--- comments: true description: Workouts Monitoring Using Ultralytics YOLOv8 keywords: Ultralytics, YOLOv8, Object Detection, Pose Estimation, PushUps, PullUps, Ab workouts, Notebook, IPython Kernel, CLI, Python SDK --- # Workouts Monitoring using Ultralytics YOLOv8 🚀 Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike.



Watch: Workouts Monitoring using Ultralytics YOLOv8 | Pushups, Pullups, Ab Workouts

## Advantages of Workouts Monitoring? - **Optimized Performance:** Tailoring workouts based on monitoring data for better results. - **Goal Achievement:** Track and adjust fitness goals for measurable progress. - **Personalization:** Customized workout plans based on individual data for effectiveness. - **Health Awareness:** Early detection of patterns indicating health issues or over-training. - **Informed Decisions:** Data-driven decisions for adjusting routines and setting realistic goals. ## Real World Applications | Workouts Monitoring | Workouts Monitoring | |:----------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------:| | ![PushUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cf016a41-589f-420f-8a8c-2cc8174a16de) | ![PullUps Counting](https://github.com/RizwanMunawar/ultralytics/assets/62513924/cb20f316-fac2-4330-8445-dcf5ffebe329) | | PushUps Counting | PullUps Counting | !!! Example "Workouts Monitoring Example" === "Workouts Monitoring" ```python from ultralytics import YOLO from ultralytics.solutions import ai_gym import cv2 model = YOLO("yolov8n-pose.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) gym_object = ai_gym.AIGym() # init AI GYM module gym_object.set_args(line_thickness=2, view_img=True, pose_type="pushup", kpts_to_check=[6, 8, 10]) frame_count = 0 while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break frame_count += 1 results = model.track(im0, verbose=False) # Tracking recommended #results = model.predict(im0) # Prediction also supported im0 = gym_object.start_counting(im0, results, frame_count) cv2.destroyAllWindows() ``` === "Workouts Monitoring with Save Output" ```python from ultralytics import YOLO from ultralytics.solutions import ai_gym import cv2 model = YOLO("yolov8n-pose.pt") cap = cv2.VideoCapture("path/to/video/file.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) video_writer = cv2.VideoWriter("workouts.avi", cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) gym_object = ai_gym.AIGym() # init AI GYM module gym_object.set_args(line_thickness=2, view_img=True, pose_type="pushup", kpts_to_check=[6, 8, 10]) frame_count = 0 while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break frame_count += 1 results = model.track(im0, verbose=False) # Tracking recommended #results = model.predict(im0) # Prediction also supported im0 = gym_object.start_counting(im0, results, frame_count) video_writer.write(im0) cv2.destroyAllWindows() video_writer.release() ``` ???+ tip "Support" "pushup", "pullup" and "abworkout" supported ### KeyPoints Map ![keyPoints Order Ultralytics YOLOv8 Pose](https://github.com/ultralytics/ultralytics/assets/62513924/f45d8315-b59f-47b7-b9c8-c61af1ce865b) ### Arguments `set_args` | Name | Type | Default | Description | |-------------------|--------|----------|----------------------------------------------------------------------------------------| | `kpts_to_check` | `list` | `None` | List of three keypoints index, for counting specific workout, followed by keypoint Map | | `view_img` | `bool` | `False` | Display the frame with counts | | `line_thickness` | `int` | `2` | Increase the thickness of count value | | `pose_type` | `str` | `pushup` | Pose that need to be monitored, `pullup` and `abworkout` also supported | | `pose_up_angle` | `int` | `145` | Pose Up Angle value | | `pose_down_angle` | `int` | `90` | Pose Down Angle value | ### Arguments `model.predict` | Name | Type | Default | Description | |-----------------|----------------|------------------------|----------------------------------------------------------------------------| | `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | | `conf` | `float` | `0.25` | object confidence threshold for detection | | `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | | `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | | `half` | `bool` | `False` | use half precision (FP16) | | `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | | `max_det` | `int` | `300` | maximum number of detections per image | | `vid_stride` | `bool` | `False` | video frame-rate stride | | `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | | `visualize` | `bool` | `False` | visualize model features | | `augment` | `bool` | `False` | apply image augmentation to prediction sources | | `agnostic_nms` | `bool` | `False` | class-agnostic NMS | | `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | | `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | | `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers | ### Arguments `model.track` | Name | Type | Default | Description | |-----------|---------|----------------|-------------------------------------------------------------| | `source` | `im0` | `None` | source directory for images or videos | | `persist` | `bool` | `False` | persisting tracks between frames | | `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' | | `conf` | `float` | `0.3` | Confidence Threshold | | `iou` | `float` | `0.5` | IOU Threshold | | `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | | `verbose` | `bool` | `True` | Display the object tracking results |