Musculoskeletal Modelling of Altered Gait using OpenSim

Musculoskeletal Modelling
of Altered Gait using OpenSim

Introduction

Walking, a fundamental skill, encompasses various factors and nuances, making it a complex activity. Conditions affecting the nervous and muscular systems can compromise this ability, impacting the quality of life for many individuals who rely on walking as their primary physical activity. In this collaborative project with a team of three, I delved into studying the dynamics of walking. Utilizing Musculoskeletal Modeling software OpenSim, we conducted a muscle-driven simulation of walking for normal and altered gait patterns. Our focus encompassed normal and stiff-knee gait cases, employing motion capture, force plates, and EMG systems. The comprehensive analysis of muscle activities, coupled with muscle force values, aimed to draw conclusions for the development of effective interventions and treatments to restore and enhance walking ability.

Methodology

In this experiment, a healthy adult, aged 24 (height of 165 cm and weight of 75.5 kg) volunteered as the participant. The participant wore 49 markers for motion capture from 8 VICON cameras, following a template designed for the Gait_2392 model to affix the markers to specific body segments. Additionally, 6 Electromyography (EMG) sensors were placed on the gluteus maximus, biceps femoris, rectus femoris, tibialis anterior, soleus, and lateral gastrocnemius to record lower limb muscle activity. The experiment setup included a 96 cm walkway with two AMTI force plates for measuring ground reaction forces. The VICON motion sensor, force plates, and EMG recorder were synchronized through the VICON Lock+, with the recording triggered by a 5V signal at the start of the experiment. The workspace was then calibrated and a static data was recorded which was used to scale the simulation model according to the subject’s
body dimensions (to modify the anthropometry, or physical dimensions, of the generic model so that it matches the anthropometry of a particular subject)

Experiment Protocol: The participant performed four trials for the baseline condition of normal overground walking with verbal instruction of maintaining their comfortable walking speed. For Altered walking, a stiff rod of roughly 60cm was taken and attached to the subject’s right leg with Velcro bands which made sure that the subject was unable to flex his knee, simulating a stiff knee gait. Similar to the normal walking condition, the subject performed four walking bouts over the force plates, during which data was recorded.

import cv2
import torch
from torchvision import models, transforms
from custom_dataset import MyLazyDataset
from torchvision.models.quantization import mobilenet_v3_large
import torchvision
import torch
from torchvision import transforms, datasets
from torch.utils.data import Dataset, DataLoader
import torchvision.models.quantization as models
import torch.optim as optim
import time
import copy
from torch import nn
import numpy as np
from PIL import Image

import pyrealsense2 as rs

# Set device
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'

# Load the model
model = models.mobilenet_v2(pretrained=False)
model.load_state_dict(torch.load('D:\\Documents\\Exp_Data\\datafinaldatamain.pt', map_location=torch.device('cpu')))
model.to(DEVICE)
model.eval()

# Define data transforms for the test data
data_transforms_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)  # Enable depth stream
pipeline.start(config)

fno = 0
num_segments = 3

try:
    while True:
        frames = pipeline.wait_for_frames()
        color_frame = frames.get_color_frame()
        depth_frame = frames.get_depth_frame()
        color_image = np.asanyarray(color_frame.get_data())

        # Get the width and height of the frames
        width, height = color_frame.get_width(), color_frame.get_height()
        
        segment_height = height // num_segments

        for i in range(num_segments):
            start_y = i * segment_height
            end_y = (i + 1) * segment_height
            segment = color_image[start_y:end_y, :]
            input_image = Image.fromarray(cv2.cvtColor(segment, cv2.COLOR_BGR2RGB))
            input_tensor = data_transforms_test(input_image).unsqueeze(0).to(DEVICE)

            # Calculate center coordinates
            center_x = width // 2
            center_y = start_y + (segment_height // 2)

            # Get depth value at center point
            depth_value = depth_frame.get_distance(center_x, center_y) if depth_frame else 0.0

            # Draw a point at the center
            cv2.circle(color_image, (center_x, center_y), 3, (0, 255, 0), -1)

            with torch.no_grad():
                outputs = model(input_tensor)
                _, preds = torch.max(outputs, 1)

            
            if preds.item() == 0:
                label = "Downstairs"
            elif preds.item() == 1:
                label = "Overground"
            else:
                label = "Upstairs"
                
            confidence = outputs[0][preds.item()].item()

            cv2.rectangle(color_image, (0, start_y), (width, end_y), (0, 0, 0), 2)
            cv2.putText(color_image, f"Segment {i+1}", (10, start_y + 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
            cv2.putText(color_image, f"Class: {label}", (10, start_y + 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.putText(color_image, f"Confidence: {confidence:.2f}", (10, start_y + 90), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
            cv2.putText(color_image, f"Depth: {depth_value:.4f} m", (center_x + 10, center_y), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 0), 1, cv2.LINE_AA)

        frame_display_string = "Frame: " + str(fno)
        cv2.putText(color_image, frame_display_string, (width - 100, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1, cv2.LINE_AA)
        
        cv2.imshow('RealSense Prediction', color_image)

        fno += 1

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

finally:
    pipeline.stop()

cv2.destroyAllWindows()