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

comments	description	keywords
true	Optimize your fitness routine with real-time workouts monitoring using Ultralytics YOLOv8. Track and improve your exercise form and performance.	workouts monitoring, Ultralytics YOLOv8, pose estimation, fitness tracking, exercise assessment, real-time feedback, exercise form, performance metrics

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.

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	PullUps Counting

!!! Example "Workouts Monitoring Example"

=== "Workouts Monitoring"

    ```python
    import cv2
    from ultralytics import YOLO, solutions

    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 = solutions.AIGym(
        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
    import cv2
    from ultralytics import YOLO, solutions

    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 = solutions.AIGym(
        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

Arguments AIGym

Name	Type	Default	Description
kpts_to_check	list	None	List of three keypoints index, for counting specific workout, followed by keypoint Map
line_thickness	int	2	Thickness of the lines drawn.
view_img	bool	False	Flag to display the image.
pose_up_angle	float	145.0	Angle threshold for the 'up' pose.
pose_down_angle	float	90.0	Angle threshold for the 'down' pose.
pose_type	str	pullup	Type of pose to detect ('pullup', pushup, abworkout, squat).

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