は、まず、入力装置２７を用いて正規化基準値計測プログラムを起動させ、ＣＰＵ３５は、主に正規化基準値演算部４１を用いて正規化基準値計測プログラムを実行する。学習者は、上述の体位及び装着状態で、表示装置２５の画面を参照しながら、等尺性収縮により手先に力を発生させる。
【００３７】
学習者がこのように手先に力を発生させている間、表面電極２１−１〜２１−５からの出力は、上述の信号処理により擬似張力に変換されて、ＣＰＵ３５に取り込まれる。この正規化基準値の計測の場合では、ＤＩＶ３２−１〜３２−５の所定の値は、１に設定される。即ち、ＤＩＶ３２−１〜３２−５は、入力をそのまま出力する。そして、６軸力覚センサ２３からの出力もＣＰＵ３５に取り込まれる。ＣＰＵ３５は、これら取り込んだ値をメモリ３６に格納するとともに記録装置２６に記録し、６軸力覚センサ２３からの出力を表示装置２５に表示する。
【００３８】
表示装置２５は、６軸力覚センサ２３に加わる力が５Ｎであることを示す円と、学習者が６軸力覚センサ２３に加えている現在の力の方向及び大きさを示すカーソルと、３０度ずつの１２方向の矢印とを表示し、学習者は、表示装置２５を見ながら所定方向にこの円上にカーソルが一致するように力を発生させる。これにより学習者は、学習者の恣意によらない所定の力、即ち、５Ｎの力を発生させる。
【００３９】
測定は、すべての筋肉を活性化させるために図２（ｂ）に示す正のＸ方向（嘗屈方向）から３０度ずつ１２方向に力を発生させように、２４秒間で反時計回りに一周するように行われた。ここで、次の方向に力の発生方向を変えるごとに力を抜き０Ｎに戻すようにしている。これは、学習者に５Ｎ以上の力を発生させないようにするためである。なお、測定開始の方向は、特に限定されるものではなく、何れの方向から始めても良い。そして、６軸力覚センサ２３から手首関節までの距離を乗算して橈屈・尺屈方向及び嘗屈・背屈方向の筋電信号を計測する。
【００４０】
ＣＰＵ３５は、取り込まれた擬似張力から、筋肉毎に擬似張力の最大値を求める。この擬似張力の最大値が正規化基準値である。ＣＰＵ３５は、筋肉毎に対応する正規化基準値を表示装置２５に表示するとともに記録装置２６に記録する。そして、ＣＰＵ３５は、このように求めた各正規化基準値を、ＤＩＶ３２−１〜３２−５の所定の値として、各筋肉に対応させてそれぞれに設定する。これによって、表面電極２１−１〜２１−５から得られる擬似張力（筋電信号）と筋肉の張力との間に、一定の対応関係を作ることができる。
【００４１】
さらに、表面電極２１−６〜２１−１０などを用いて、右前腕について同様に正規化基準値を計測し、ＤＩＶ３２−６〜３２−１０における所定の値が各筋肉に対応させてそれぞれ設定される。
【００４２】
このような正規化基準値で筋電信号を正規化するので、装着状態の相違による抵抗値の相違を吸収することができる。被験者の恣意によらないので被験者間で擬似張力及び関節のトルクなどを比較することが可能となる。なお、本実施形態では、所定の力として５Ｎの場合において正規化基準値を求めたが、他の力、例えば３Ｎや７Ｎなどの力を発生させて正規化基準値を求めてもよい。
【００４３】
（トルク・擬似張力関係式の演算・・・係数行列ａの算出）
正規化基準値が算出されると、ＣＰＵ３５は、メモリ３６に格納されている係数行列演算プログラムを起動し、主に係数行列演算部４２を用いて係数行列演算プログラムを実行する。これにより運動学習支援装置１０は、係数行列演算モードになる。係数行列演算モードは、擬似張力Ｔとトルクτとに基づいて係数行列ａを演算するモードである。
【００４４】
一般に、或る自由度の関節トルクτは、その方向に関してのモーメントアームａと張力Ｔとの積をすべての筋肉について足し合わせたものになり、式４のように表される。
τ＝ａＴ　　　　　　　　　　　　　　　　・・・　（式４）
ここで、τ＝（τ１、τ２、・・・、τｊ）^ｔ　　　　　　　　・・・　（式５−１）
Ｔ＝（Ｔ１、Ｔ２、・・・、Ｔｉ）^ｔ　　　　　　・・・　（式５−２）
及びａはｊ行ｉ列の行列であり、ｉ，ｊは、関節にトルクを生じさせる各筋肉に付された番号である。例えば、手首関節トルクの場合には、前述の▲１▼〜▲５▼である。
【００４５】
モーメントアームａは、一般に関節回転軸と筋肉作用線との距離を指すが、パッティング動作の場合には、手首関節は大きく変化しないと考えられる。このため、モーメントアームａは、一定とみなすことができ、筋肉は、等尺性収縮を行うものと考えられる。そのため、モーメントアームａは、等尺性収縮に対する寄与度と考えられるので、このような場合にはモーメントアームａを係数行列と呼称することとする。
【００４６】
擬似張力Ｔ、トルクτ及び係数行列ａの間における式４、式５−１及び式５−２の関係から、ＣＰＵ３５は、メモリ３６に格納されているＬＰＦ３４−１〜３４−１０の出力に各正規化基準値を考慮した上で、擬似張力Ｔとトルクτとに基づいて最小自乗法により係数行列ａを演算する。なお、ＤＩＶ３２−１〜３２−１０に正規化基準値を設定し、学習者が上述の正規化基準値計測と同様な動作を行い、係数行列ａを求めるに必要な擬似張力Ｔとトルクτとのデータを得てもよい。
【００４７】
ここで、図４は、力の測定結果及び手首関節のトルクを示す図である。図４（ａ）は左手首関節における、時系列で表示した背屈・嘗屈方向のトルクを示し、図４（ｂ）は左手首関節における、時系列で表示した尺屈・橈屈方向のトルクを示し、図４（ｃ）は右手首関節における、時系列で表示した背屈・嘗屈方向のトルクを示し、そして、図４（ｄ）は右手首関節における、時系列で表示した尺屈・橈屈方向のトルクを示す。これらの縦軸は、Ｎｍ単位で示すトルクであり、横軸は、秒単位で示す測定経過時間である。また、図４における、約２秒ごとにのあらわれるピークは、１２方向のうちの一方向に相当し、最初のピークは、左手首関節の場合では図２（ｂ）の正のＸ方向（嘗屈方向）に等尺性収縮により手先に力を発生させた場合であり、右手首関節の場合では図２（ｂ）の正のＸ方向（背屈方向）に等尺性収縮により手先に力を発生させた場合である。そして、破線は６軸力覚センサ２３で測定される実測値であり、実線は式１から求めた演算値である。図４に示すように、実線と破線は、よく一致しており、係数行列ａの的確性が検証された。
【００４８】
（運動学習目標値の入力）
係数行列が算出されると、ＣＰＵ３５は、メモリ３６に格納されている運動学習目標値入力プログラムを起動し、主に筋活性化レベル演算部４３、同時活性化レベル演算部４４及び運動学習目標値設定部４５を用いて運動学習目標値入力プログラムを実行する。これにより運動学習支援装置１０は、運動学習目標値入力モードになる。運動学習目標値入力モードは、目的動作の基準となる動作を行った場合における、目的動作に係る動作部分の硬さを決める各筋肉の筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを入力するモードである。例えば、目的動作がゴルフパッドである場合では、ゴルフボールがカップに入った場合におけるパッティング動作中の筋活性化レベル及び同時活性化レベルである。また例えば、目的動作が陶器の製作の場合では、ろくろを用いて形を崩すことなしに器を形成することができた場合における手の筋活性化レベル及び同時活性化レベルである。
【００４９】
まず、学習者は、目的動作に係る動作部分の硬さを決める各筋肉の筋電信号を測定することができるように、複数の表面電極２１を装着し、目的動作を実行する。例えば、ゴルフパッドを学習しようとする場合には、図３に示す動作部分の各箇所に表面電極２１−１〜２１−１０を装着し、所定のパッティング動作を行う。
【００５０】
ＣＰＵ３５は、学習者が運動中の擬似張力を表面電極２１−１〜２１−１０、ＤＩＦＡ３１−１〜３１−１０、ＤＩＶ３２−１〜３２−１０、ＦＷＲＣ３３−１〜３３−１０及びＬＰＦ３４−１〜３４−１０を介して取り込む。次に、ＣＰＵ３５は、式６に従い筋活性化レベルＭＣＬを算出し、式７に従い同時活性化レベルＣＣＬを算出する。
【００５１】
【数４】

【００５２】
【数５】

【００５３】
ここで、式６及び式７は、等尺性収縮の場合では、動作部分の硬さの変化が筋活性化レベルの変化に対応すると考えられることに基づくものである。
【００５４】
次に、ＣＰＵ３５は、算出した筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを表示装置２５に表示する。学習者は、今の目的動作を基準とする場合には、入力装置を用いてその旨を入力する。例えば、ゴルフパッドの場合には、ゴルフボールがカップに入ってパッティング動作が成功した場合に、学習者は、入力装置２７を用いて今の動作を基準とする旨を入力する。ＣＰＵ３５は、今の目的動作が基準動作である旨の信号を受信すると、算出した筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを運動学習目標値としてメモリ３６に格納する。一方、或る一定期間内にこの信号を受信しなかった場合には、次の目的動作を学習者に促すメッセージを表示装置２５に表示する。
【００５５】
学習者は、このメッセージを参照して新たに目的動作を行い、ＣＰＵ３５は、上述と同様の動作により筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを算出して表示装置２５に表示する。ＣＰＵ３５は、学習者が目的動作を基準動作とする旨を入力装置２７から入力するまでこれらの動作を繰り返す。このようにして運動学習支援装置１０は、運動学習目標値が入力される。
【００５６】
