This work introduces a UWB range dataset featuring five distinct individuals in nine different postures, representing nine separate classes. The dataset is designed for classification using KNN, SVM, and MLP models, aimed at advancing human-robot interaction.
Examples for UWB based posture recognition for human robot interaction
Clone this repo
git clone [email protected]:salmasalimii/UWB-based-posture-recognition.git
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=2)
knn.fit(x_train,y_train)
pred = knn.predict(x_test)
from sklearn.svm import SVC
model = SVC()
model.fit(x_train, y_train)
predictions = model.predict(x_test)
from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)
mlp.fit(x_train, y_train)
predictions = mlp.predict(x_test)
The detailed code for implementing these models is available upon request.