atsushi が 2026年01月31日19時01分39秒に編集

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## 初めに　**皆さん、こんにちは。** 今回は筋電位と脳波を活用して義手を動かせるのかをテストしてみました！一般的に義手への生体信号の応用には筋電位が活用されていますが、脳波の場合は実際どれくらいの精度で認識できるのかなどに興味があり両方に取り組んでみました。初めて取り組むことが多く、おぼつかないところがあるかもしれませんが、よろしくお願いいたします！！最初に結果をお伝えしておくと、筋電位はある程度動かせる感じがしましたが、脳波の場合は動かせているかどうかが分からない感じでした。あまり良い結果とはなりませんでしたが、見ていただけると幸いです。　 ## 脳波と筋電位 #### ・非侵襲の脳波（EEG）について非侵襲の脳波（EEG）とは脳神経細胞（ニューロン）の活性化によって生じる電位を頭皮上に設置した電極から記録・記録した物のことを指します。今回は脳波を取得するための脳波計にgtec社のUnicornを使用し、電極配置はある程度有効そうな位置を予測して配置しています。 ![gtec社のUnicorn](https://camo.elchika.com/d7356d64337c27ccaa13c96d8ed869efcb26d86c/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f64393838316561332d363232622d343534372d396331362d313839666530383966383935/) #### ・筋電位について筋電位は脳波と比べて信号の振幅が大きいため、一般的な脳波計でも測れます。そこで、「pieeg」と呼ばれる簡易生体電位計測デバイス使用し、取得してみました。比較的安価に生体信号を取得できるデバイスで、今回はこのデバイスを活用して筋電位を取得してみました。 ![pieeg](https://camo.elchika.com/3a3625dfef253fe215064f5505766274737ec531/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f36333737336566632d383739312d343530362d626638302d643634323865376565386639/) 筋電位の貼り付け位置に関しては以下となります。 ![配置位置](https://camo.elchika.com/6026629d28bf138259afbd81fab7b4937de68a4c/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f30663262613734302d333964312d343431632d613636312d303435303231666363383462/) **Biasoutとref は鎖骨 ch1は手首の下 ch2は肘より上でグー・パーした際に筋肉が動く位置** ![貼り付け位置](https://camo.elchika.com/c3eb18171199b85884530340ba33ce4b1ee128c7/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f35656462613263372d313438352d343166612d623666392d323665303634353734376438/) #### 記録方法に関して今回、AIを活用して生体信号の違いを分類しており、AI学習させるためには生体信号に対応付けられた教師ラベルが必要となります。そのため、被験者には以下のような動画を見てもらい、教師ラベルが付けられたデータの取得を行っています。最初に十字が表示された後、被験者に数秒間だけ動作・想起してもらう物を示す画像が表示されます。その後、再度4秒間十字が表示されるので、その間に被験者は直前に表示される動作を想起もしくは動かしてもらうことで教師ラベルに応じた信号の取得を行っています。 @[youtube](https://youtu.be/uCpy2hXpVyk) ## 作成工程今回、主に３Dプリンタや市販のものを組み合わせて義手を作成しています。３D プリンタに関するデータは以下の物を調整して使用しています。秋月商店で販売しているモータなど日本で買えるものを使用して作成できるように多少改造しています。　基本的に中を後で開閉出来るようにしたり、組み替えたりするために100均のマジックテープやねじ穴を設けて固定しています。 [【引用した3Dプリントデータ】Robotic Prosthetic Hand](https://www.thingiverse.com/thing:1691704) ![固定方法の一例](https://camo.elchika.com/8e12c757b007bdf87ef8fcd86f72246357b8edf2/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f61323064643333632d393030622d343362342d383834642d666533356536383634656437/) ### AIについてと不足分を補う工夫 EEGNetと呼ばれる畳み込みニューラルネットワークモデルを筋電・脳波共に分類で使用してみました。このモデルは構造的に軽量でマイコンへの実装がしやすいモデルです。Tensorflow(python上)で学習した場合、以下くらいの精度でした。 | 脳波 | 2分類で6割程度 | |:---:|:---| | **筋電** | **2分類で9割程度** | **注意）** 脳波は個人差があるので、この精度は目安として考えてください。 Spresenseに実装するモデルと重み（tflite形式の物）に関するコードはそのまま実行できるわけではないので注意してください。 #### 多分類をするのは難しい（特に、EEGでは...）そこでジャイロセンサを付けて角度によって出る動作を制限してみました。以下が簡単なイメージ図で、2分類以上が難しいと判断して、角度によって動作内容を変える仕組みにしました。何もしていない時・想起していない時は半開きの状態となり、動作・想起している際の動きに関しては角度で決まるようにしています。 ![イメージ図](https://camo.elchika.com/11ef50743dffcd27dce5adeeb0d57fca16fe2e8e/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f31353438333365322d663430622d343265372d383738352d313837653162303561376337/) ## 作成したプログラムについて #### Spresense用コードファイル数が多いので全体の構成を示します。サンプルコードを利用しているので多くなっています。 ![ディレクトリ構成](https://camo.elchika.com/4807b5e1e090827fd724dc48a11d15b1ba71a3bc/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f66346266346238362d333335382d343766342d623438362d613631653735643437646266/) ##### main.ino メインとなるコードで、ループ（脳波を受信して義手を動かす流れ）で実行する内容を記載しています。グーを継続するといった動き・想起の検出は難しいので、AIの検出で5回何もしていない状態が続くと半開き状態になるように設定しております。 ``` #include "common.h" //共通する項目の宣言 #include "hand.h" //Armの動作 #include "wifi.h" //UDPによるデータの受信 #include "port.h" //シリアル通信でのデータ受信 #include "gyro.h" //姿勢の検出 #include "tflite.h" //Tensorflow #include <LowPower.h> void setup (){ LowPower.begin(); LowPower.clockMode(CLOCK_MODE_156MHz); //動作周波数の変更 Serial.begin(115200); if (recive == "udp"){ udp_setup(); } setup_hand(); setup_gyro(); setup_tflite(); } int no_move_count = 0 ; void loop (){ //===================== //データの受け取り //===================== if (recive == "udp") { udp_receive(); } else if (recive == "Serial") { Serial_receive(); } //===================== //データの確認　 //===================== /*末尾の値から受信できているかを確認*/ Serial.println("-----data cheak------"); for (int ch = 0; ch < H; ch++) { int8_t last_value = dataArray[ch][W - 1][0]; // 最後尾の値を取得 printf("CH %d last value = %d\n", ch, last_value); } //===================== //tensorflow_liteの実行 //===================== float result = main_tflite(); int gyro_result = main_gyro(); if (result) result = gyro_result; Serial.print("result_class:"); Serial.println(result); //========== //義手の動作 //========== if (no_move_count==5){ //初期化 main_hand(0); no_move_count=0; }else if (result==0){ //無動作のカウント no_move_count++; }else{ //動作実行 main_hand(result); } } ``` ##### tflite.h Tensorflow liteによる動作とランニング前の入力データの量子化などを行っています。なお、筋電に関するAIの閾値は高めに設定しています。 ``` //tensorflow list model //EMG #include "EMG/EMG_C2_model.h" //EEG #include "EEG/EEG_C2_model.h" //tflite lib #include "tensorflow/lite/micro/all_ops_resolver.h" #include "tensorflow/lite/micro/micro_error_reporter.h" #include "tensorflow/lite/micro/micro_interpreter.h" #include "tensorflow/lite/micro/system_setup.h" #include "tensorflow/lite/schema/schema_generated.h" // モデル・インタプリタ・テンソル関連 tflite::ErrorReporter* error_reporter = nullptr; const tflite::Model* model = nullptr; tflite::MicroInterpreter* interpreter = nullptr; TfLiteTensor* input = nullptr; TfLiteTensor* output = nullptr; // TensorArena 設定 constexpr int kTensorArenaSize = 100*1024; //モデルを変える時に調整必須 static uint8_t tensor_arena[kTensorArenaSize]; extern int8_t dataArray[H][W][1]; void setup_tflite() { tflite::InitializeTarget(); //モデルを取得 //モデルを変える時、入力データの形状も変更する必要がある if (USE_MODE == "EEG"){ model = tflite::GetModel(EEG_C2_hand_image_tflite); } else { model = tflite::GetModel(EMG_C2_hand_image_tflite); } if (model->version() != TFLITE_SCHEMA_VERSION) { Serial.println("Model schema mismatch!"); while (true); } // インタプリタの初期化 static tflite::MicroErrorReporter micro_error_reporter; error_reporter = &micro_error_reporter; static tflite::AllOpsResolver resolver; // Build an interpreter to run the model with. static tflite::MicroInterpreter static_interpreter( model, resolver, tensor_arena, kTensorArenaSize, error_reporter); interpreter = &static_interpreter; if (interpreter->AllocateTensors() != kTfLiteOk) { Serial.print("Error:AllocateTensors() failed"); Serial.println(interpreter->AllocateTensors()); Serial.print(kTfLiteOk); while (true); } Serial.print("Required arena size: "); Serial.println(interpreter->arena_used_bytes()); input = interpreter->input(0); output = interpreter->output(0); } //tflite run float main_tflite() { //debug /* for (int ii = 0; ii < W; ++ii) { // W: 時系列長（サンプル数） for (int i = 0; i < H; ++i) { // H: チャンネル数（8など） Serial.print(dataArray[i][ii][0]); Serial.print(i < H - 1 ? "," : "\n"); // カンマ区切り、最後だけ改行 } } */ for (int i = 0; i < H; ++i) { for (int j = 0; j < W; ++j) { float x = static_cast<float>(dataArray[i][j][0]); int32_t quantized = static_cast<int32_t>((x / input->params.scale) + input->params.zero_point); // int8_tの範囲にクリップ if (quantized > 127) quantized = 127; if (quantized < -128) quantized = -128; // (i, j, 0) のインデックス → フラットに変換 int index = i * W + j; input->data.int8[index] = static_cast<int8_t>(quantized); } } //実行 Serial.println("tflite_run"); if (interpreter->Invoke() != kTfLiteOk) { Serial.println("Invoke failed!"); return -1; } //出力 int num_classes = output->dims->data[1]; float max_score = -INFINITY; int predicted_class = -1; //総クラス数 Serial.print("total classes:"); Serial.println(num_classes); //出力確認 for (int i = 0; i < num_classes; ++i) { int8_t value = output->data.int8[i]; float score = (value - output->params.zero_point) * output->params.scale; Serial.print("Class "); Serial.print(i); Serial.print(": "); Serial.print(score, 4); Serial.print(" int val: "); Serial.println(value); if (score > max_score) { max_score = score; predicted_class = i ; } } // 出力に関する設定 if ((max_score > 0.65) || (USE_MODE == 'EEG')) { return predicted_class; } else { return 0; } } ``` ##### AIモデルのコード EEGNetの重みなどに関する内容を記載したファイルです。・EMG_C2_model.h ・EEG_C2_model.h EEG_C2_model.hの方が少し大きい理由は電極数が多く、入力データのサイズが異なるからです。 ##### ・EMG_C2_model.h ``` unsigned char EMG_C2_hand_image_tflite[] = { 0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x8c, 0x00, 0x00, 0x00, 0x0c, 0x01, 0x00, 0x00, 0x90, 0x0b, 0x00, 0x00, 0xa0, 0x0b, 0x00, 0x00, 0x64, 0x21, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x9e, 0xf3, 0xff, 0xff, 0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 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0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x03 }; unsigned int EEG_C2_hand_image_tflite_len = 10392; ``` ##### wifi.h UDPでのデータの受信を行っており、パケットごとに分割されるデータの末尾に番号を付加することで、安定した受信ができるになっています。これに関しては以下の方ものを参考にさせていただきました。 [【SPRESENSE】映像伝送モジュールの作成【ドローン】](https://elchika.com/article/ccbb1ead-ca4a-444d-b3db-890ffcebdc09/) ``` #include <TelitWiFi.h> #include "config.h" TelitWiFi gs2200; TWIFI_Params gsparams; char server_cid = 0; const uint16_t UDP_PACKET_SIZE = W + 1; //パケット最大データ量

int8_t recv_buf [UDP_PACKET_SIZE] = {0}; // UDP受信バッファ　　uint8_tだと値が変わるかも

int8_t recv_buf [UDP_PACKET_SIZE] = {0}; // UDP受信バッファ　　