ここで、上述では運動学習目標値は、学習者自身が目的動作を行って運動学習支援装置１０に入力したが、目的動作に熟達した熟達者に目的動作を行わせて運動学習支援装置１０に入力するようにしてもよい。また、他の運動学習支援装置１０を用いて熟達者による運動学習目標値を取得し、これを記録した記録媒体から外部記憶装置２４を介して学習者が使用する運動学習支援装置１０に入力するようにしてもよい。特に、日舞、能、人形浄瑠璃などの芸能や大工、左官などの生産技能などのような目的動作の成否が明瞭に識別できない場合には、熟練者によって運動学習目標値を入力又は取得することは有効である。これによって芸能や匠の業が保存可能となると共に後述の学習により後世に伝えることもできる。
【００５７】
このように運動学習支援装置１０は、自己の成功した場合の動作や熟練者を手本とする場合でも初級レベル、中級レベル及び上級レベルの熟練者の各動作などのように、基準となる動作を様々なレベルに設定することができ、学習者は、学習レベルに合わせて運動学習目標値を設定することができる。
【００５８】
（運動学習）
運動学習目標値が入力されると、ＣＰＵ３５は、メモリ３６に格納されている運動学習プログラムを起動し、主に筋活性化レベル演算部４３及び同時活性化レベル演算部４４を用いて運動学習プログラムを実行する。これにより運動学習支援装置１０は、運動学習モードになる。運動学習モードは、運動学習目標値を用いて学習者が目的動作を学習し、習得するモードである。
【００５９】
まず、ＣＰＵ３５は、表示装置２５に目的動作に係る運動学習目標値を表示する。学習者は、目的動作に係る動作部分の硬さを決める各筋肉の筋電信号を測定することができるように、複数の表面電極２１を装着する。なお、表面電極２１を容易・正確に装着することができるようにする観点から、目的動作に対応する表面電極２１の装着位置をメモリ３６に格納し、ＣＰＵ３５は、メモリ３６を参照することによってこの装着位置を表示装置２５に表示するようにしてもよい。次に、学習者は、表示装置２５に表示された運動学習目標値を参照しながら、目的動作を実行する。
【００６０】
ＣＰＵ３５は、学習者が動作中の擬似張力を表面電極２１−１〜２１−１０、ＤＩＦＡ３１−１〜３１−１０、ＤＩＶ３２−１〜３２−１０、ＦＷＲＣ３３−１〜３３−１０及びＬＰＦ３４−１〜３４−１０を介して取り込む。次に、ＣＰＵ３５は、式６に従い筋活性化レベルＭＣＬを算出し、式７に従い同時活性化レベルＣＣＬを算出する。
【００６１】
図５は、運動学習モードにおける表示装置の表示画面の一例を示す図である。図５において、左側表示領域５０−１に各筋肉の筋活性化レベルＭＣＬ５１−Ａ〜５１−Ｊ及びその運動学習目標値５２−Ａ〜５２−Ｊを表示し、右側表示領域５０−２に各前腕の同時活性化レベルＣＣＬ５５−Ｌ、５５−Ｒ及び運動学習目標値５６−Ｌ、５６−Ｒを表示する。横軸は、筋活性化レベルＭＣＬの場合では筋活性化レベルＭＣＬが何れの筋肉Ａ〜Ｊであるかを示し、同時活性化レベルＣＣＬの場合では同時活性化レベルＣＣＬが左前腕Ｌか右前腕Ｒであるかを示す。左縦軸はＭＣＬのレベルを示し、右縦軸はＣＣＬのレベルを示す。
【００６２】
次に、ＣＰＵ３５は、図５に示すように、算出した筋活性化レベルＭＣＬ５１−Ａ〜５１−Ｊ及び同時活性化レベルＣＣＬ５５−Ｌ、５５−Ｒをメモリ３６内の運動学習目標値５２−Ａ〜５２−Ｊ、５６−Ｌ、５６−Ｒと共に表示装置２５に表示する。
【００６３】
学習者は、今行った目的動作に関する筋活性化レベルＭＣＬ５１−Ａ〜５１−Ｊ及び同時活性化レベルＣＣＬ５５−Ｌ、５５−Ｒと運動学習目標値の筋活性化レベルＭＣＬ５２−Ａ〜５２−Ｊ及び同時活性化レベルＣＣＬ５６−Ｌ、５６−Ｒとの差をそれぞれ見比べることによって、今行った目的動作と成功する場合の目的動作との差を認識することができる。学習者は、学習を終了する場合にはその旨を入力装置２７を用いて入力する。ＣＰＵ３５は、学習を終了する旨の信号を受信すると、運動学習プログラムを終了し、或る一定期間内にこの信号を受信しなかった場合には、次の学習を学習者に促すメッセージを表示装置２５に表示する。
【００６４】
このメッセージを見た後に、学習者は、今行った目的動作に関する筋活性化レベルＭＣＬ５１−Ａ〜５１−Ｊ及び同時活性化レベルＣＣＬ５５−Ｌ、５５−Ｒと運動学習目標値の筋活性化レベルＭＣＬ５２−Ａ〜５２−Ｊ及び同時活性化レベルＣＣＬ５６−Ｌ、５６−Ｒとの差を参考に何れの生体部位に力を入れるかを決定し、新たに目的動作を行って学習を進める。なお、筋活性化レベルと筋肉との対応関係が容易に分かるようにこれを表示装置２５に表示するようにしてもよい。例えば、図３に示すような各筋肉の位置を示した動作部分のイラストを表示し、左側表示領域の横軸に表示された何れの筋肉であるかを示す符号Ａ〜Ｊをイラスト中の各筋肉に対応付けて表示する。
【００６５】
ＣＰＵ３５は、上述と同様の動作により筋活性化レベルＭＣＬ５１−Ａ〜５１−Ｊ及び同時活性化レベルＣＣＬ５５−Ｌ、５５−Ｒを算出して運動学習目標値５２−Ａ〜５２−Ｊ、５６−Ｌ、５６−Ｒと共に表示装置２５に表示する。ＣＰＵ３５は、学習者が学習を終了する旨を入力装置２７から入力するまでこれらの動作を繰り返す。このようにして学習者は、運動学習支援装置１０を用いて動作部分の硬さの程度を含めて目的動作を学習することができる。
【００６６】
特に、本発明に係る運動学習支援装置１０では、今行った目的動作の同時活性化レベルＣＣＬと運動学習目標値の同時活性化レベルＣＣＬとが表示装置２５に表示され視覚化されるので、学習者は、両同時活性化レベルＣＣＬの差を認識することができ、動作部分が運動学習目標値に達する場合よりも硬いのか軟らかいのかを判断することができる。そして、本発明に係る運動学習支援装置１０では、今行った目的動作の筋活性化レベルＭＣＬと運動学習目標値の筋活性化レベルＭＣＬとも表示装置２５に表示され視覚化されるので、学習者は、各筋肉別に両筋活性化レベルＭＣＬの差を認識することができ、どの筋肉にどの程度の力を入れるべきかを判断することができる。これによって、学習者は、目的動作に達しなかった原因を知ることができ、本発明に係る運動学習支援装置１０を使用しない場合よりも短期間に目的動作を習得することができる。
【００６７】
なお、今行った目的動作に関する筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬと運動学習目標値の各値（ＭＣＬ、ＣＣＬ）との差を容易に学習者に認識させ得る観点から、差を数値表示するようにしてもよい。
【００６８】
そして、今行った目的動作に関する筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬと運動学習目標値の各値（ＭＣＬ、ＣＣＬ）との差を容易に学習者に認識させ得る観点から、差に応じて表示色を変えてもよい。例えば、図５に示すように運動学習目標値の各値（ＭＣＬ、ＣＣＬ）をバー５２、５６で表示し、今行った目的動作に関する筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを棒グラフ５１、５５で表示する場合に、今行った目的動作に関する筋活性化レベルＭＣＬが運動学習目標値の筋活性化レベルＭＣＬに対して３０％未満、３０％以上６０％未満、６０％以上９０％未満、９０％以上１１０％未満、１１０％以上１４０％未満、１４０％以上１７０％未満及び１７０％以上である場合には、それぞれ棒グラフ５１、５６を赤、橙、黄、緑、黄、橙及び赤のように色分け表示する。
【００６９】
また、初心者の学習を便宜を図り即修させる観点から、目的動作を成功させるためにはどの筋肉に力を最も強く入れどの筋肉の力を最も抜くかを目的動作別に対応テーブルを作成してメモリ３６に格納し、初心者が学習を開始する場合にＣＰＵ３５がこの対応テーブルを参照して、表示装置２５に表示させるようにしてもよい。あるいは、目的動作が複数の動作部分を運動させることによって行われる場合に、目的動作を成功させるためにはどの動作部分を最も硬くさせどの動作部分を最も軟らかくさせるかを目的動作別に対応テーブルを作成してメモリ３６に格納し、初心者が学習を開始する場合にＣＰＵ３５がこの対応テーブルを参照して、表示装置２５に表示させるようにしてもよい。例えば、ゴルフパッドの場合には、右利きの場合に“右腕よりも左腕の方を硬く握ってください”のメッセージを表示装置２５に表示させるようにする。
【００７０】
そして、本実施形態では、係数行列演算プログラム、運動学習目標値入力プログラム及び運動学習プログラムを自動起動するようにしたが、学習者が入力装置２７を用いてこれら各プログラムを起動させるようにしてもよい。
【００７１】
また、本実施形態では、今行った目的動作に関する筋活性化レベルＭＣＬや同時活性化レベル及び運動学習目標値ＣＣＬなどを表示装置２５によって学習者に提示するようにしたが、提示手段として運動学習支援装置１０にスピーカやブザーなどの音源を表示装置２５に代えて又は表示装置２５にさらに加えて、音によって知らせるようにしてもよい。このような音による場合では、今行った目的動作に関する筋活性化レベルＭＣＬとこれの運動学習目標値との差や同時活性化レベルＣＣＬとこれの運動学習目標値との差は、例えば、音色や音の長短などによって示すようにすればよい。あるいは、提示手段として運動学習支援装置１０に触覚刺激（電気刺激を含む）を行う刺激装置を表示装置２５に代えて又は表示装置２５にさらに加えて、触覚によって知らせるようにしてもよい。このような刺激による場合では、今行った目的動作に関する筋活性化レベルＭＣＬとこれの運動学習目標値との差や同時活性化レベルＣＣＬとこれの運動学習目標値との差は、例えば、刺激の種類や長短などによって示すようにすればよい。このように提示手段は、人間の知覚を利用するものでよい。
【００７２】
さらに、本実施形態では、運動学習目標値を予め測定してそれを記録・表示するようにしたが、図１（ｂ）に示す表面電極２１、ＤＩＦＡ３１、ＤＩＶ３２、ＦＷＲＣ３３及びＬＰＦ３４からなる擬似張力測定部を２組用意して、教師となる熟練者及び学習者の筋電信号を同時に測定して、学習者の筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬと教師の筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬとの差を表示装置２５に表示するようにしてもよい。
