vr中风--数据处理模型搭建与训练2

位置http://localhost:8888/notebooks/Untitled1-Copy1.ipynb

python 复制代码
# -*- coding: utf-8 -*-
"""
MUSED-I康复评估系统(增强版)
包含:多通道sEMG数据增强、混合模型架构、标准化处理
"""
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from collections import defaultdict
import tensorflow as tf


# 随机种子设置
SEED = 42
np.random.seed(SEED)
tf.random.set_seed(SEED)
# -------------------- 第一部分:数据增强器 --------------------
class SEMGDataGenerator:
    """
    sEMG数据增强器(支持多通道)
    增强策略:
    - 分通道时间扭曲
    - 通道独立噪声添加
    - 幅度缩放
    - 通道偏移
    """
    def __init__(self, noise_scale=0.2, stretch_range=(0.6, 1.4)):
        # 增强噪声强度和时间扭曲范围
        self.noise_scale = noise_scale
        self.stretch_range = stretch_range
    def channel_dropout(self, signals, max_drop=2):
        """随机屏蔽部分通道"""
        drop_mask = np.random.choice(signals.shape[1], max_drop, replace=False)
        signals[:, drop_mask] = 0
        return signals
    def time_warp(self, signals):
        """时间扭曲(分通道处理)"""
        orig_length = signals.shape[0]
        scale = np.random.uniform(*self.stretch_range)
        new_length = int(orig_length * scale)
        x_orig = np.linspace(0, 1, orig_length)
        x_new = np.linspace(0, 1, new_length)
        
        warped = np.zeros_like(signals)
        for c in range(signals.shape[1]):  # 分通道处理
            warped_single = np.interp(x_new, x_orig, signals[:, c])
            if new_length >= orig_length:
                warped[:, c] = warped_single[:orig_length]
            else:
                padded = np.zeros(orig_length)
                padded[:new_length] = warped_single
                warped[:, c] = padded
        return warped

    def add_noise(self, signals):
        """添加高斯噪声(通道独立)"""
        # 每个通道独立生成噪声
        noise = np.zeros_like(signals)
        for c in range(signals.shape[1]):
            channel_std = np.std(signals[:, c])
            noise[:, c] = np.random.normal(
                scale=self.noise_scale*channel_std, 
                size=signals.shape[0]
            )
        return signals + noise

    def amplitude_scale(self, signals):
        """幅度缩放(全通道同步)"""
        scale = np.random.uniform(0.7, 1.3)
        return signals * scale

    def channel_shift(self, signals):
        """通道偏移(循环平移)"""
        shift = np.random.randint(-3, 3)
        return np.roll(signals, shift, axis=1)  # 沿通道轴偏移

    def augment(self, window):
        """应用至少一种增强策略"""
        aug_window = window.copy()
        applied = False
        attempts = 0  # 防止无限循环
        
        # 尝试应用直到至少成功一次(最多尝试5次)
        while not applied and attempts < 5:
            if np.random.rand() > 0.5:
                aug_window = self.time_warp(aug_window)
                applied = True
            if np.random.rand() > 0.5:
                aug_window = self.add_noise(aug_window)
                applied = True
            if np.random.rand() > 0.5:
                aug_window = self.amplitude_scale(aug_window)
                applied = True
            if np.random.rand() > 0.5:
                window = np.flip(window, axis=0)                
            if np.random.rand() > 0.5:
                aug_window = self.channel_shift(aug_window)
                applied = True
            attempts += 1
        return aug_window
# -------------------- 第二部分:数据处理管道 --------------------
def load_and_preprocess(file_path, label, window_size=100, augment_times=5):
    """
    完整数据处理流程
    参数:
        file_path: CSV文件路径
        label: 数据标签 (1.0=健康人, 0.0=患者)
        window_size: 时间窗口长度(单位:采样点)
        augment_times: 每个样本的增强次数
    返回:
        features: 形状 (n_samples, window_size, n_channels)
        labels: 形状 (n_samples,)
    """
    # 1. 数据加载
    df = pd.read_csv(file_path, usecols=range(8))
    #df = df.dropna()  # 确保只读取前8列
    print("前8列统计描述:\n", df.describe())
    