extern int8_t dataArray[H][W][1]; // AI の入力データ（H チャンネル × 1024サンプル） void udp_setup() { AtCmd_Init(); /* Initialize SPI access of GS2200 */ Init_GS2200_SPI_type(iS110B_TypeC); /* Initialize AT Command Library Buffer */ gsparams.mode = ATCMD_MODE_STATION; gsparams.psave = ATCMD_PSAVE_DEFAULT; if (gs2200.begin(gsparams)) { ConsoleLog("GS2200 Initilization Fails"); while(1); } //接続の確立 while(1){ /* GS2200 Association to AP */ if (gs2200.activate_station(AP_SSID, PASSPHRASE)) { ConsoleLog("Association Fails"); continue; } server_cid = gs2200.connectUDP(UDPSRVR_IP,UDPSRVR_PORT,LocalPort); ConsolePrintf("server_cid: %d \r\n", server_cid); if (server_cid == ATCMD_INVALID_CID) { ConsoleLog("UDP connection failed"); continue; } break; } ConsoleLog("WiFi setup success"); } void udp_receive() { Serial.println("UDP recive start"); int receive_count = 0; while (receive_count < H) { //　バッファの初期化 WiFi_InitESCBuffer(); // ----- GS2200 から UDP データ受信 ----- int len = gs2200.read(server_cid, recv_buf, UDP_PACKET_SIZE); // ---- "udp_start" なら受信リセット ---- if (memcmp(recv_buf, "udp_start", 9) == 0) { Serial.println("UDP START received → reset buffer"); receive_count = 0; continue; } //データサイズの確認 if (len <= 0) { Serial.println("failed:recive data size 0"); continue; } //パケット番号（チャンネル番号） int8_t packet_num = recv_buf[len-1]; //パケット番号の確認 if (packet_num > H) { Serial.print("Invalid packet_num = "); Serial.println(packet_num); Serial.print("packet_size = "); Serial.println(len); Serial.print("Error stop!"); receive_count = 0; continue; } //データ部分を1ch分をコピー memcpy(dataArray[packet_num], recv_buf, W); Serial.print("CH="); Serial.print(packet_num); Serial.print(" received (size="); Serial.print(len); Serial.print(")"); Serial.print("receive_count="); Serial.println(receive_count); receive_count++; //受信完了であることを通知 gs2200.write(server_cid,"ok", 3); } Serial.println("=== ALL CHANNELS RECEIVED ==="); } ``` ##### config.h wifiのSSIDやパスワード等に関する設定を行うファイルです。ラズパイやPCなどのIPを指定します。 ``` #ifndef _CONFIG_H_ #define _CONFIG_H_ #define AP_SSID "" #define PASSPHRASE "" #define UDPSRVR_IP "" //ラズパイのIPもしくはPCのIP #define UDPSRVR_PORT "10001" #define LocalPort "10001" #endif /* _CONFIG_H_ */ ``` ##### common.h 受信するデータの電極数やサンプル数等の設定を行うファイルです。 ``` //入力形状 #define H (8) // 脳波 or 筋電のチャンネル数 #define W (1000) // 1 チャンネルあたりのサンプル数 int8_t dataArray[H][W][1]; // AI の入力データ（H チャンネル × 1024サンプル） //動作の指定 #define USE_MODE "EEG" //筋電位と脳波の使い分けが可能 #define recive "udp" ``` ##### ジャイロセンサについてジャイロセンサーに関しては主にサンプルプログラムを流用しています。メインとなるコードを以下のファイルへと変更しており、上下の角度を検出してその結果を返す様に編集しております。・gyro.h ・gyro.cpp 【使用したセンサー】[SONY SPRESENSE用6軸加速度計・ジャイロスコープセンサ (BMI270) Addonボード](https://www.switch-science.com/products/9870?srsltid=AfmBOoqRiyGQvbWt_pQYmTE70cMhG3BNXPRYjJRAeYdgN0Guk1GJi7O7) 【サンプルコード】[6軸加速度計・ジャイロスコープセンサ (BMI270) Addonボードのサンプルコード](https://github.com/fooping-tech/Spresense_6dof_add_on/tree/main/example/Spresense_6dof_add_on_test) ##### gyro.h ``` #include "BMI270_Arduino.h" struct Attitude { float roll; float pitch; }; void setup_gyro(); int main_gyro(); ``` ##### gyro.cpp ``` #include "gyro.h" BMI270Class BMI270; void IMU_print_rslt(int8_t rslt) { switch (rslt) { case BMI2_OK: return; /* Do nothing */ break; case BMI2_E_NULL_PTR: Serial.println("Error [" + String(rslt) + "] : Null pointer"); break; case BMI2_E_COM_FAIL: Serial.println("Error [" + String(rslt) + "] : Communication failure"); break; case BMI2_E_DEV_NOT_FOUND: Serial.println("Error [" + String(rslt) + "] : Device not found"); break; case BMI2_E_OUT_OF_RANGE: Serial.println("Error [" + String(rslt) + "] : Out of range"); break; case BMI2_E_ACC_INVALID_CFG: Serial.println("Error [" + String(rslt) + "] : Invalid accel configuration"); break; case BMI2_E_GYRO_INVALID_CFG: Serial.println("Error [" + String(rslt) + "] : Invalid gyro configuration"); break; case BMI2_E_ACC_GYR_INVALID_CFG: Serial.println("Error [" + String(rslt) + "] : Invalid accel/gyro configuration"); break; case BMI2_E_INVALID_SENSOR: Serial.println("Error [" + String(rslt) + "] : Invalid sensor"); break; case BMI2_E_CONFIG_LOAD: Serial.println("Error [" + String(rslt) + "] : Configuration loading error"); break; case BMI2_E_INVALID_PAGE: Serial.println("Error [" + String(rslt) + "] : Invalid page "); break; case BMI2_E_INVALID_FEAT_BIT: Serial.println("Error [" + String(rslt) + "] : Invalid feature bit"); break; case BMI2_E_INVALID_INT_PIN: Serial.println("Error [" + String(rslt) + "] : Invalid interrupt pin"); break; case BMI2_E_SET_APS_FAIL: Serial.println("Error [" + String(rslt) + "] : Setting advanced power mode failed"); break; case BMI2_E_AUX_INVALID_CFG: Serial.println("Error [" + String(rslt) + "] : Invalid auxilliary configuration"); break; case BMI2_E_AUX_BUSY: Serial.println("Error [" + String(rslt) + "] : Auxilliary busy"); break; case BMI2_E_SELF_TEST_FAIL: Serial.println("Error [" + String(rslt) + "] : Self test failed"); break; case BMI2_E_REMAP_ERROR: Serial.println("Error [" + String(rslt) + "] : Remapping error"); break; case BMI2_E_GYR_USER_GAIN_UPD_FAIL: Serial.println("Error [" + String(rslt) + "] : Gyro user gain update failed"); break; case BMI2_E_SELF_TEST_NOT_DONE: Serial.println("Error [" + String(rslt) + "] : Self test not done"); break; case BMI2_E_INVALID_INPUT: Serial.println("Error [" + String(rslt) + "] : Invalid input"); break; case BMI2_E_INVALID_STATUS: Serial.println("Error [" + String(rslt) + "] : Invalid status"); break; case BMI2_E_CRT_ERROR: Serial.println("Error [" + String(rslt) + "] : CRT error"); break; case BMI2_E_ST_ALREADY_RUNNING: Serial.println("Error [" + String(rslt) + "] : Self test already running"); break; case BMI2_E_CRT_READY_FOR_DL_FAIL_ABORT: Serial.println("Error [" + String(rslt) + "] : CRT ready for DL fail abort"); break; case BMI2_E_DL_ERROR: Serial.println("Error [" + String(rslt) + "] : DL error"); break; case BMI2_E_PRECON_ERROR: Serial.println("Error [" + String(rslt) + "] : PRECON error"); break; case BMI2_E_ABORT_ERROR: Serial.println("Error [" + String(rslt) + "] : Abort error"); break; case BMI2_E_GYRO_SELF_TEST_ERROR: Serial.println("Error [" + String(rslt) + "] : Gyro self test error"); break; case BMI2_E_GYRO_SELF_TEST_TIMEOUT: Serial.println("Error [" + String(rslt) + "] : Gyro self test timeout"); break; case BMI2_E_WRITE_CYCLE_ONGOING: Serial.println("Error [" + String(rslt) + "] : Write cycle ongoing"); break; case BMI2_E_WRITE_CYCLE_TIMEOUT: Serial.println("Error [" + String(rslt) + "] : Write cycle timeout"); break; case BMI2_E_ST_NOT_RUNING: Serial.println("Error [" + String(rslt) + "] : Self test not running"); break; case BMI2_E_DATA_RDY_INT_FAILED: Serial.println("Error [" + String(rslt) + "] : Data ready interrupt failed"); break; case