---
comments: true
description: Optimize your fitness routine with real-time workouts monitoring using Ultralytics YOLOv8. Track and improve your exercise form and performance.
keywords: 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 ](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.
< p align = "center" >
< br >
< iframe loading = "lazy" width = "720" height = "405" src = "https://www.youtube.com/embed/LGGxqLZtvuw"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
< / iframe >
< br >
< strong > Watch:< / strong > Workouts Monitoring using Ultralytics YOLOv8 | Pushups, Pullups, Ab Workouts
< / p >
## 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 |
