LiDAR-based point cloud analysis for Human Activity Recognition (HAR) is increasingly relevant in assistive technologies, particularly for supporting elderly and care-dependent individuals through applications such as fall detection. In this work, we present DenseVoxelNet3D, a compact and efficient 3D convolutional neural network designed for voxelized LiDAR input. The architecture combines a voxelization-based preprocessing pipeline with densely connected layers to improve feature propagation and mitigate vanishing gradient issues. By integrating global average pooling and dropout-based regularization, the model achieves strong generalization with minimal overfitting. On the HmPEAR dataset [14], DenseVoxelNet3D outperforms conventional architectures such as AlexNet and ResNet in terms of classification accuracy and computational efficiency. Our code is available at https://github.com/eckabeg/DenseVoxelNet3D.git .