BMI2_E_INVALID_FOC_POSITION: Serial.println("Error [" + String(rslt) + "] : Invalid FOC position"); break; default: Serial.println("Error [" + String(rslt) + "] : Unknown error code"); break; } } int8_t IMU_configure_sensor() { int8_t rslt; uint8_t sens_list[2] = { BMI2_ACCEL, BMI2_GYRO }; struct bmi2_sens_config config[2]; /* Configure the type of feature. */ config[0].type = BMI2_ACCEL; /* NOTE: The user can change the following configuration parameters according to their requirement. */ /* Set Output Data Rate */ config[0].cfg.acc.odr = BMI2_ACC_ODR_200HZ; /* Gravity range of the sensor (+/- 2G, 4G, 8G, 16G). */ config[0].cfg.acc.range = BMI2_ACC_RANGE_2G; /* The bandwidth parameter is used to configure the number of sensor samples that are averaged * if it is set to 2, then 2^(bandwidth parameter) samples * are averaged, resulting in 4 averaged samples. * Note1 : For more information, refer the datasheet. * Note2 : A higher number of averaged samples will result in a lower noise level of the signal, but * this has an adverse effect on the power consumed. */ config[0].cfg.acc.bwp = BMI2_ACC_NORMAL_AVG4; /* Enable the filter performance mode where averaging of samples * will be done based on above set bandwidth and ODR. * There are two modes * 0 -> Ultra low power mode * 1 -> High performance mode(Default) * For more info refer datasheet. */ config[0].cfg.acc.filter_perf = BMI2_PERF_OPT_MODE; /* Configure the type of feature. */ config[1].type = BMI2_GYRO; /* The user can change the following configuration parameters according to their requirement. */ /* Set Output Data Rate */ config[1].cfg.gyr.odr = BMI2_GYR_ODR_100HZ; /* Gyroscope Angular Rate Measurement Range.By default the range is 2000dps. */ config[1].cfg.gyr.range = BMI2_GYR_RANGE_2000; /* Gyroscope bandwidth parameters. By default the gyro bandwidth is in normal mode. */ config[1].cfg.gyr.bwp = BMI2_GYR_NORMAL_MODE; /* Enable/Disable the noise performance mode for precision yaw rate sensing * There are two modes * 0 -> Ultra low power mode(Default) * 1 -> High performance mode */ config[1].cfg.gyr.noise_perf = BMI2_POWER_OPT_MODE; /* Enable/Disable the filter performance mode where averaging of samples * will be done based on above set bandwidth and ODR. * There are two modes * 0 -> Ultra low power mode * 1 -> High performance mode(Default) */ config[1].cfg.gyr.filter_perf = BMI2_PERF_OPT_MODE; /* Set the accel configurations. */ rslt = BMI270.set_sensor_config(config, 2); if (rslt != BMI2_OK) return rslt; rslt = BMI270.sensor_enable(sens_list, 2); if (rslt != BMI2_OK) return rslt; return rslt; } void setup_gyro() { delay(20); int8_t rslt = BMI270.begin(BMI270_I2C,BMI2_I2C_SEC_ADDR); IMU_print_rslt(rslt); rslt = IMU_configure_sensor(); IMU_print_rslt(rslt); pinMode(LED0, OUTPUT); pinMode(LED1, OUTPUT); pinMode(LED2, OUTPUT); } int main_gyro() { struct bmi2_sens_float sensor_data; int8_t rslt = BMI270.bmi2_get_sensor_float(&sensor_data); IMU_print_rslt(rslt); float IMU_x = sensor_data.acc.x; float IMU_y = sensor_data.acc.y; float IMU_z = sensor_data.acc.z; float IMU_gx = sensor_data.gyr.x; float IMU_gy = sensor_data.gyr.y; float IMU_gz = sensor_data.gyr.z; Serial.print("IMU_x:"); Serial.print(IMU_x); Serial.print(",IMU_y:"); Serial.print(IMU_y); Serial.print(",IMU_z:"); Serial.print(IMU_z); Serial.print(",IMU_gx:"); Serial.print(IMU_gx); Serial.print(",IMU_gy:"); Serial.print(IMU_gy); Serial.print(",IMU_gz:"); Serial.println(IMU_gz); // 上向き判定 bool is_up = (sensor_data.acc.z < 9.0f) && (sensor_data.acc.y < -4.0f); // 下向き判定 bool is_down = (sensor_data.acc.z < 9.0f) && (sensor_data.acc.y > 4.0f); //　どれでもない時水平 bool is_level = (is_up==0)&&(is_down==0); // LED 制御 digitalWrite(LED0, is_level ? HIGH : LOW); digitalWrite(LED1, is_up ? HIGH : LOW); digitalWrite(LED2, is_down ? HIGH : LOW); return (is_up == 1) ? 3 :(is_down == 1) ? 2 : 1 ; // ジャイロ結果を戻す } ``` ##### hand.cpp 義手の指を動作させるモータの動きに関するファイルです。グー・チョキ・パーなどに設定する角度に関しては作成時にある程度調整する必要があります。 ``` #include <Servo.h> Servo finger_1,finger_2; void setup_hand() { finger_1.attach(11); finger_2.attach(8); //初期動作 finger_1.write(150); finger_2.write(120); } void main_hand(int command){ if (command == 1) { //グー finger_1.write(180); finger_2.write(180); } else if (command == 2) { //パー finger_1.write(0); finger_2.write(0); } else if (command == 3) { //チョキ finger_1.write(180); finger_2.write(0); } else { //グーとパーの中間状態 finger_1.write(150); finger_2.write(120); } } ``` ##### データの送信側となるpieeg（ラズパイ4）用のコードライブラリにはbrainflowを使用しています。このライブラリでフィルタリングとデータの取得を行っており、リアルタイムの4秒間の信号を送信しています。 ``` import time from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds from brainflow.data_filter import DataFilter, FilterTypes, AggOperations, NoiseTypes import matplotlib.pyplot as plt import numpy as np from scipy.io import loadmat, savemat import pandas as pd import socket import os #使用するデバイス選択 #board_id = BoardIds.PIEEG_BOARD board_id = BoardIds.UNICORN_BOARD # ============ # UDP 設定 # ============ UDP_IP = "192.168.10.110" # 送信先(spresense)のIP UDP_PORT = 10001 sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) sock.bind(("0.0.0.0", UDP_PORT)) #受信に必須 sock.settimeout(0.5) BUFFER_SIZE = 1000 ch = 8 #送信するチャンネル数(電極数)の指定 sampling_num = 4000 #1回で取得するデータ数の指定 def Convert_int8(x): x = np.array(x) xmin = x.min() xmax = x.max() print(f"before (max){xmax} ,(min){xmin}") x = np.clip(x,-128,127).astype(np.int8) xmin = x.min() xmax = x.max() print(f"after (max){xmax} ,(min){xmin}") return x def main(fig_plot=False,save_mat=False): BoardShim.enable_board_logger() params = BrainFlowInputParams() board = BoardShim(board_id, params) board.prepare_session() board.start_stream() eeg_channels = BoardShim.get_eeg_channels(board_id) sampling_rate = BoardShim.get_sampling_rate(board_id) print(f"board use channel:({eeg_channels})") print(f"sampling_rate:({sampling_rate})") time.sleep(14) #最初に14秒間時間をとってます（一応） while 1 : #============== #データの受け取り #============== try: data = board.get_current_board_data(sampling_num) if data.shape[1] < sampling_num: # サンプル不足の場合はスキップ time.sleep(0.001) continue print(f"brainflow get_data shape :({data.shape})") except KeyboardInterrupt: print("終了します") board.stop_stream() board.release_session() #=================== #各種フィルターの適用 #=================== filter_time = time.time() for _ , channel in enumerate(eeg_channels): #バンドパス