【００７３】
そして、本実施形態では、筋電信号は、表面誘導法により測定されたが、これに限定されるものではない。活動電位を計測できる方法であれば、何でもよい。例えば、針電極法でもよい。針電極法は、針状の電極を筋肉に刺入して筋肉局部の活動電位を記録する方法である。
【００７４】
（比較実験結果）
次に、本発明に係る運動学習支援装置１０の学習効果を検証するために以下の実験を行った。実験は、人工芝上でカップに見立てた半径８ｃｍの目標円内にゴルフボールを到達させるゴルフパッドの学習について行った。ゴルフボールを置いた位置から目標円中心までの距離が１ｍ及び２ｍのパッティングについて行った。学習者は、ゴルフ初心者である２名の青年男性Ａ，Ｂである。
【００７５】
まず、比較対象実験として、学習者Ａ，Ｂは、表示装置２５の表示画面を見ることができない状態でパッティング動作を行い、各腕の同時活性化レベルＣＣＬとパッティング誤差とを測定した。
【００７６】
図６乃至図９は、それぞれ、学習者Ａ及びＢが１ｍ及び２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルＣＣＬとを示す図である。これら各図において、（ａ）は各パッティングにおけるパッティング誤差を示し、横軸はパッティングの回数であり縦軸は目標円中心からの距離（ｃｍ）である。（ｂ）は、左前腕の同時活性化レベルＣＣＬを示し、（ｃ）は、右前腕の同時活性化レベルＣＣＬを示す。（ｂ）、（ｃ）における横軸はパッティングの試行回数であり縦軸はレベルである。
【００７７】
図６乃至図９を見ると分かるように、目標円内にゴルフボールが到達し、パッティングが成功する場合もあるが、その成功頻度は、パッティングの回数が増えるに従って顕著に大きくなることはなく、比較対照実験中にほぼ同じ頻度である。また、各腕の同時活性化レベルＣＣＬもパッティングの回数が増えるに従って或る一定値に収束する傾向はなく、ランダムに推移している。
【００７８】
次に、本発明に係る運動学習支援装置１０を使用した実験として、学習者Ａ，Ｂは、始め表示装置２５の表示画面を見ることができない状態でパッティング動作を行い、途中から表示装置２５の表示画面を見ながらパッティング動作を行い、各腕の同時活性化レベルＣＣＬとパッティング誤差とを測定した。
【００７９】
図１０、図１２、図１４及び図１６は、それぞれ、学習者Ａ及びＢが１ｍ及び２ｍのパッティングを行った場合における各筋肉の筋活性化レベルＭＣＬを示す図である。これら各図において、１は左腕の橈屈手根屈筋の筋活性化レベルＭＣＬ１であり、２は左腕の尺側手根伸筋の筋活性化レベルＭＣＬ２であり、３は左腕の指伸筋の筋活性化レベルＭＣＬ３であり、４は左腕の尺側手根屈筋の筋活性化レベルＭＣＬ４であり、５は左腕の方形回内筋の筋活性化レベルＭＣＬ５であり、６は右腕の橈屈手根屈筋の筋活性化レベルＭＣＬ６であり、７は右腕の尺側手根伸筋の筋活性化レベルＭＣＬ７であり、８は左腕の指伸筋の筋活性化レベルＭＣＬ８であり、９は左腕の尺側手根屈筋の筋活性化レベルＭＣＬ９であり、そして、１０は左腕の方形回内筋の筋活性化レベルＭＣＬ１０である。各図の横軸はパッティングの回数であり、縦軸はレベルである。
【００８０】
図１１、図１３、図１５及び図１７は、それぞれ、学習者Ａ及びＢが１ｍ及び２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルＣＣＬとを示す図である。これら各図において、（ａ）は各パッティングにおけるパッティング誤差を示し、横軸はパッティングの回数であり縦軸は目標円中心からの距離（ｃｍ）である。（ｂ）は、左前腕の同時活性化レベルＣＣＬを示し、（ｃ）は、右前腕の同時活性化レベルＣＣＬを示す。（ｂ）、（ｃ）における横軸はパッティングの試行回数であり縦軸はレベルである。運動学習目標値は、これら各図において水平の実線で示している。また、図１１、図１３、図１５及び図１７は、パッティング回数がそれぞれ４回目、２回目、１回目及び１回目のパッティングが成功した場合における各筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬをそれぞれ運動学習目標値とし、パッティング回数がそれぞれ成功直後の５回目、３回目、２回目及び２回目から学習者Ａが表示装置２５の表示画面を見ることができる状態でパッティングを行った場合の結果である。
【００８１】
図１０乃至図１７を見ると分かるように、画面表示の参照可否を境に、成功頻度は、パッティングの回数が増えるに従って顕著に大きくなっている。また、各腕の同時活性化レベルＣＣＬは、パッティングの回数が増えるに従って運動学習目標値に収束する傾向がある。パッティング直後に今のパッティングにおける各筋活性化レベルＭＣＬ及び同時活性化レベルＣＣＬを表示装置に表示すると共に運動学習目標値も表示するので、本実験結果から、これらを表示しない場合には７０回行っても誤差はそれほど減少しないが、これらを表示すると３０数回で誤差の変化から学習できることがわかる。
【００８２】
【発明の効果】
以上のように、本発明に係る運動学習支援装置、運動学習支援方法及び運動学習支援プログラムは、表示装置が、運動学習目標値と今の動作における動作部分のインピーダンスの程度を表示するので、両者の差が視覚化される。このため、学習者は、この差を認識することで基準動作と現動作との相違の原因を知ることができ、動作部分が運動学習目標値に達するようにそのインピーダンスを調整することができる。従って、学習者は、基準動作を短期間に習得・再現することができる。動作の運動軌跡だけでなく、従来において習得が困難であった動作におけるインピーダンスも容易に習得することができる。また、動作におけるインピーダンスも記録及び保存可能である。
【図面の簡単な説明】
【図１】本実施形態における運動学習支援装置の構成を示す図である。
【図２】肘関節角度１８０度・肩関節角度９０度の状態で手首関節に対する正規化データ取得の状況を示す図である。
【図３】図３は、表面電極のだいたいの装着位置と筋肉との関係を示す図である。
【図４】力の測定結果及び手首関節のトルクを示す図である。
【図５】運動学習モードにおける表示装置の表示画面の一例を示す図である。
【図６】比較対照実験において、学習者Ａが１ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図７】比較対照実験において、学習者Ａが２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図８】比較対照実験において、学習者Ｂが１ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図９】比較対照実験において、学習者Ｂが２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図１０】運動学習支援装置を用いた実験において、学習者Ａが１ｍのパッティングを行った場合における各筋肉の筋活性化レベルを示す図である。
【図１１】運動学習支援装置を用いた実験において、学習者Ａが１ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図１２】運動学習支援装置を用いた実験において、学習者Ａが２ｍのパッティングを行った場合における各筋肉の筋活性化レベルを示す図である。
【図１３】運動学習支援装置を用いた実験において、学習者Ａが２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図１４】運動学習支援装置を用いた実験において、学習者Ｂが１ｍのパッティングを行った場合における各筋肉の筋活性化レベルを示す図である。
【図１５】運動学習支援装置を用いた実験において、学習者Ｂが１ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図１６】運動学習支援装置を用いた実験において、学習者Ｂが２ｍのパッティングを行った場合における各筋肉の筋活性化レベルを示す図である。
【図１７】運動学習支援装置を用いた実験において、学習者Ｂが２ｍのパッティングを行った場合におけるパッティング誤差と各腕の同時活性化レベルとを示す図である。
【図１８】関節トルクと屈筋及び伸筋との関係を示す図である。
【符号の説明】
２１　表面電極、２２　信号処理部、２３　６軸力覚センサ、２４　外部記憶装置、２５　表示装置、３２　割算回路、３４　低域通過フィルタ、３５　中央処理装置、３６　メモリ、４１　正規化基準値演算部、４２　係数行列演算部、４３　筋活性化レベル演算部、４４　同時活性化レベル演算部、４５　運動学習目標値設定部[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to a motion learning support device that supports learning of a motion, and more particularly to a motion learning support device that can also learn the impedance of a motion portion that mainly contributes to the motion in each part of a living body. The present invention also relates to an exercise learning support method and an exercise learning support program applied to the exercise learning support device.
