---
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.
## 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"
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"
video_writer = cv2.VideoWriter("workouts.avi",
cv2.VideoWriter_fourcc(*'mp4v'),
int(cap.get(5)),
(int(cap.get(3)), int(cap.get(4))))
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 |
| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks |
| `classes` | `None or list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