DataFilter.perform_bandpass(data[channel], BoardShim.get_sampling_rate(board_id), 0.5, 60.0, 3, FilterTypes.BUTTERWORTH, 0) #電源ノイズの除去 DataFilter.perform_bandstop(data[channel], BoardShim.get_sampling_rate(board_id), 48.0, 52.0, 2, FilterTypes.BUTTERWORTH, 0) #環境ノイズの除去 DataFilter.remove_environmental_noise(data[channel], BoardShim.get_sampling_rate(board_id),NoiseTypes.FIFTY.value) print("フィルタリング時間：",time.time()-filter_time) #====================== # 図にプロット #====================== if fig_plot == True: df = pd.DataFrame(np.transpose(data[:,200:-200])) plt.figure() df[eeg_channels].plot(subplots=True) plt.savefig('after_processing.png') #====================== #.mat形式でのデータの保存 #====================== if save_mat == True: os.makedirs("./mat_data/",exist_ok=True) save_data = {"data":data[eeg_channels]} timestamp = time.strftime('%Y-%m-%d_%H-%M-%S') savemat(f"./mat_data/pieeg_{timestamp}data.mat",save_data) data[1:ch+1] = Convert_int8(data[1:ch+1]) #-128~127にクリップ #====================== #UDP形式での送信 #====================== sock.sendto("udp_start".encode(), (UDP_IP, UDP_PORT)) #送信開始を伝える start_send_time = time.time() for i , channel in enumerate(eeg_channels): send_chunk = np.clip(data[channel][-1000:], -128, 127) #予備でもっかいクリップ + 最新1秒を送信します。 send_chunk = np.array(send_chunk, dtype=np.int8) send_chunk = np.append(send_chunk, np.int8(i)) print(f"Ch{i} last value before send: before({data[channel][-1]}),after({send_chunk[-2]}) 末尾パケット番号({send_chunk[-1]})") #送受信 while True: sock.sendto(send_chunk.tobytes(), (UDP_IP, UDP_PORT)) try: ACK, _ = sock.recvfrom(9) if ACK == b'ok\x00': break except socket.timeout: print("ACKタイムアウト。再送します。") continue #送信チャンネル数に達すると終了 if channel == ch : break send_time = time.time()-start_send_time print(f"Send 時間 : {send_time}") #送信時間 if __name__ == "__main__": print("======開始=====") main(fig_plot=False,save_mat=False) #図にプロットすると時間がかかる print("======終了=====") ``` ## 完成品 ![完成品](https://camo.elchika.com/437632b8f373f2058cdf8dccf3046aea732c77f3/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f32386464663061302d646535642d343463302d623334332d336235663338656331333564/) ## 動作の検証実際に動作検証を行っていきます。以下がその環境となっております。電力が足りないのか、ジャイロセンサーやモータの動きがおかしくなったりしますが、リセットをかけたりすると動いたりするので、今回はこの状態で動作を検証しました。 ![動作時の環境](https://camo.elchika.com/8b41a458e27213b3c94188bf8d9313e13588bead/687474703a2f2f73746f726167652e676f6f676c65617069732e636f6d2f656c6368696b612f76312f757365722f39323964323734662d343865632d343964332d396166342d3063346433643639666634342f38346465393037642d353463302d343136352d386661392d646131613262363438363864/) ### ジャイロ動作についての確認角度を変えることでLEDの点灯が変化しているのが確認できました。 @[youtube](https://youtu.be/S-4Duejmkgs) ### 筋電位で動かしたとき何もしていない状態（＝０）とグー（＝１）を検出させて動かしてみました。動作に間隔をあけても、ちゃんとそれに応じたように、何も動作していないことが継続されており、ある程度ですが認識している感じはしました。 @[youtube](https://youtu.be/G90LGy9RloQ) ### 脳波で動かしたとき次に、脳波で動かしてみた感じの結果について示します。以下が実際に動かした際の動画になりますが、動かせている感じはあまりしなかったです。もともと分類精度が低いのでうまくいっている感じがなく、精度やユーザビリティの向上は今後の課題だと考えています。 @[youtube](https://youtu.be/Mq1zITvid5s) ### 動かしてみた感じ現状だとある程度ですが認識しているのかもしれませんが、実用的とは程遠い感じになりました。むずかしい... より多くの違いを認識した動作の実現や安定性など課題が山ほどあります。 ## 感想と今後いろいろ試行錯誤しながらやりましたが、かなり難易度が高く、多くの動作を義手に実現させるには今の自分で難しいと感じました。非常に良い勉強になりました！！ **この度は、拝見していただき誠にありがとうございました！！** 　