[0002]
[Prior art]
In the case of animals, particularly humans, in learning motions, they often acquire a predetermined motion by imitating while watching the motion of another person. Sports skills such as golf, tennis, skiing, swimming, etc., carpenters, plasterers, production skills such as assembly work in factories, driving skills such as car operation, crane operation, production machine operation, ceramics, Nimai, Noh, Ningyo Joruri etc. Conventionally, when learning and inheriting various skills such as performing arts, a learner has learned by looking at the motion of a skilled person or by looking at a recorded video of the movement of a skilled person.
[0003]
On the other hand, when exercising, in many cases, not only one specific muscle works but a plurality of muscles work at the same time. The muscles that cooperate during one exercise are referred to as joint muscles, and the joint muscles are located on the same side of one joint in principle. On the other hand, muscles having the opposite functions are called opposing muscles (antagonistic muscles), and the opposing muscles are on the opposite side of the joint. In one exercise, the joint muscles naturally contract at the same time, but at the same time, the opposing muscles are also tense to some extent to adjust the exercise. Because the muscle contracts and generates only tension, changing the angle of the joint requires a pair of muscles that work antagonistically on both sides of the joint. Therefore, even if the force applied to the outside from the moving parts such as hands and feet is the same, and even if the locus of the moving parts is the same, if the combination of the tension of the extensor and the flexor is different, The impedance and force direction of the moving part will be different. A muscle that works in the direction of extending the joint in a manner independent of the direction of movement is called an extensor, and a muscle that works in the direction of bending the joint is called a flexor. When the joint is extended, the extensor is mainly active, the joint muscles are the extensors, and the opposing muscles are the flexors.
[0004]
FIG. 18 is a diagram showing the relationship between joint torque and flexor and extensor muscles. The horizontal axis in FIG. 18 is the angle of the joint, and the vertical axis is the tension of the flexor or extensor. As shown in FIG. 18, when the same joint angle θ1 is maintained, the tension generated by the extensor muscle a and the flexor muscle b includes a plurality of candidates and has redundancy. For this reason, the activity level of the extensor muscle a and the flexor muscle b can be changed even in the same posture of the moving part, and the extensor muscle can be hardened (C1) or softened (C2). For example, a state in which the elbow joint of the arm is stationary or operating with the elbow joint bent at 90 degrees is mainly caused by cooperation between the biceps of the upper arm and the triceps of the upper arm, which is in a relationship of opposition thereto. For simplicity, it is considered that the distance from the joint center to each muscle is constant regardless of the joint angle, and the product of the tension of each muscle and the moment arm is defined as torque. In this case, even when the torque of the biceps of the upper arm is 25 Nm and the torque of the triceps of the upper arm is 5 Nm, the torque of the biceps of the upper arm is 45 Nm and the torque of the triceps of the upper arm is 25 Nm, the elbow joint Is 20 Nm which is the difference. However, the hardness of the arm is larger in the former case than in the latter case. This means that, for example, when the arm is pushed, the former case is much more difficult to move than the latter case.
[0005]
Humans have empirically known that by changing the impedance of a motion part, the same motion produces different results. For example, in a golf pad, even when the head has the same trajectory and speed, the moving distance of the golf ball differs according to the hardness of the arm, and the golfer experiences that the harder arm rolls farther than the softer arm. I know. For this reason, humans need to skillfully control the impedance including the hardness and viscosity of the operating part by judging the working conditions.
[0006]
In addition, although the case where the length of the muscle fiber changes due to contraction to generate tension has been described above, the muscle fiber does not change its length due to contraction, but also functions to increase the tension. Such shrinkage is called isometric shrinkage. In the case of isometric contraction, the impedance of the moving part can be similarly considered. For example, when the abdominal pressure is greatly applied, the abdominal muscle is hard, and when the abdominal pressure is not applied, the abdominal muscle is soft.
[0007]
[Problems to be solved by the invention]
By the way, the learner needs to select an appropriate combination of the tension of the extensor muscle and the flexor muscle because the result of the motion differs depending on the degree of the impedance of the motion part. Although it is possible to learn, there is a problem that the degree of impedance of the operating part cannot be learned. In other words, there is a problem that even if the learner can learn the trajectory of the moving part of the skilled person, the result of the movement is different from that of the skilled person because the degree of the impedance of the moving part is not learned. Also, it is recognizable that the result of the operation was different from the expert and the operation failed, but when the trajectory of the operation part is almost the same as the expert, the cause of the failure cannot be understood. there were.
[0008]
Therefore, the present invention has been made in view of the above problems, and has as its object to provide an exercise learning support device that can also learn the impedance of an operation part that mainly contributes to the operation in each part of the living body. And It is another object of the present invention to provide a motor learning support method and a motor learning support program applied to the motor learning support device.
[0009]
[Means for Solving the Problems]
In order to achieve the above object, the motion learning support device according to the present invention presents, as a motion learning target value, the impedance of the motion part of the living body related to the motion when performing the reference motion, and performs the motion. It is provided with a presentation means for presenting the degree of impedance of the operating portion when the operation is performed. Then, the motor learning support method according to the present invention includes the steps of: presenting, as a motor learning target value, the impedance of the moving part of the living body related to the operation when the reference operation is performed; and performing the operation when the operation is performed. Presenting the degree of impedance of the portion. Further, the exercise learning support program according to the present invention, the step of presenting the impedance of the operating part of the living body according to the operation when the reference operation is performed to the computer as the exercise learning target value, when the operation is performed Presenting the degree of impedance of the operating part of the method.
[0010]
The exercise learning support device, the exercise learning support method, and the exercise learning support program having the above-described configuration are configured such that the display unit presents, as the exercise learning target value, the impedance of the operating part of the living body related to the operation when the reference operation is performed. At the same time, since the degree of impedance of the motion part when the motion is performed is presented, the difference between the target value of the exercise learning and the degree of impedance of the motion part in the current motion is visualized. Therefore, by recognizing the difference, the learner can know the cause of the difference between the reference operation and the current operation, and adjusts the impedance so that the operation part reaches the motor learning target value. be able to. Therefore, the learner can learn the reference operation in a short time.
[0011]
In order to achieve the above-mentioned object, in the above-mentioned exercise learning support device, a myoelectric signal detecting means for detecting an electromyographic signal based on a muscle action potential; and a pseudo-tension conversion for converting the myoelectric signal into pseudo tension. Means, force detecting means for detecting a force, normalized reference value calculating means for calculating a normalized reference value for normalizing the myoelectric signal based on the pseudo tension and the force, and a torque applied to the joint. The pseudo tension and the force based on the myoelectric signal normalized by the normalization reference value are calculated for a plurality of muscles causing the joint torque, and the joint torque and the pseudo torque are calculated based on the obtained pseudo tension and the force. Coefficient matrix calculating means for calculating a coefficient matrix for associating tension, and multiplying a myoelectric signal of a muscle by an absolute value of the coefficient matrix corresponding to the muscle to calculate a muscle activation level of the muscle for the plurality of muscles Operate on each Activation level calculation means, impedance calculation means for calculating impedance based on each muscle activation level, and impedance calculated by the impedance calculation means when a reference operation is performed is defined as the exercise learning target value. It further comprises a motor learning target value setting means, and the presenting means is configured to present the motor learning target value and an impedance calculated by the impedance calculating means when an operation is performed.
[0012]
Since such an exercise learning support device adopts an impedance based on the muscle activation level as an index of the impedance of the operating part, in addition to the above-described operation and effect, it is possible to accurately evaluate the degree of the impedance of the operating part.
[0013]
BEST MODE FOR CARRYING OUT THE INVENTION
Hereinafter, embodiments according to the present invention will be described with reference to the drawings. In each drawing, the same components are denoted by the same reference numerals, and description thereof will be omitted.
[0014]
(Configuration of the embodiment)
The present embodiment is an embodiment of an exercise learning support device, an exercise learning support method, an exercise learning support program, and a computer-readable recording medium recording the exercise learning support program according to the present invention. This is an embodiment in which impedance including hardness and viscosity is presented by a muscle activation level or a simultaneous activation level obtained from the muscle activation level.
[0015]
FIG. 1 is a diagram illustrating a configuration of the exercise learning support device according to the present embodiment. In FIG. 1, the motor learning support device 10 includes surface electrodes 21-1 to 21-10, a signal processing unit 22, a six-axis force sensor 23, an external storage device 24, a display device 25, a recording device 26, and an input device 27. It is configured with. The signal processing unit 22 includes a differential amplifier (hereinafter abbreviated as “DIFA”) 31-1 to 31-10, a division circuit (hereinafter abbreviated as “DIV”) 32-1 to 32-10, Full-wave rectifiers (hereinafter abbreviated as "FWRC") 33-1 to 33-10, low-pass filters (hereinafter abbreviated as "LPF") 34-1 to 34-10, and a central processing unit (hereinafter abbreviated as "LPF"). , "CPU") 35 and a memory 36.
[0016]
The plurality of surface electrodes 21-1 to 21-10 are attached to an operation part to be measured, and detect an action potential of a muscle related to an operation to be learned (hereinafter, abbreviated as “target operation”). . The diameter of this surface electrode is about 10 mm, and one set of surface electrodes 21 is attached to the body along the muscle fiber with a distance between the electrodes of 15 mm. In this embodiment, the myoelectric signal is measured by a surface induction method that records an action potential by attaching an electrode to the surface of the skin. The number of sets of surface electrodes is determined by the muscle to be measured, and the muscle to be measured is determined by the target operation. For example, since a golf pad is learned as an experimental example as described later, in the present embodiment, it is necessary to measure the hardness of both forearms as the hardness of the operating portion related to the golf pad, and the muscles of each of the five forearms are required. Of the surface electrodes 21-1 to 21-10 for measuring the myoelectric signals of the first and second electrodes.
