Felipe Ballen-Moreno

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans.

Various sensors are employed in dynamic human-robot collaboration manufacturing environments for real-time human pose estimation to improve safety through collision-avoidance systems and gesture command recognition to enhance human-robot interaction. However, the most widely used sensors – RGBD cameras – often underperform under varying lighting and environmental conditions and raise privacy concerns. This paper introduces mmPrivPose3D, a novel system designed to prioritize privacy while performing human pose estimation and gesture command recognition using a 60 GHz industrial Frequency Modulated Continuous Wave (FMCW) RaDAR with a 10 m maximum range and 29 degrees angular resolution. The system employs a parallel architecture including a 3D Convolutional Neural Network (CNN) for pose estimation, which extracts 19 keypoints of the human skeleton, along with a random forest classifier for recognizing gesture commands. The system was trained on a dataset involving ten individuals performing various movements in a human-robot interaction context, including walking in the workspace and hand-waving gestures. Our model demonstrated a low Mean Per Joint Position Error (MPJPE) of 4.8% across keypoints for pose estimation and, for gesture recognition, an accuracy of 96.3% during k-fold cross-validation and 96.2% during inference. mmPrivPose3D has the potential for application in human workspace localization and human-to-robot communication, particularly in contexts where privacy is a concern.

Wearable robots have become increasingly prevalent in various applications, including rehabilitation, power augmentation, and assistance. However, one of the challenges in designing wearable robots is how to attach them to the human body. The attachment method should be secure, reliable, comfortable, effective, and controlled for the user. Moreover, the attachment points should not interfere with the user’s daily activities, and the attachment process should not be time-consuming or complicated. Typical straps nowadays require a time-consuming and cumbersome donning and doffing procedure from therapists for users needing rehabilitation therapy. Therefore, we propose a novel pneumatically actuated soft strap to enclose the limb and automate part of the strapping procedure. This paper proposes a preliminary design utilizing soft bending actuators for attaching physical interfaces to humans, with integrated active elements for facilitating and automating the strapping process. Finite element analysis was conducted to assess pressure requirements, bending curvature, and geometry, with simulation results demonstrating a promising agreement, with a root mean square error (RMSE) of 3.4° in bending angle. In the future, an additional locking mechanism would be required to provide the necessary holding force and fully constrain the limb.

Stroke disease leads to a partial or complete disability affecting muscle strength and functional mobility. Early rehabilitation sessions might induce neuroplasticity and restore the affected function or structure of the patients. Robotic rehabilitation minimizes the burden on therapists by providing repetitive and regularly monitored therapies. Commercial exoskeletons have been found to assist hip and knee motion. For instance, unilateral exoskeletons have the potential to become an effective training system for patients with hemiparesis. However, these robotic devices leave the ankle joint unassisted, essential in gait for body propulsion and weight-bearing. This article evaluates the effects of the robotic ankle orthosis T-FLEX during cooperative assistance with the AGoRA unilateral lower-limb exoskeleton (hip and knee actuation). This study involves nine subjects, measuring muscle activity and gait parameters such as stance and swing times. The results showed a reduction in muscle activity in the Biceps Femoris of 50%, Lateral Gastrocnemius of 59% and Tibialis Anterior of 35% when adding T-FLEX to the AGoRA unilateral lower-limb exoskeleton. No differences were found in gait parameters. Nevertheless, stability is preserved when comparing the two legs. Future works should focus on evaluating the devices in ground tests in healthy subjects and pathological patients.

People who suffer from stroke have a higher difficulty performing gait activity, affecting their quality of life. New technologies have been developed to assist and rehabilitate the affected limbs. This paper presents the AGoRA V2 unilateral lower-limb exoskeleton and the assessment of physiological and spatiotemporal parameters of gait in 10 subjects who participated in a test. AGORA V2 combine a stiff structure for the Hip and Knee and the T-FLEX ankle exoskeleton (soft structure). The results showed a significant decrease in muscle activity compared to the condition without an exoskeleton. This decrease was 8% in Biceps Femoris (BF) muscle activity and 4% in Vastus Medialis (VM) muscle activity, generated by proper assistance of the devices thigh muscles. Tibialis Anterior (TA) and Lateral Gastrocnemius (LG) muscles and gait times did not show significant changes, which can be interpreted as a correct synchronisation of the devices with the person’s gait. The results obtained can be used as a baseline for future studies with pathological patients.

Exoskeletons have been assessed by qualitative and quantitative features known as performance indicators. Within these, the ergonomic indicators have been isolated, creating a lack of methodologies to analyze and assess physical interfaces. In this sense, this work presents a three-dimensional relative motion assessment method. This method quantifies the difference of orientation between the user’s limb and the exoskeleton link, providing a deeper understanding of the Human–Robot interaction. To this end, the AGoRA exoskeleton was configured in a resistive mode and assessed using an optoelectronic system. The interaction quantified a difference of orientation considerably at a maximum value of 41.1 degrees along the sagittal plane. It extended the understanding of the Human–Robot Interaction throughout the three principal human planes. Furthermore, the proposed method establishes a performance indicator of the physical interfaces of an exoskeleton.