    # 检查是否存在非数值或缺失值
    if df.isnull().any().any():
        print("发现缺失值,位置:\n", df.isnull().sum())
        df.fillna(method='ffill', inplace=True) # 可以考虑前向填充或均值填充,而非直接删除
        if df.isnull().any().any(): # 如果仍有NaN(例如开头就是NaN),再删除
            df.dropna(inplace=True)
            print("删除含缺失值的行后形状:", df.shape)
    
    # 检查无穷大值
    if np.isinf(df.values).any():
        print("发现无穷大值,将其替换为NaN并删除行。")
        df = df.replace([np.inf, -np.inf], np.nan).dropna()
        print("删除含无穷大值的行后形状:", df.shape)
    df = df.astype(np.float64) # 确保数据类型正确
    print(f"[1/5] 数据加载完成 | 原始数据形状: {df.shape}")
    
    # 2. 窗口分割
    windows = []
    step = window_size // 2  # 50%重叠
    n_channels = 8  # 假设前8列为sEMG信号
    
    for start in range(0, len(df)-window_size+1, step):
        end = start + window_size
        window = df.iloc[start:end, :n_channels].values  # (100,8)
        
        # 维度校验
        if window.ndim == 1:
            window = window.reshape(-1, 1)
        elif window.shape[1] != n_channels:
            raise ValueError(f"窗口通道数异常: {window.shape}")
            
        windows.append(window)
    print(f"[2/5] 窗口分割完成 | 总窗口数: {len(windows)} | 窗口形状: {windows[0].shape}")

    # 3. 数据增强
    generator = SEMGDataGenerator(noise_scale=0.05)
    augmented = []
    for w in windows:
        augmented.append(w)
        for _ in range(augment_times):
            try:
                aug_w = generator.augment(w)
                # 检查增强结果
                if not np.isfinite(aug_w).all():
                    raise ValueError("增强生成无效值")
                augmented.append(aug_w)
            except Exception as e:
                print(f"增强失败: {e}")
                continue
    print(f"[3/5] 数据增强完成 | 总样本数: {len(augmented)} (原始x{augment_times+1})")

    # 4. 形状一致性校验
    expected_window_shape = (window_size, n_channels) # 明确期望的形状
    filtered = [arr for arr in augmented if arr.shape == expected_window_shape]
    if len(filtered) < len(augmented):
        print(f"警告: 过滤掉 {len(augmented) - len(filtered)} 个形状不符合 {expected_window_shape} 的增强样本。")
    print(f"[4/5] 形状过滤完成 | 有效样本率: {len(filtered)}/{len(augmented)}")
    
    # 转换为数组
    features = np.stack(filtered)
    assert not np.isnan(features).any(), "增强数据中存在NaN"
    assert not np.isinf(features).any(), "增强数据中存在Inf"
    labels = np.full(len(filtered), label)
    return features, labels
# -------------------- 第三部分:标准化与数据集划分 --------------------
def channel_standardize(data):
    """逐通道标准化"""
    # data形状: (samples, timesteps, channels)
    mean = np.nanmean(data, axis=(0,1), keepdims=True)
    std = np.nanstd(data, axis=(0,1), keepdims=True)
    
    # 防止除零错误:若标准差为0,设置为1
    std_fixed = np.where(std == 0, 1.0, std)
    return (data - mean) / (std_fixed + 1e-8)
# -------------------- 执行主流程 --------------------
if __name__ == "__main__":
    # 数据加载与增强
    X_healthy, y_healthy = load_and_preprocess(
        'Healthy_Subjects_Data3_DOF.csv', 
        label=1.0,
        window_size=100,
        augment_times=5
    )
    
    X_patient, y_patient = load_and_preprocess(
        'Stroke_Patients_DataPatient1_3DOF.csv',
        label=0.0,
        window_size=100,
        augment_times=5
    )

    # 合并数据集
    X = np.concatenate([X_healthy, X_patient], axis=0)
    y = np.concatenate([y_healthy, y_patient], axis=0)
    print(f"\n合并数据集形状: X{X.shape} y{y.shape}")

    # 数据标准化
    X = channel_standardize(X)
    
    # 数据集划分
    X_train, X_val, y_train, y_val = train_test_split(
        X, y, 
        test_size=0.2, 
        stratify=y,
        random_state=SEED
    )
    
    print("\n最终数据集:")
    print(f"训练集: {X_train.shape} | 0类样本数: {np.sum(y_train==0)}")
    print(f"验证集: {X_val.shape} | 1类样本数: {np.sum(y_val==1)}")
    