[0017]
The detection is performed by sampling at a sampling period of 2 kHz and 12 bits, and the myoelectric signals from the surface electrodes 21-1 to 21-10 are amplified to predetermined levels by the DIFAs 22-1 to 22-10, respectively. . Each of the amplified myoelectric signals is input to each of the DIVs 32-1 to 32-10 and is divided by a predetermined value. This predetermined value will be described later. The divided myoelectric signals are input to full-wave rectifiers 33-1 to 33-10, respectively, and are full-wave rectified. An average is calculated for every 10 points of this full-wave rectified signal for each myoelectric signal (EGMave), and a moving average for every 5 points is calculated based on Equation 1 (EMGma). The value EMGma obtained in this manner is called a smooth myoelectric signal.
[0018]
(Equation 1)

[0019]
These smooth muscle electrical signals are input to LPFs 34-1 to 34-10, respectively. In the end, it is sampled at 200 Hz. The LPFs 34-1 to 34-10 convert the respective smooth muscle electrical signals into pseudo tensions and output them. The pseudo tension is a value corresponding to the tension generated by each muscle. The LPFs 34-1 to 34-10 are secondary filters, and the cutoff frequency thereof is several Hz, more preferably, several Hz, from the viewpoint of making the correspondence between the tension actually generated by the muscle and the pseudo tension accurate. The frequency is set to 1 Hz to 3 Hz, and still more preferably to 2 Hz to 3 Hz. In this embodiment, it is set to 2 Hz.
[0020]
In voluntary movement, the impulse transmitted from the upper center is transmitted to each muscle through the α motor neuron of the spinal cord, action potential is generated, the muscle contracts, and torque is generated in each joint, and the desired movement is performed. Occur. The pseudo tension, which is the output signal obtained by converting the myoelectric signal by the low-pass filter, is expected to reflect the firing frequency of the α motor neuron. It is believed that there is. The pseudo tension T (n) at the time n is given by Expression 2.
[0021]
(Equation 2)

[0022]
This is an FIR filter using Wi as an impulse response. In the present embodiment, the impulse response h (t) is represented by Expression 3.
[0023]
[Equation 3]

[0024]
Output signals (pseudo tension) of the LPFs 34-1 to 34-10 are output to the CPU 35. The CPU 35 includes a normalization reference value calculator 41, a coefficient matrix calculator 42, a muscle activation level calculator 43, a simultaneous activation level calculator 44, and an exercise learning target value setting unit 45. And so on. The normalization reference value calculation unit 41, the coefficient matrix calculation unit 42, the muscle activation level calculation unit 43, and the simultaneous activation level calculation unit 44 determine the normalization reference value, the coefficient matrix, the muscle activation level, and the simultaneous activation level, respectively. The calculation is performed as described later, and the exercise learning target value setting unit 45 sets the hardness of the operation part when the reference operation is performed as the exercise learning target value, as described later. Further, the CPU 35 processes data according to various programs, controls the 6-axis force sensor 23, the external storage device 24, the display device 25, the recording device 26, the input device 27, the DIV 32, and the memory 36, and exchanges data. I do. The memory 36 is connected to the CPU 35 and includes a RAM (Random Access Memory) and a ROM (Read Only Memory). The memory 36 includes a normalized reference value measurement program for measuring a normalized reference value, a coefficient matrix operation program for calculating a coefficient matrix, an exercise learning target value input program for inputting an exercise learning target value, and exercise learning. Programs, such as a motor learning program for assisting the robot and a control program for controlling each device, various values during execution of these programs, outputs from the surface electrodes 21-1 to 21-10, and a 6-axis force sensor 23 Stores the output from.
[0025]
In the 6-axis force sensor 23, a rod-shaped grip 23-1 is fixedly protruded from the base 23-2, and is applied to the grip 23-1 by a piezoelectric element provided at the bottom of the grip 23-1. Is detected. The six-axis force sensor 23 is arranged on a worktable 28 whose height can be adjusted. The external storage device 24 is a storage medium such as a flexible disk, a CD-R (Compact Disc Recordable) and a DVD-R (Digital Versatile Disc Recordable) and an auxiliary storage device for reading and writing data. For example, a flexible disk drive, a CD-R R drive and DVD-R drive. If the memory 36 does not store programs such as a normalized reference value measurement program, a coefficient matrix calculation program, a motion learning target value input program, and a motion learning program necessary for operating the motion learning support device 10, The recording medium on which these are recorded is read into the memory 36 via the external storage device 24.
[0026]
The display device 25 is a CRT, an LCD, or the like, and displays processing results of the CPU 35 such as output signals (pseudo tension) of the LPFs 34-1 to 34-10, output signals of the 6-axis force sensor 23, and output signals of the input device 27. indicate. The recording device 26 is an output device such as a printer that stores data on a medium such as paper, and includes output signals (pseudo tension) of the LPFs 34-1 to 34-10, output signals of the six-axis force sensor 23, and an input device 27. The processing result of the CPU 35 such as the output signal of the CPU 35 is recorded. The input device 27 is a device for inputting various commands and data to be given to the CPU 35, such as designation of a startup program, and is, for example, a keyboard and a mouse.
[0027]
(Operation of this embodiment)
The exercise learning support device 10 includes a normalization reference value measurement mode, a coefficient matrix operation mode, an exercise learning target value input mode, and an exercise learning mode. By using each of these modes, the learner can operate. The operation can be learned, including the degree of hardness of the part. Hereinafter, each mode will be described.
[0028]
(Normalized reference value measurement mode)
The normalization reference value measurement mode is a mode used when measuring a normalization reference value.
[0029]
Animal movements are caused by the contraction of muscles innervated by motor nerves and are coordinated by many skeletal muscles. The motor nerve is composed of a bundle of many motor nerve fibers, and each motor nerve fiber controls several to over 100 muscle fibers. Muscle fibers under the control of one motor nerve fiber can simultaneously be considered as a unit of skeletal muscle movement because they generate and contract action potentials simultaneously due to the impulse of the nerve fiber, and are called motor units. An electromyographic signal indicates an action potential of a skeletal muscle in a living body. In the case of the myoelectric signal, no action potential is recorded when the muscle is completely relaxed, and the action potential appears and is recorded when the muscle contracts due to the impulse of the motor nerve. The frequency of action potentials increases as contractions become stronger, due to an increase in the frequency of nerve impulses transmitted to a single motor unit and an increase in the number of motor units participating in contraction. Such myoelectric signals have no direct correspondence between the observed electrode potential and the muscle tension level. That is, even in the pseudo tension obtained from the myoelectric signal, there is no direct correspondence between the level of the pseudo tension and the level of the muscle tension. Therefore, in order to relate a certain level of the pseudo tension to which level of the muscle tension, a measurement of the normalized reference value is performed, and a predetermined value in the DIVs 32-1 to 32-10 is calculated based on the result. decide.
[0030]
As a normalization method, there is a method of relatively normalizing the maximum voluntary contraction force as 1. In this method, since the maximum voluntary contraction force is a muscle tension that is considered to be the maximum of the subject, it is considered to be arbitrarily determined by the subject, and sometimes the same muscle is different. The following normalization method is excellent in that it does not depend on the subject's arbitraryness.
[0031]
In the normalization of the myoelectric signal, as a first step, a predetermined force is generated in the operating portion, and the myoelectric signal of each muscle in this case is measured by the surface electrodes 21-1 to 21-10. As a second step, the maximum value of the pseudo tension is determined for each muscle. This maximum value is a normalized reference value for normalizing the myoelectric signal of each muscle, which is obtained for each muscle. Then, predetermined values in DIVs 32-1 to 32-10 are determined according to the normalized reference values.
[0032]
Next, as more specifically described, the success or failure of the golf pad depends on the hardness of both arms, so that it is necessary to measure a normalized reference value for the left arm and the right arm. Since the normalized reference values of both arms are measured in the same manner, the case of the left arm will be described below, and the description of the case of the right arm will be omitted.
[0033]
FIG. 2 is a diagram illustrating a situation in which normalized data for a wrist joint is obtained in a state where the elbow joint angle is 180 degrees and the shoulder joint angle is 90 degrees. FIG. 3 is a diagram showing the relationship between the approximate mounting position of the surface electrode and the muscle. FIG. 3A shows the ventral side of the left forearm, and FIG. 3B shows the dorsal side of the left forearm.
[0034]
In FIG. 1 and FIG. 2A, a learner who wants to learn an operation using the exercise learning support device 10 sits on a chair 29 and holds his left arm forearm while keeping his arms horizontal with his shoulders. It is fixed to the worktable 28. In the forearm for measuring the myoelectric signal in the present embodiment, there are also muscles involved in the movement of the finger, such as the deep finger flexor muscles. The palm was fixed to the 6-axis force sensor 23 in a state where the thumb was applied to the upper part of the grip 23-1 using the mold. Thus, the torque generated at the wrist joint can be measured by the six-axis force sensor 23 without applying extra force to the fingers.
