diff --git a/motion_app.py b/motion_app.py
index 6255d72..fee0763 100644
--- a/motion_app.py
+++ b/motion_app.py
@@ -12,12 +12,14 @@ from audio_player import AudioPlayer
 from pose_analyzer import PoseSimilarityAnalyzer
 from config import REALSENSE_AVAILABLE
 
-def draw_skeleton_with_similarity(img, keypoints, scores, joint_similarities=None, openpose_skeleton=True, kpt_thr=0.43, line_width=1):
+def draw_skeleton_with_similarity(img, keypoints, scores, joint_similarities=None, openpose_skeleton=True, kpt_thr=0.43, line_width=2):
     """
     自定义骨骼绘制函数，根据关节相似度设置颜色
     相似度 > 90: 绿色
     相似度 80-90: 黄色
     相似度 < 80: 红色
+    
+    支持多人检测，将绘制所有检测到的人体骨骼
     """
     if keypoints is None or len(keypoints) == 0:
         return img
@@ -40,44 +42,52 @@ def draw_skeleton_with_similarity(img, keypoints, scores, joint_similarities=Non
     # 骨骼的默认颜色
     default_color = (0, 255, 255)  # 黄色
     
-    person_kpts = keypoints[0] if len(keypoints.shape) > 2 else keypoints
-    person_scores = scores[0] if len(scores.shape) > 1 else scores
+    # 确保keypoints和scores的形状正确，处理多人情况
+    if len(keypoints.shape) == 2:
+        keypoints = keypoints[None, :, :]
+        scores = scores[None, :, :]
     
-    # 绘制骨骼
-    for limb_id, limb in enumerate(skeleton):
-        joint_a, joint_b = limb
+    # 遍历所有人
+    num_instances = keypoints.shape[0]
+    for person_idx in range(num_instances):
+        person_kpts = keypoints[person_idx]
+        person_scores = scores[person_idx]
         
-        if joint_a >= len(person_scores) or joint_b >= len(person_scores):
-            continue
+        # 绘制骨骼
+        for limb_id, limb in enumerate(skeleton):
+            joint_a, joint_b = limb
             
-        if person_scores[joint_a] < kpt_thr or person_scores[joint_b] < kpt_thr:
-            continue
+            if joint_a >= len(person_scores) or joint_b >= len(person_scores):
+                continue
+                
+            if person_scores[joint_a] < kpt_thr or person_scores[joint_b] < kpt_thr:
+                continue
+                
+            x_a, y_a = person_kpts[joint_a]
+            x_b, y_b = person_kpts[joint_b]
             
-        x_a, y_a = person_kpts[joint_a]
-        x_b, y_b = person_kpts[joint_b]
+            # 确定线条颜色
+            color = default_color
+            if joint_similarities is not None:
+                # 检查这个连接是否有对应的关节角度
+                if (joint_a, joint_b) in joint_to_angle_mapping:
+                    angle_name = joint_to_angle_mapping[(joint_a, joint_b)]
+                    if angle_name in joint_similarities:
+                        similarity = joint_similarities[angle_name]
+                        if similarity > 90:
+                            color = (0, 255, 0)  # 绿色
+                        elif similarity > 80:
+                            color = (0, 255, 255)  # 黄色
+                        else:
+                            color = (0, 0, 255)  # 红色
+            
+            cv2.line(img, (int(x_a), int(y_a)), (int(x_b), int(y_b)), color, thickness=line_width)
         
-        # 确定线条颜色
-        color = default_color
-        if joint_similarities is not None:
-            # 检查这个连接是否有对应的关节角度
-            if (joint_a, joint_b) in joint_to_angle_mapping:
-                angle_name = joint_to_angle_mapping[(joint_a, joint_b)]
-                if angle_name in joint_similarities:
-                    similarity = joint_similarities[angle_name]
-                    if similarity > 90:
-                        color = (0, 255, 0)  # 绿色
-                    elif similarity > 80:
-                        color = (0, 255, 255)  # 黄色
-                    else:
-                        color = (0, 0, 255)  # 红色
-        
-        cv2.line(img, (int(x_a), int(y_a)), (int(x_b), int(y_b)), color, thickness=line_width)
-    
-    # 绘制关键点
-    for kpt_id, (x, y) in enumerate(person_kpts):
-        if person_scores[kpt_id] < kpt_thr:
-            continue
-        cv2.circle(img, (int(x), int(y)), 3, (255, 0, 255), -1)
+        # 绘制关键点
+        for kpt_id, (x, y) in enumerate(person_kpts):
+            if person_scores[kpt_id] < kpt_thr:
+                continue
+            cv2.circle(img, (int(x), int(y)), 3, (255, 0, 255), -1)
     
     return img