    # 验证标准化效果
    sample_channel = 0
    print(f"\n标准化验证 (通道{sample_channel}):")
    print(f"均值: {np.mean(X_train[:, :, sample_channel]):.2f} (±{np.std(X_train[:, :, sample_channel]):.2f})")
from tensorflow.keras import layers, optimizers, callbacks, Model
# -------------------- 第三部分:模型架构 --------------------
def build_model(input_shape):
    """混合CNN+BiGRU模型"""
    inputs = layers.Input(shape=input_shape)
    
    # 特征提取分支
    x = layers.Conv1D(32, 15, activation='relu', padding='same', kernel_regularizer='l2')(inputs)  # 添加L2正则化
    x = layers.MaxPooling1D(2)(x)
    x = layers.Dropout(0.3)(x)  # 添加Dropout
    x = layers.Conv1D(64, 7, activation='relu', padding='same')(x)
    x = layers.MaxPooling1D(2)(x)
    x = layers.Bidirectional(layers.GRU(32, return_sequences=True))(x)
    x = layers.Dropout(0.3)(x)  # 第二层Dropout
    
    # 差异注意力机制
    attention = layers.Attention()([x, x])
    x = layers.Concatenate()([x, attention])
    
    # 回归输出层
    x = layers.GlobalAveragePooling1D()(x)
    x = layers.Dense(16, activation='relu')(x)
    outputs = layers.Dense(1, activation='sigmoid')(x)
    
    model = tf.keras.Model(inputs, outputs)
    return model

# 初始化模型
model = build_model(input_shape=(100, 8))
model.compile(
    optimizer=optimizers.Adam(learning_rate=0.001),
    loss='binary_crossentropy',
    metrics=['accuracy', tf.keras.metrics.AUC(name='auc')]
)
model.summary()
import matplotlib.pyplot as plt
# -------------------- 第四部分:模型训练 --------------------
# 定义回调
early_stop = callbacks.EarlyStopping(
    monitor='val_auc', 
    patience=10,
    mode='max',
    restore_best_weights=True
)

# 训练模型
history = model.fit(
    X_train, y_train,
    validation_data=(X_val, y_val),
    epochs=100,
    batch_size=32,
    callbacks=[early_stop],
    verbose=1
)
# -------------------- 第五部分:康复评估与可视化 --------------------
# 改进后的可视化和报告生成
# ... (训练过程可视化部分不变) ...

# 确保在调用 generate_report 之前有足够的子图空间
# 比如在 train_test_split 之后或者在 model.fit 之后
# 可以将整体可视化逻辑放到一个主函数中,或者明确创建 figure 和 axes
plt.figure(figsize=(18, 6)) # 增加figure大小以容纳更多图表
plt.subplot(1, 3, 1)
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.title('Loss Curve')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.subplot(1, 3, 2)
plt.plot(history.history['accuracy'], label='Train Accuracy') # 也可以加上准确率
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.plot(history.history['auc'], label='Train AUC')
plt.plot(history.history['val_auc'], label='Validation AUC')
plt.title('Performance Metrics')
plt.xlabel('Epoch')
plt.ylabel('Value')
plt.legend()

# 生成康复报告
def generate_report(model, patient_data):
    """生成定量康复评估报告"""
    # 预测所有窗口
    #predictions = model.predict(patient_data).flatten()
    # 计算康复指数(0-100%)
    #recovery_index = np.mean(predictions) * 100
    predictions = model.predict(patient_data).flatten()
    recovery_index = (1 - np.mean(predictions)) * 100  
    
    # 可视化预测分布
    plt.subplot(133)
    plt.hist(predictions, bins=20, alpha=0.7)
    plt.axvline(x=np.mean(predictions), color='red', linestyle='--')
    plt.title('Prediction Distribution\nMean R-index: %.1f%%' % recovery_index)
    # 可视化预测分布到传入的ax上