[0035]
As shown in FIG. 3, the plurality of surface electrodes 21-1 to 21-5 serve as extensors / flexors of the wrist joint, such as (1) radial carpal flexor, (2) ulnar carpal extensor of the left forearm, and ▲ 3) finger extensors, (4) ulnar carpi flexor, and (5) square pronation muscles are mounted so as to be able to measure myoelectric signals generated in the five muscles, respectively. As is generally known in medicine, the flexor carpi radialis has a weak function as the flexor muscle and the pronation muscle in the elbow joint, but in the case of volar flexion in the carpal joint and the extensor carpi radialis longus. Works mainly in the case of radial deviation in cooperation with. The ulnar carpal extensor is the abductor muscle and mainly works in the case of ulnar displacement of the hand. The finger extensors serve primarily to assist finger extension and wrist extension. The ulnar carpi flexor works mainly in the case of volar flexion and ulnar deviation of the hand. The square pronation muscles mainly work when pronation the forearm. Pronation and supination are rotational movements and are performed about the axis from the radial head glenoid to the ulnar styloid process.
[0036]
The learner first activates the normalized reference value measurement program using the input device 27, and the CPU 35 mainly executes the normalized reference value measurement program using the normalized reference value calculation unit 41. The learner generates a force on his / her hand by isometric contraction while referring to the screen of the display device 25 in the above-described posture and wearing state.
[0037]
While the learner is thus generating a force on his / her hand, the output from the surface electrodes 21-1 to 21-5 is converted into pseudo tension by the above-described signal processing, and is taken into the CPU 35. In the case of measurement of this normalized reference value, the predetermined values of DIVs 32-1 to 32-5 are set to 1. That is, the DIVs 32-1 to 32-5 output the input as it is. The output from the six-axis force sensor 23 is also taken into the CPU 35. The CPU 35 stores these fetched values in the memory 36 and also records them in the recording device 26, and displays the output from the six-axis force sensor 23 on the display device 25.
[0038]
The display device 25 includes a circle indicating that the force applied to the 6-axis force sensor 23 is 5N, a cursor indicating the direction and magnitude of the current force applied by the learner to the 6-axis force sensor 23, The learner generates a force so that the cursor coincides with this circle in a predetermined direction while looking at the display device 25 while displaying 12 directions of arrows each having 30 degrees. Thereby, the learner generates a predetermined force that is not arbitrarily determined by the learner, that is, a force of 5N.
[0039]
The measurement was performed in a counterclockwise direction for 24 seconds so as to generate a force in 12 directions each of 30 degrees from the positive X direction (the bending direction) shown in FIG. 2 (b) in order to activate all muscles. Made to be. Here, each time the direction in which the force is generated is changed in the next direction, the force is released and the force is returned to 0N. This is to prevent the learner from generating a force of 5N or more. Note that the measurement start direction is not particularly limited, and may be started from any direction. Then, by multiplying the distance from the 6-axis force sensor 23 to the wrist joint, the myoelectric signals in the radius flexion and ulnar flexion directions and the flexion and dorsiflexion directions are measured.
[0040]
The CPU 35 obtains the maximum value of the pseudo tension for each muscle from the pseudo tension taken in. The maximum value of the pseudo tension is the normalized reference value. The CPU 35 displays the normalized reference value corresponding to each muscle on the display device 25 and records it on the recording device 26. Then, the CPU 35 sets the respective normalized reference values thus obtained as the predetermined values of the DIVs 32-1 to 32-5 in correspondence with the respective muscles. Thereby, a certain correspondence can be created between the pseudo tension (myoelectric signal) obtained from the surface electrodes 21-1 to 21-5 and the muscle tension.
[0041]
Further, the normalization reference value is similarly measured for the right forearm using the surface electrodes 21-6 to 21-10 and the like, and predetermined values in the DIVs 32-6 to 32-10 are set corresponding to each muscle. You.
[0042]
Since the myoelectric signal is normalized by such a normalized reference value, it is possible to absorb a difference in resistance value due to a difference in the mounting state. Since it is not arbitrarily determined by the subjects, it is possible to compare the pseudo tension, the torque of the joint, and the like between the subjects. In the present embodiment, the normalization reference value is obtained when the predetermined force is 5N. However, another force, for example, a force such as 3N or 7N may be generated to obtain the normalization reference value.
[0043]
(Calculation of torque / pseudo-tension relation formula: calculation of coefficient matrix a)
When the normalized reference value is calculated, the CPU 35 activates the coefficient matrix operation program stored in the memory 36 and executes the coefficient matrix operation program mainly using the coefficient matrix operation unit 42. Thereby, the motor learning support device 10 enters the coefficient matrix calculation mode. The coefficient matrix calculation mode is a mode in which the coefficient matrix a is calculated based on the pseudo tension T and the torque τ.
[0044]
In general, the joint torque τ of a certain degree of freedom is obtained by adding the product of the moment arm a and the tension T in that direction for all the muscles, and is expressed by Expression 4.
τ = aT (Equation 4)
Here, τ = (τ1, τ2,..., Τj) ^t ... (Equation 5-1)
T = (T1, T2,..., Ti) ^t ... (Equation 5-2)
And a are a matrix of j rows and i columns, and i and j are numbers assigned to each muscle that generates a torque at the joint. For example, in the case of wrist joint torque, the above-mentioned (1) to (5) are used.
[0045]
The moment arm a generally indicates the distance between the joint rotation axis and the muscle action line. In the case of the putting operation, it is considered that the wrist joint does not change significantly. For this reason, the moment arm a can be regarded as constant, and the muscle is considered to perform isometric contraction. Therefore, since the moment arm a is considered to be a degree of contribution to isometric contraction, in such a case, the moment arm a is referred to as a coefficient matrix.
[0046]
From the relations of Expression 4, Expression 5-1 and Expression 5-2 between the pseudo tension T, the torque τ, and the coefficient matrix a, the CPU 35 outputs to the outputs of the LPFs 34-1 to 34-10 stored in the memory 36 The coefficient matrix a is calculated by the least square method based on the pseudo tension T and the torque τ in consideration of the normalized reference value. Note that the normalized reference values are set in the DIVs 32-1 to 32-10, and the learner performs the same operation as the above-described normalized reference value measurement, and sets the pseudo tension T and the torque τ necessary for obtaining the coefficient matrix a. May be obtained.
[0047]
Here, FIG. 4 is a diagram showing the measurement results of the force and the torque of the wrist joint. FIG. 4 (a) shows the dorsiflexion / torsoflexion torque displayed in time series at the left wrist joint, and FIG. 4 (b) shows the extensor / radioflexion time display in time series at the left wrist joint. 4 (c) shows the torque in the dorsiflexion and buckling direction in the right wrist joint in time series, and FIG. 4 (d) shows the time scale in the right wrist joint in time series. Indicates the bending / radial torque. The vertical axis is the torque in Nm, and the horizontal axis is the measured elapsed time in seconds. In FIG. 4, the peaks that appear every two seconds correspond to one of the twelve directions, and the first peak corresponds to the positive X direction in FIG. In the case of a right wrist joint, force is exerted on the hand by isometric contraction in the positive X direction (dorsiflexion direction) in FIG. 2B. Is generated. The dashed line is an actual measurement value measured by the six-axis force sensor 23, and the solid line is a calculated value obtained from Expression 1. As shown in FIG. 4, the solid line and the broken line are in good agreement, and the accuracy of the coefficient matrix a has been verified.
[0048]
(Input of motor learning target value)
When the coefficient matrix is calculated, the CPU 35 starts the exercise learning target value input program stored in the memory 36, and mainly executes the muscle activation level operation unit 43, the simultaneous activation level operation unit 44, and the exercise learning target value. The exercise learning target value input program is executed using the setting unit 45. Thereby, the exercise learning support device 10 enters the exercise learning target value input mode. The exercise learning target value input mode is a mode for inputting the muscle activation level MCL and the simultaneous activation level CCL of each muscle that determines the hardness of the motion part related to the target motion when the motion that is the reference of the target motion is performed. It is. For example, when the target operation is a golf pad, the muscle activation level and the simultaneous activation level during the putting operation when the golf ball enters the cup. For example, in the case where the intended operation is the manufacture of pottery, the levels are the muscle activation level and the simultaneous activation level in the case where a vessel can be formed without breaking the shape using a potter's wheel.
[0049]
First, the learner wears the plurality of surface electrodes 21 and executes the target operation so that the myoelectric signal of each muscle that determines the hardness of the operation portion related to the target operation can be measured. For example, when trying to learn a golf pad, the surface electrodes 21-1 to 21-10 are attached to each part of the operation part shown in FIG. 3, and a predetermined putting operation is performed.
[0050]
The CPU 35 calculates the pseudo tension during the exercise of the learner by the surface electrodes 21-1 to 21-10, DIFA31-1 to 31-10, DIV32-1 to 32-10, FWRC33-1 to 33-10, and LPF34-1 to LPF34-1. Take in via 34-10. Next, the CPU 35 calculates the muscle activation level MCL according to Equation 6, and calculates the simultaneous activation level CCL according to Equation 7.
[0051]
(Equation 4)

[0052]
(Equation 5)

[0053]
Here, Equations 6 and 7 are based on the assumption that in the case of isometric contraction, a change in stiffness of the moving part corresponds to a change in muscle activation level.
[0054]
Next, the CPU 35 displays the calculated muscle activation level MCL and the simultaneous activation level CCL on the display device 25. When the current target operation is used as a reference, the learner inputs that fact using the input device. For example, in the case of a golf pad, when the golf ball enters the cup and the putting operation is successful, the learner uses the input device 27 to input that the current operation is a reference. When receiving the signal indicating that the current target operation is the reference operation, the CPU 35 stores the calculated muscle activation level MCL and the simultaneous activation level CCL in the memory 36 as exercise learning target values. On the other hand, if this signal is not received within a certain period, a message urging the learner to perform the next target operation is displayed on the display device 25.
[0055]
The learner performs a new target operation with reference to this message, and the CPU 35 calculates the muscle activation level MCL and the simultaneous activation level CCL by the same operation as described above, and displays them on the display device 25. The CPU 35 repeats these operations until the learner inputs from the input device 27 that the target operation is the reference operation. In this way, the exercise learning support device 10 receives the exercise learning target value.
