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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],
    )

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        results = model.track(im0, verbose=False)  # Tracking recommended
        # results = model.predict(im0)  # Prediction also supported
        im0 = gym_object.start_counting(im0, results)

    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],
    )

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        results = model.track(im0, verbose=False)  # Tracking recommended
        # results = model.predict(im0)  # Prediction also supported
        im0 = gym_object.start_counting(im0, results)
        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

FAQ

How do I monitor my workouts using Ultralytics YOLOv8?

To monitor your workouts using Ultralytics YOLOv8, you can utilize the pose estimation capabilities to track and analyze key body landmarks and joints in real-time. This allows you to receive instant feedback on your exercise form, count repetitions, and measure performance metrics. You can start by using the provided example code for pushups, pullups, or ab workouts as shown:

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],
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    results = model.track(im0, verbose=False)
    im0 = gym_object.start_counting(im0, results)

cv2.destroyAllWindows()

For further customization and settings, you can refer to the AIGym section in the documentation.

What are the benefits of using Ultralytics YOLOv8 for workout monitoring?

Using Ultralytics YOLOv8 for workout monitoring provides several key benefits:

Optimized Performance: By tailoring workouts based on monitoring data, you can achieve better results.
Goal Achievement: Easily track and adjust fitness goals for measurable progress.
Personalization: Get customized workout plans based on your individual data for optimal effectiveness.
Health Awareness: Early detection of patterns that indicate potential health issues or over-training.
Informed Decisions: Make data-driven decisions to adjust routines and set realistic goals.

You can watch a YouTube video demonstration to see these benefits in action.

How accurate is Ultralytics YOLOv8 in detecting and tracking exercises?

Ultralytics YOLOv8 is highly accurate in detecting and tracking exercises due to its state-of-the-art pose estimation capabilities. It can accurately track key body landmarks and joints, providing real-time feedback on exercise form and performance metrics. The model's pretrained weights and robust architecture ensure high precision and reliability. For real-world examples, check out the real-world applications section in the documentation, which showcases pushups and pullups counting.

Can I use Ultralytics YOLOv8 for custom workout routines?

Yes, Ultralytics YOLOv8 can be adapted for custom workout routines. The AIGym class supports different pose types such as "pushup", "pullup", and "abworkout." You can specify keypoints and angles to detect specific exercises. Here is an example setup:

from ultralytics import solutions

gym_object = solutions.AIGym(
    line_thickness=2,
    view_img=True,
    pose_type="squat",
    kpts_to_check=[6, 8, 10],
)

For more details on setting arguments, refer to the Arguments AIGym section. This flexibility allows you to monitor various exercises and customize routines based on your needs.

How can I save the workout monitoring output using Ultralytics YOLOv8?

To save the workout monitoring output, you can modify the code to include a video writer that saves the processed frames. Here's an example:

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],
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    results = model.track(im0, verbose=False)
    im0 = gym_object.start_counting(im0, results)
    video_writer.write(im0)

cv2.destroyAllWindows()
video_writer.release()

This setup writes the monitored video to an output file. For more details, refer to the Workouts Monitoring with Save Output section.

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