位置http://localhost:8888/notebooks/Untitled1-Copy1.ipynb
# -*- 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()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)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[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)
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
- 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。
- 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。