[0056]
Here, in the above description, the learner's own target value is input to the exercise learning support device 10 by performing the target operation, but the learner who is proficient in the target operation is caused to perform the target operation and the exercise learning support device 10 You may make it input. In addition, an exercise learning target value obtained by an expert is acquired by using another exercise learning support device 10, and the acquired value is input to the exercise learning support device 10 used by the learner via the external storage device 24 from a recording medium on which the target value is acquired. You may do so. In particular, if it is not possible to clearly identify the success or failure of the desired action, such as performing arts such as Nimai, Noh and Ningyo Joruri, and production skills such as carpenter and plasterer, input or obtain the motor learning target value by a skilled person. Is valid. This makes it possible to preserve the performing arts and craftsmanship, and also to pass it on to later generations by learning later.
[0057]
As described above, the exercise learning support apparatus 10 performs a reference operation such as an operation when the user succeeds or an operation of a beginner level, an intermediate level, and an advanced level expert even when a skilled person is used as a model. Can be set to various levels, and the learner can set the exercise learning target value according to the learning level.
[0058]
(Motor learning)
When the exercise learning target value is input, the CPU 35 starts the exercise learning program stored in the memory 36, and mainly executes the exercise learning program using the muscle activation level calculation unit 43 and the simultaneous activation level calculation unit 44. Execute Accordingly, the exercise learning support device 10 enters the exercise learning mode. The exercise learning mode is a mode in which the learner learns and learns the target operation using the exercise learning target value.
[0059]
First, the CPU 35 displays the exercise learning target value related to the target operation on the display device 25. The learner wears the plurality of surface electrodes 21 so as to be able to measure the myoelectric signal of each muscle that determines the hardness of the motion portion related to the target motion. In addition, from the viewpoint that the front electrode 21 can be easily and accurately mounted, the mounting position of the front electrode 21 corresponding to the target operation is stored in the memory 36, and the CPU 35 refers to this memory 36 by referring to the memory 36. The mounting position may be displayed on the display device 25. Next, the learner performs the target operation while referring to the exercise learning target value displayed on the display device 25.
[0060]
The CPU 35 applies the pseudo tension during the operation of the learner to the surface electrodes 21-1 to 21-10, DIFA31-1 to 31-10, DIV32-1 to 32-10, FWRC33-1 to 33-10, and LPF34-1 to LPF34-1. Take in via 34-10. Next, the CPU 35 calculates the muscle activation level MCL according to Equation 6, and calculates the simultaneous activation level CCL according to Equation 7.
[0061]
FIG. 5 is a diagram illustrating an example of a display screen of the display device in the exercise learning mode. In FIG. 5, the muscle activation levels MCL51-A to 51-J of each muscle and their exercise learning target values 52-A to 52-J are displayed in a left display area 50-1, and each of them is displayed in a right display area 50-2. The simultaneous activation levels CCL55-L, 55-R of the forearm and the target values 56-L, 56-R for the motor learning are displayed. The horizontal axis indicates which muscle A to J the muscle activation level MCL is in the case of the muscle activation level MCL, and the simultaneous activation level CCL is the left forearm L or the right forearm in the case of the simultaneous activation level CCL. Indicates whether it is R. The left vertical axis indicates the level of MCL, and the right vertical axis indicates the level of CCL.
[0062]
Next, the CPU 35 stores the calculated muscle activation levels MCL51-A to 51-J and the simultaneous activation levels CCL55-L and 55-R in the exercise learning target value 52-A in the memory 36, as shown in FIG. Are displayed on the display device 25 together with .about.52-J, 56-L and 56-R.
[0063]
The learner has the muscle activation levels MCL51-A to 51-J and the simultaneous activation levels CCL55-L and 55-R relating to the target action just performed, and the muscle activation levels MCL52-A to 52-J of the exercise learning target value. By comparing the difference between the target operation and the simultaneous activation levels CCL56-L and 56-R, it is possible to recognize the difference between the target operation performed just now and the target operation in the case of success. When completing the learning, the learner inputs that fact using the input device 27. The CPU 35 terminates the exercise learning program when receiving a signal to end the learning, and displays a message urging the learner to perform the next learning when the signal is not received within a certain period of time. 25 is displayed.
[0064]
After seeing this message, the learner determines the muscle activation levels MCL51-A to 51-J and the simultaneous activation levels CCL55-L, 55-R and the muscle activation level of the motor learning target value relating to the target action performed just now. With reference to the differences between the MCLs 52-A to 52-J and the simultaneous activation levels CCL56-L and 56-R, it is determined which of the living body parts is to be stressed, and learning is performed by newly performing a target operation. It should be noted that this may be displayed on the display device 25 so that the correspondence between the muscle activation level and the muscle can be easily understood. For example, an illustration of an operating portion indicating the position of each muscle as shown in FIG. 3 is displayed, and symbols A to J indicating which muscle is displayed on the horizontal axis of the left display area are shown in each of the illustrations. It is displayed in association with the muscle.
[0065]
The CPU 35 calculates the muscle activation levels MCL51-A to 51-J and the simultaneous activation levels CCL55-L, 55-R by the same operation as described above, and calculates the exercise learning target values 52-A to 52-J, 56-R. L and 56-R are displayed on the display device 25. The CPU 35 repeats these operations until the learner inputs from the input device 27 that the learning ends. In this way, the learner can use the exercise learning support device 10 to learn the target motion including the degree of hardness of the motion part.
[0066]
In particular, in the exercise learning support device 10 according to the present invention, the simultaneous activation level CCL of the target operation and the simultaneous activation level CCL of the exercise learning target value are displayed on the display device 25 and visualized. The person can recognize the difference between the two simultaneous activation levels CCL, and can determine whether the moving part is harder or softer than when the movement learning target value is reached. Then, in the exercise learning support device 10 according to the present invention, the muscle activation level MCL of the target action and the muscle activation level MCL of the exercise learning target value are displayed and visualized on the display device 25. Can recognize the difference between the activation levels MCL of both muscles for each muscle, and can determine which muscle should apply more force. Thereby, the learner can know the cause of not reaching the target operation, and can learn the target operation in a shorter time than when the exercise learning support device 10 according to the present invention is not used.
[0067]
In addition, from the viewpoint of allowing the learner to easily recognize the difference between the muscle activation level MCL and the simultaneous activation level CCL relating to the target action just performed and each value (MCL, CCL) of the exercise learning target value, the difference is represented by a numerical value. It may be displayed.
[0068]
Then, from the viewpoint of allowing the learner to easily recognize the difference between the muscle activation level MCL and the simultaneous activation level CCL relating to the target operation performed just now and each value (MCL, CCL) of the exercise learning target value, the difference is determined according to the difference. To change the display color. For example, as shown in FIG. 5, the respective values (MCL, CCL) of the exercise learning target values are displayed by bars 52 and 56, and the muscle activation level MCL and the simultaneous activation level CCL relating to the target operation performed just now are represented by a bar graph 51, In the case of displaying by 55, the muscle activation level MCL relating to the target action performed just now is less than 30%, 30% or more and less than 60%, 60% or more and less than 90% with respect to the muscle activation level MCL of the exercise learning target value, When the values are 90% or more and less than 110%, 110% or more and less than 140%, 140% or more and less than 170%, and 170% or more, respectively, the bar graphs 51 and 56 are represented by red, orange, yellow, green, yellow, orange, and red. Color-coded as follows.
[0069]
In addition, from the viewpoint of making the learning of the beginner easy and improve immediately, in order to make the target operation successful, a correspondence table is created for each target operation to determine which muscles should be put in the strongest and which muscles should be released the most. 36, and when the beginner starts learning, the CPU 35 may refer to this correspondence table and display it on the display device 25. Alternatively, when the target operation is performed by moving a plurality of operation parts, a correspondence table is created for each target operation to determine which operation part is the hardest and which operation part is the softest in order to succeed the target operation. Then, the data may be stored in the memory 36, and when the beginner starts learning, the CPU 35 may refer to this correspondence table and cause the display device 25 to display. For example, in the case of a golf pad, in the case of a right-handed person, the message “Please hold the left arm harder than the right arm” is displayed on the display device 25.
[0070]
In the present embodiment, the coefficient matrix calculation program, the exercise learning target value input program, and the exercise learning program are automatically activated. However, the learner may activate these programs using the input device 27. Good.
[0071]
Further, in the present embodiment, the muscle activation level MCL, the simultaneous activation level, the exercise learning target value CCL, and the like relating to the target operation just performed are presented to the learner by the display device 25. A sound source such as a speaker or a buzzer may be notified to the support device 10 by sound instead of or in addition to the display device 25. In the case of such a sound, the difference between the muscle activation level MCL and the exercise learning target value relating to the current target operation and the difference between the simultaneous activation level CCL and the exercise learning target value thereof are, for example, timbres. Or the length of the sound. Alternatively, a stimulating device that performs tactile stimulation (including electric stimulation) to the motor learning support device 10 as a presentation unit may be notified by tactile sense instead of or in addition to the display device 25. In the case of such a stimulus, the difference between the muscle activation level MCL and the exercise learning target value or the difference between the simultaneous activation level CCL and the exercise learning target value related to the target operation performed this time is, for example, the stimulus. It may be indicated by the type, length, etc. Thus, the presentation means may utilize human perception.
[0072]
Further, in the present embodiment, the motor learning target value is measured in advance, and the measured value is recorded and displayed. However, the pseudo tension measurement including the surface electrode 21, DIFA31, DIV32, FWRC33 and LPF34 shown in FIG. Two sets of sections are prepared, and the myoelectric signals of the trained expert and the learner are measured simultaneously, and the learner's muscle activation level MCL and simultaneous activation level CCL and the teacher's muscle activation level MCL and simultaneous The difference from the activation level CCL may be displayed on the display device 25.
