With the aging population of the developed countries, the diseases of affluence become common. One of the consequences of their complications are chronic wounds that affect more and more people. Because the healing of chronic wounds is a lengthy process, its monitoring should be as simple as possible, yet providing accurate and reliable documentation. In this paper, we present an image acquisition system and wound surface reconstruction method using several imaging modalities: color photography, thermal imaging, and depth perception. The proposed method is dedicated mainly to wounds located on the limbs where body curvature is large, and its influence on 2D results cannot be neglected. Our approach minimizes the extra effort taken by the medical staff to prepare the wound outline as it is still performed using 2D color photo and then mapped into the 3D space. The method was validated on 29 data sets containing two 3D point clouds from two depth imaging devices (depth camera and stereo camera) as well as 2D color photos and thermal maps. The approach was compared with expert delineations as well as other contemporary methods for wound surface reconstruction presented in the literature. Performed experiments and the obtained results show that the proposed method is statistically concordant with expert delineations performed in 3D.

This paper presents a concept of a 4D multimodal speaker model (4D-MSM) for asynchronous remote speech diagnosis. Recording and archiving diagnostically significant articulation material remain an issue in computer-aided speech diagnosis. Therefore, we propose a workflow for preparing and storing reliable and easily interpretable multimodal data regarding pronunciation. According to our assumptions, data acquisition should be non-invasive, comfortable for both the patient and therapist, not interfere with the articulation process, and provide essential data of high quality. We developed and employed a dedicated device, obtaining a 15-channel spatially distributed audio signal and stable stereovision stream from two cameras focused on the lower part of the face. Our framework for data preprocessing covers digital beamforming of the multichannel audio signal, audio-video synchronization, and segmentation of words in the audio signal. Then, we use stereo data to calculate and adjust the depth map and prepare point clouds. Simultaneously, we delineate the mouth in video frames using a dedicated semi-automated segmentation algorithm. The point clouds are then textured with the camera images with superimposed mouth regions. Finally, we add the audio track to constitute the 4D-MSM. In the paper, we show the concept and detailed specification of the model and present experiments to justify the methodology. Proposed 4D-MSMs may be employed in remote speech diagnosis for objectifying and archiving diagnoses, conducting asynchronous consultations, and documenting the progress in therapy.

Speech disorders are significant barriers to the balanced development of a child. Many children in Poland are affected by lisps (sigmatism)—the incorrect articulation of sibilants. Since speech therapy diagnostics is complex and multifaceted, developing computer-assisted methods is crucial. This paper presents the results of assessing the usefulness of hybrid feature vectors extracted based on multimodal (video and audio) data for the place of articulation assessment in sibilants /s/ and /ʂ/. We used acoustic features and, new in this field, visual parameters describing selected articulators’ texture and shape. Analysis using statistical tests indicated the differences between various sibilant realizations in the context of the articulation pattern assessment using hybrid feature vectors. In sound /s/, 35 variables differentiated dental and interdental pronunciation, and 24 were visual (textural and shape). For sibilant /ʂ/, we found 49 statistically significant variables whose distributions differed between speaker groups (alveolar, dental, and postalveolar articulation), and the dominant feature type was noise-band acoustic. Our study suggests hybridizing the acoustic description with video processing provides richer diagnostic information.