ultralytics/docs/en/guides/workouts-monitoring.md

comments	description	keywords
true	Workouts Monitoring Using Ultralytics YOLOv8	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 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.

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 overtraining.
• Informed Decisions: Data-driven decisions for adjusting routines and setting realistic goals.

Real World Applications

Workouts Monitoring	Workouts Monitoring
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.predict(im0, verbose=False)
        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.predict(im0, verbose=False)
        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

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