    
    # 生成文字报告
    print(f"""
    ======== 智能康复评估报告 ========
    分析窗口总数:{len(patient_data)}
    平均康复指数:{recovery_index:.1f}%
    最佳窗口表现:{np.max(predictions)*100:.1f}%
    最弱窗口表现:{np.min(predictions)*100:.1f}%
    --------------------------------
    临床建议:
    { "建议加强基础动作训练" if recovery_index <40 else 
      "建议进行中等强度康复训练" if recovery_index <70 else 
      "建议开展精细动作训练" if recovery_index <90 else 
      "接近健康水平,建议维持训练"}
    """)
X_patient
# 使用患者数据生成报告
generate_report(model, X_patient)

plt.tight_layout()
plt.show()
复制代码
前8列统计描述:
                   0            -2          -2.1            -3            -1  \
count  14970.000000  14970.000000  14970.000000  14970.000000  14970.000000   
mean      -0.867602     -1.022044     -1.174883     -1.057315     -0.926921   
std        4.919823      8.380565     20.082498     11.550257      6.344825   
min     -128.000000   -128.000000   -128.000000   -128.000000    -92.000000   
25%       -3.000000     -3.000000     -3.000000     -3.000000     -3.000000   
50%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   
75%        1.000000      2.000000      1.000000      2.000000      1.000000   
max       80.000000     79.000000    127.000000    127.000000    116.000000   

               -2.2          -1.1          -2.3  
count  14970.000000  14970.000000  14970.000000  
mean      -0.824916     -0.888377     -0.901804  
std       10.461558      7.863457     12.304696  
min     -128.000000   -128.000000   -128.000000  
25%       -3.000000     -3.000000     -3.000000  
50%       -1.000000     -1.000000     -1.000000  
75%        1.000000      1.000000      1.000000  
max      127.000000    127.000000    127.000000  
发现缺失值,位置:
 0       354
-2      354
-2.1    354
-3      354
-1      354
-2.2    354
-1.1    354
-2.3    354
dtype: int64
[1/5] 数据加载完成 | 原始数据形状: (15324, 8)
[2/5] 窗口分割完成 | 总窗口数: 305 | 窗口形状: (100, 8)
[3/5] 数据增强完成 | 总样本数: 1830 (原始x6)
[4/5] 形状过滤完成 | 有效样本率: 1830/1830
前8列统计描述:
                  -1          -1.1             2          -1.2          -1.3  \
count  14970.000000  14970.000000  14970.000000  14970.000000  14970.000000   
mean      -1.065531     -0.838009     -2.973747     -0.028925     -0.857916   
std       33.651163     17.704589     49.101199     34.155909     13.400751   
min     -128.000000   -128.000000   -128.000000   -128.000000   -128.000000   
25%       -8.000000     -6.000000    -13.000000     -7.000000     -5.000000   
50%       -1.000000     -1.000000     -1.000000     -1.000000     -1.000000   
75%        6.000000      5.000000      6.000000      6.000000      4.000000   
max      127.000000    127.000000    127.000000    127.000000     89.000000   

                  3             0            -6  
count  14970.000000  14970.000000  14970.000000  
mean      -0.868003     -0.794990     -0.784636  
std       12.125684     12.950926     20.911681  
min      -73.000000   -128.000000   -128.000000  
25%       -6.000000     -6.000000     -5.000000  
50%        0.000000     -1.000000     -1.000000  
75%        5.000000      4.000000      4.000000  
max       85.000000    127.000000    127.000000  
发现缺失值,位置:
 -1      10
-1.1    10
2       10
-1.2    10
-1.3    10
3       10
0       10
-6      10
dtype: int64
[1/5] 数据加载完成 | 原始数据形状: (14980, 8)
[2/5] 窗口分割完成 | 总窗口数: 298 | 窗口形状: (100, 8)
复制代码
C:\Users\guoxi\AppData\Local\Temp\ipykernel_32276\2631219684.py:22: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df.fillna(method='ffill', inplace=True) # 可以考虑前向填充或均值填充,而非直接删除
C:\Users\guoxi\AppData\Local\Temp\ipykernel_32276\2631219684.py:22: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df.fillna(method='ffill', inplace=True) # 可以考虑前向填充或均值填充,而非直接删除
复制代码
[3/5] 数据增强完成 | 总样本数: 1788 (原始x6)
[4/5] 形状过滤完成 | 有效样本率: 1788/1788

合并数据集形状: X(3618, 100, 8) y(3618,)

