Skip to content

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
PushUps Counting PullUps Counting

Workouts Monitoring Example

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.track(im0, verbose=False)  # Tracking recommended
    #results = model.predict(im0)  # Prediction also supported
    im0 = gym_object.start_counting(im0, results, frame_count)

cv2.destroyAllWindows()

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.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()
Support

"pushup", "pullup" and "abworkout" supported

KeyPoints Map

keyPoints Order Ultralytics YOLOv8 Pose

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

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


Created 2023-12-02, Updated 2024-04-18
Authors: glenn-jocher (6), RizwanMunawar (1)

Comments