[0073]
In the present embodiment, the myoelectric signal is measured by the surface guidance method, but is not limited to this. Any method can be used as long as it can measure the action potential. For example, a needle electrode method may be used. The needle electrode method is a method in which a needle-like electrode is inserted into a muscle to record an action potential of a local muscle.
[0074]
(Comparative experiment results)
Next, the following experiment was performed to verify the learning effect of the exercise learning support device 10 according to the present invention. The experiment was conducted on learning of a golf pad that allows a golf ball to reach a target circle having a radius of 8 cm, which is like a cup on an artificial turf. The distance from the position where the golf ball was placed to the center of the target circle was 1 m and 2 m. The learners are two young men A and B who are golf beginners.
[0075]
First, as an experiment for comparison, learners A and B performed a putting operation in a state where the display screen of the display device 25 could not be seen, and measured the simultaneous activation level CCL and putting error of each arm.
[0076]
FIGS. 6 to 9 are diagrams showing the putting error and the simultaneous activation level CCL of each arm when the learners A and B perform putting of 1 m and 2 m, respectively. In each of these figures, (a) shows the putting error in each putting, the horizontal axis is the number of puttings, and the vertical axis is the distance (cm) from the center of the target circle. (B) shows the simultaneous activation level CCL of the left forearm, and (c) shows the simultaneous activation level CCL of the right forearm. The horizontal axis in (b) and (c) is the number of trials of putting, and the vertical axis is the level.
[0077]
As can be seen from FIGS. 6 to 9, the golf ball may reach the target circle and the putting may succeed, but the success frequency does not increase significantly as the number of puttings increases. Approximately the same frequency during comparative experiments. Also, the simultaneous activation level CCL of each arm does not tend to converge to a certain value as the number of puttings increases, and changes at random.
[0078]
Next, as an experiment using the motor learning support device 10 according to the present invention, the learners A and B perform a putting operation in a state where the display screen of the display device 25 cannot be viewed first, and The putting operation was performed while watching the display screen, and the simultaneous activation level CCL and putting error of each arm were measured.
[0079]
FIG. 10, FIG. 12, FIG. 14, and FIG. 16 are diagrams showing the muscle activation level MCL of each muscle when learners A and B perform putting of 1 m and 2 m, respectively. In each of these figures, 1 is the muscle activation level MCL1 of the flexor carpal flexor of the left arm, 2 is the muscle activation level MCL2 of the ulnar carpal extensor of the left arm, and 3 is the finger extensor of the left arm. The muscle activation level MCL3, 4 is the muscle activation level MCL4 of the ulnar carpi flexor of the left arm, 5 is the muscle activation level MCL5 of the square pronation muscle of the left arm, and 6 is the radius arm of the right arm Root flexor muscle activation level MCL6, 7 is the muscle activation level MCL7 of the ulnar carpal extensor of the right arm, 8 is the muscle activation level of the finger extensor muscle of the left arm MCL8, 9 is the left arm Muscle activation level MCL9 of the ulnar carpal flexor and 10 is the muscle activation level MCL10 of the square pronation muscle of the left arm. The horizontal axis in each figure is the number of puttings, and the vertical axis is the level.
[0080]
FIG. 11, FIG. 13, FIG. 15, and FIG. 17 are diagrams showing the putting error and the simultaneous activation level CCL of each arm when the learners A and B perform putting of 1 m and 2 m, respectively. In each of these figures, (a) shows the putting error in each putting, the horizontal axis is the number of puttings, and the vertical axis is the distance (cm) from the center of the target circle. (B) shows the simultaneous activation level CCL of the left forearm, and (c) shows the simultaneous activation level CCL of the right forearm. The horizontal axis in (b) and (c) is the number of putting trials, and the vertical axis is the level. The exercise learning target value is indicated by a horizontal solid line in each of these figures. FIGS. 11, 13, 15 and 17 show the muscle activation level MCL and the simultaneous activation level CCL when the number of puttings is 4th, 2nd, 1st and 1st, respectively. The results in the case where the learner A is able to see the display screen of the display device 25 from the fifth, third, second, and second times immediately after the success of the putting times, respectively, as the exercise learning target values. It is.
[0081]
As can be seen from FIGS. 10 to 17, the success frequency increases remarkably as the number of puttings increases, depending on whether or not the screen display can be referred to. Further, the simultaneous activation level CCL of each arm tends to converge to the exercise learning target value as the number of puttings increases. Immediately after putting, each muscle activation level MCL and simultaneous activation level CCL in the current putting are displayed on the display device and the exercise learning target value is also displayed. Even though the error does not decrease so much, it can be seen that when these are displayed, learning can be performed from a change in the error in thirty or more times.
[0082]
【The invention's effect】
As described above, in the exercise learning support device, the exercise learning support method, and the exercise learning support program according to the present invention, the display device displays the exercise learning target value and the degree of impedance of the motion part in the current operation. Is visualized. For this reason, the learner can know the cause of the difference between the reference motion and the current motion by recognizing the difference, and can adjust the impedance so that the motion portion reaches the motor learning target value. Therefore, the learner can learn and reproduce the reference motion in a short time. Not only the motion trajectory of the motion, but also the impedance of the motion, which has been difficult to learn in the past, can be easily learned. In addition, the impedance during operation can be recorded and stored.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a configuration of an exercise learning support device according to an embodiment.
FIG. 2 is a diagram showing a situation in which normalized data for a wrist joint is acquired in a state where an elbow joint angle is 180 degrees and a shoulder joint angle is 90 degrees.
FIG. 3 is a diagram showing a relationship between an approximate mounting position of a surface electrode and a muscle.
FIG. 4 is a diagram showing a force measurement result and a wrist joint torque.
FIG. 5 is a diagram illustrating an example of a display screen of the display device in the exercise learning mode.
FIG. 6 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner A puts 1 m in a comparative control experiment.
FIG. 7 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner A puts 2 m in a comparative control experiment.
FIG. 8 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner B puts 1 m in a comparative control experiment.
FIG. 9 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner B puts 2 m in a comparative control experiment.
FIG. 10 is a diagram showing a muscle activation level of each muscle when a learner A puts 1 m in an experiment using the motor learning support device.
FIG. 11 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner A puts 1 m in an experiment using the motor learning support device.
FIG. 12 is a diagram showing the muscle activation level of each muscle when a learner A puts 2 m in an experiment using the motor learning support device.
FIG. 13 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner A puts 2 m in an experiment using the motor learning support device.
FIG. 14 is a diagram showing a muscle activation level of each muscle when a learner B puts 1 m in an experiment using the motor learning support device.
FIG. 15 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner B puts 1 m in an experiment using the motor learning support device.
FIG. 16 is a diagram showing a muscle activation level of each muscle when a learner B puts 2 m in an experiment using the motor learning support device.
FIG. 17 is a diagram showing a putting error and a simultaneous activation level of each arm when a learner B puts 2 m in an experiment using the motor learning support device.
FIG. 18 is a diagram showing the relationship between joint torque and flexor and extensor muscles.
[Explanation of symbols]
Reference Signs List 21 surface electrode, 22 signal processing unit, 2 6-axis force sensor, 24 external storage device, 25 display device, 32 division circuit, 34 low-pass filter, 35 central processing unit, 36 memory, 41 normalization reference value calculation Unit, 42 coefficient matrix operation unit, 43 muscle activation level operation unit, 44 simultaneous activation level operation unit, 45 exercise learning target value setting unit

Claims (6)

It is provided with presenting means for presenting the impedance of the movement part of the living body related to the movement when the reference movement is performed as the target value of the exercise learning and presenting the degree of the impedance of the movement part when the movement is performed. An exercise learning support device characterized by the following. EMG signal detection means for detecting a myoelectric signal based on the action potential of muscle, pseudo tension conversion means for converting the myoelectric signal to pseudo tension, force detection means for detecting force, the pseudo tension and the force Normalization reference value calculation means for calculating a normalization reference value for normalizing the myoelectric signal based on the plurality of muscles that generate a torque at a joint. Coefficient matrix calculating means for calculating the pseudo tension and the force based on the myoelectric signal, and calculating a coefficient matrix for associating joint torque and pseudo tension based on the obtained pseudo tension and the force; A muscle activation level calculating means for multiplying a signal by an absolute value of the coefficient matrix corresponding to the muscle to calculate a muscle activation level of the muscle for each of the plurality of muscles, based on each of the muscle activation levels Impeded And a motion learning target value setting unit that sets the impedance calculated by the impedance calculation unit when the reference operation is performed as the motor learning target value, and the presentation unit includes: The motor learning support apparatus according to claim 1, wherein the motor learning target value and the impedance calculated by the impedance calculating means when the operation is performed are presented. The exercise learning support device according to claim 2, wherein the exercise learning target value further includes a target value for a muscle activation level, and the presenting unit further presents a muscle activation level. The normalization reference value calculation means measures a plurality of pseudo tensions when a constant force is detected by the force detection means, and obtains a maximum value from the plurality of pseudo tensions as a normalization reference value. The exercise learning support device according to claim 2, wherein: The method comprises the steps of: presenting, as a motor learning target value, the impedance of a motion part of the living body related to the motion when a reference motion is performed; and presenting the degree of impedance of the motion part when the motion is performed. An exercise learning support method characterized in that: Presenting, to a computer, an impedance of a motion part of the living body related to the motion when the reference motion is performed as a motion learning target value; and presenting a degree of impedance of the motion portion when the motion is performed. Exercise learning support program for running.

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