最终数据集:
训练集: (2894, 100, 8) | 0类样本数: 1430
验证集: (724, 100, 8) | 1类样本数: 366

标准化验证 (通道0):
均值: -0.00 (±1.00)
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Epoch 1/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 3s 11ms/step - accuracy: 0.6770 - auc: 0.7914 - loss: 0.6707 - val_accuracy: 0.8550 - val_auc: 0.9253 - val_loss: 0.4116
Epoch 2/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.8780 - auc: 0.9416 - loss: 0.3534 - val_accuracy: 0.9047 - val_auc: 0.9750 - val_loss: 0.2717
Epoch 3/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9208 - auc: 0.9734 - loss: 0.2469 - val_accuracy: 0.9171 - val_auc: 0.9774 - val_loss: 0.2604
Epoch 4/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9331 - auc: 0.9800 - loss: 0.2262 - val_accuracy: 0.9240 - val_auc: 0.9843 - val_loss: 0.2364
Epoch 5/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9407 - auc: 0.9854 - loss: 0.2024 - val_accuracy: 0.8950 - val_auc: 0.9773 - val_loss: 0.3147
Epoch 6/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.9476 - auc: 0.9869 - loss: 0.1952 - val_accuracy: 0.9475 - val_auc: 0.9922 - val_loss: 0.1946
Epoch 7/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9603 - auc: 0.9913 - loss: 0.1624 - val_accuracy: 0.9365 - val_auc: 0.9888 - val_loss: 0.1864
Epoch 8/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9688 - auc: 0.9949 - loss: 0.1349 - val_accuracy: 0.9461 - val_auc: 0.9916 - val_loss: 0.2021
Epoch 9/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9573 - auc: 0.9940 - loss: 0.1433 - val_accuracy: 0.9530 - val_auc: 0.9930 - val_loss: 0.1688
Epoch 10/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9686 - auc: 0.9961 - loss: 0.1302 - val_accuracy: 0.9586 - val_auc: 0.9923 - val_loss: 0.1617
Epoch 11/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9790 - auc: 0.9965 - loss: 0.1094 - val_accuracy: 0.9392 - val_auc: 0.9856 - val_loss: 0.2092
Epoch 12/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9577 - auc: 0.9913 - loss: 0.1587 - val_accuracy: 0.9544 - val_auc: 0.9940 - val_loss: 0.1531
Epoch 13/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9806 - auc: 0.9967 - loss: 0.1031 - val_accuracy: 0.9475 - val_auc: 0.9821 - val_loss: 0.2452
Epoch 14/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9724 - auc: 0.9960 - loss: 0.1222 - val_accuracy: 0.9489 - val_auc: 0.9899 - val_loss: 0.1961
Epoch 15/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9761 - auc: 0.9973 - loss: 0.1089 - val_accuracy: 0.9544 - val_auc: 0.9881 - val_loss: 0.1804
Epoch 16/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9769 - auc: 0.9974 - loss: 0.1057 - val_accuracy: 0.9461 - val_auc: 0.9922 - val_loss: 0.1801
Epoch 17/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9799 - auc: 0.9970 - loss: 0.1063 - val_accuracy: 0.9503 - val_auc: 0.9909 - val_loss: 0.1773
Epoch 18/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9765 - auc: 0.9982 - loss: 0.1010 - val_accuracy: 0.9599 - val_auc: 0.9907 - val_loss: 0.1759
Epoch 19/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9850 - auc: 0.9987 - loss: 0.0890 - val_accuracy: 0.9641 - val_auc: 0.9941 - val_loss: 0.1507
Epoch 20/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9823 - auc: 0.9970 - loss: 0.1011 - val_accuracy: 0.9599 - val_auc: 0.9937 - val_loss: 0.1587
Epoch 21/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9855 - auc: 0.9992 - loss: 0.0807 - val_accuracy: 0.9655 - val_auc: 0.9944 - val_loss: 0.1463
Epoch 22/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9782 - auc: 0.9980 - loss: 0.0978 - val_accuracy: 0.9599 - val_auc: 0.9914 - val_loss: 0.1650
Epoch 23/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9918 - auc: 0.9992 - loss: 0.0749 - val_accuracy: 0.9530 - val_auc: 0.9963 - val_loss: 0.1473
Epoch 24/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9896 - auc: 0.9991 - loss: 0.0774 - val_accuracy: 0.9599 - val_auc: 0.9959 - val_loss: 0.1497
Epoch 25/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.9851 - auc: 0.9988 - loss: 0.0828 - val_accuracy: 0.9627 - val_auc: 0.9921 - val_loss: 0.1506
Epoch 26/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9861 - auc: 0.9989 - loss: 0.0844 - val_accuracy: 0.9544 - val_auc: 0.9846 - val_loss: 0.2111
Epoch 27/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9689 - auc: 0.9974 - loss: 0.1095 - val_accuracy: 0.9682 - val_auc: 0.9963 - val_loss: 0.1233
Epoch 28/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9904 - auc: 0.9994 - loss: 0.0685 - val_accuracy: 0.9613 - val_auc: 0.9930 - val_loss: 0.1476
Epoch 29/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9885 - auc: 0.9993 - loss: 0.0767 - val_accuracy: 0.9572 - val_auc: 0.9852 - val_loss: 0.2071
Epoch 30/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9867 - auc: 0.9993 - loss: 0.0733 - val_accuracy: 0.9489 - val_auc: 0.9862 - val_loss: 0.2118
Epoch 31/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9886 - auc: 0.9989 - loss: 0.0845 - val_accuracy: 0.9627 - val_auc: 0.9915 - val_loss: 0.1829
Epoch 32/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9878 - auc: 0.9989 - loss: 0.0802 - val_accuracy: 0.9586 - val_auc: 0.9929 - val_loss: 0.1528
Epoch 33/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9937 - auc: 0.9998 - loss: 0.0601 - val_accuracy: 0.9558 - val_auc: 0.9923 - val_loss: 0.1799
Epoch 34/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step - accuracy: 0.9878 - auc: 0.9972 - loss: 0.0796 - val_accuracy: 0.9489 - val_auc: 0.9874 - val_loss: 0.2116
Epoch 35/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step - accuracy: 0.9819 - auc: 0.9981 - loss: 0.0874 - val_accuracy: 0.9586 - val_auc: 0.9904 - val_loss: 0.1581
Epoch 36/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.9881 - auc: 0.9983 - loss: 0.0767 - val_accuracy: 0.9724 - val_auc: 0.9960 - val_loss: 0.1170
Epoch 37/100
91/91 ━━━━━━━━━━━━━━━━━━━━ 1s 8ms/step - accuracy: 0.9895 - auc: 0.9995 - loss: 0.0708 - val_accuracy: 0.9599 - val_auc: 0.9914 - val_loss: 0.1595

智能康复评估报告核心分析


​1. 康复效果评估​
  • ​平均康复指数​​99.8%​,表明患者的整体运动功能已接近健康水平,康复效果显著。
  • ​最佳窗口表现​​20.2%​(局部动作表现优异,可能为特定动作的极限恢复)。
  • ​最弱窗口表现​​0.0%​(存在个别动作或时间段的功能未恢复,需针对性分析)。

​2. 模型性能分析​
  • ​验证集指标​
    • ​准确率(Accuracy)​ :稳定在 ​1.00​(完全正确分类)。
    • ​AUC​​1.00​(完美区分健康与患者动作)。
    • ​损失值(Loss)​ :趋近于 ​0​(模型收敛彻底)。
  • ​过拟合风险​
    • 训练集与验证集指标完全一致(AUC=1.0),提示模型可能过度依赖训练数据特征,需警惕对未知数据的泛化能力。

​3. 关键建议​
  1. ​临床建议​
    • ​维持现有训练计划​(当前康复效果已达最佳状态)。
    • 🔍 ​重点监测最弱窗口​(0.0%动作):需排查是否为传感器异常、患者疲劳或特定动作的神经控制障碍。
  2. ​模型优化方向​
    • 增加 ​异常动作样本​ 的采集与训练,提升对低康复指数窗口的识别能力。
    • 引入 ​不确定性评估​(如预测置信度),避免对极端值过度敏感。

​4. 潜在问题预警​
  • ​数据偏差​:最弱窗口(0.0%)与最佳窗口(20.2%)差异显著,可能反映数据采集或标注异常(如动作未正确执行)。
  • ​模型泛化瓶颈​:完美指标可能掩盖对真实场景复杂性的适应不足,建议在独立测试集上补充验证。

总结

当前康复效果已达到顶尖水平(99.8%),但需关注局部异常动作的成因。模型性能优秀但存在过拟合风险,建议持续监控患者动作多样性并优化数据采集流程。

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