Instrumented crutches for gait parameters evaluation

Most of the prototypes of instrumented crutches available in the literature require external motion capture devices to perform a gait analysis and to report the load applied on the crutches with respect to the gait cycle. Motion capture systems with markers require a controlled laboratory with cameras, instead IMU-based systems are more transportable, but the user must be instrumented. A new version of instrumented crutches, previously developed by the authors, allows one to measure the axial forces and to detect the gait phases during two-point assisted walking thanks to the cameras mounted on the lower part of the crutches

Emx2 is a dose-dependent negative regulator of Sox2 telencephalic enhancers.

The transcription factor Sox2 is essential for neural stem cells (NSC) maintenance in the hippocampus and in vitro. The transcription factor Emx2 is also critical for hippocampal development and NSC self-renewal. Searching for 'modifier' genes affecting the Sox2 deficiency phenotype in mouse, we observed that loss of one Emx2 allele substantially increased the telencephalic β-geo (LacZ) expression of a transgene driven by the 5' or 3' Sox2 enhancer. Reciprocally, Emx2 overexpression in NSC cultures inhibited the activity of the same transgene. In vivo, loss of one Emx2 allele increased Sox2 levels in the medial telencephalic wall, including the hippocampal primordium. In hypomorphic Sox2 mutants, retaining a single 'weak' Sox2 allele, Emx2 deficiency substantially rescued hippocampal radial glia stem cells and neurogenesis, indicating that Emx2 functionally interacts with Sox2 at the stem cell level. Electrophoresis mobility shift assays and transfection indicated that Emx2 represses the activities of both Sox2 enhancers. Emx2 bound to overlapping Emx2/POU-binding sites, preventing binding of the POU transcriptional activator Brn2. Additionally, Emx2 directly interacted with Brn2 without binding to DNA. These data imply that Emx2 may perform part of its functions by negatively modulating Sox2 in specific brain areas, thus controlling important aspects of NSC function in development

Body odors (even when masked) make you more emotional: behavioral and neural insights

Morality evolved within specific social contexts that are argued to shape moral choices. In turn, moral choices are hypothesized to be affected by body odors as they powerfully convey socially-relevant information. We thus investigated the neural underpinnings of the possible body odors effect on the participants\u2019 decisions. In an fMRI study we presented to healthy individuals 64 moral dilemmas divided in incongruent (real) and congruent (fake) moral dilemmas, using different types of harm (intentional: instrumental dilemmas, or inadvertent: accidental dilemmas). Participants were required to choose deontological or utilitarian actions under the exposure to a neutral fragrance (masker) or body odors concealed by the same masker (masked body odor). Smelling the masked body odor while processing incongruent (not congruent) dilemmas activates the supramarginal gyrus, consistent with an increase in prosocial attitude. When processing accidental (not instrumental) dilemmas, smelling the masked body odor activates the angular gyrus, an area associated with the processing of people\u2019s presence, supporting the hypothesis that body odors enhance the saliency of the social context in moral scenarios. These results suggest that masked body odors can influence moral choices by increasing the emotional experience during the decision process, and further explain how sensory unconscious biases affect human behavior

BEEHIVE: A dataset of Apis mellifera images to empower honeybee monitoring research

This data article describes the collection process of two sub-datasets comprehending images of Apis mellifera captured inside a commercial beehive (“Frame” sub-dataset, 2057 images) and at the bottom of it (“Bottom” sub-dataset, 1494 images). The data was collected in spring of 2023 (April–May) for the “Frame” sub-dataset, in September 2023 for the “Bottom” sub-dataset. Acquisitions were carried out using an instrumented beehive developed for the purpose of monitoring the colony's health status during long periods of time. The color cameras used were equipped with different lenses accordingly (liquid lenses for the internal one, standard lens of 8 mm focal length) and actuated by an embedded board, alongside red LED strips to illuminate the inside of the beehive. Images captured by the internal camera were mostly out-of-focus, thus a filtering procedure based on the adoption of focus measure operators was developed to keep only the in-focus ones. All images were manually labelled by experts using 2-class bounding boxes annotations representing full visible bees (class “bee”) and blurred or occluded bees according to the sub-dataset (“blurred_bee” or “occluded_bee” class). Annotations are provided in YOLO v8 format. The dataset can be useful for entomology research empowered by computer vision, especially for counting tasks, behavior monitoring, and pest management, since a few occurrences of Varroa destructor mites could be present in the “Frame” sub-dataset

Body measurement estimations using 3D scanner for individuals with severe motor impairments

In biomechanics, a still unresolved question is how to estimate with enough accuracy the volume and mass of each body segment of a subject. This is important for several applications ranging from the rehabilitation of injured subjects to the study of athletic performances via the analysis of the dynamic inertia of each body segment. However, traditionally this evaluation is done by referring to anthropometric tables or by approximating the volumes using manual measurements. We propose a novel method based on the 3D reconstruction of the subject’s body using the commercial low-cost camera Kinect v2. The software developed performs body segment separation in a few minutes leveraging alpha shape approximation of 3D polyhedrons to quickly compute a Montecarlo volume estimation. The procedure was evaluated on a total of 30 healthy subjects and the resulting segments’ lengths and masses were compared with the literature

Misura dell'orientamento di pezzi meccanici a geometria variabile tramite Machine Learning - sviluppo algoritmi e validazione metrologica

L’identificazione corretta della posizione e dell’orientamento di pezzi meccanici a geometria variabile è uno dei maggiori problemi nelle applicazioni di pick & place in ambito industriale. Riuscire a identificare correttamente il modo in cui il pezzo oggetto della misura è posizionato in modo da riuscire a prenderlo e spostarlo risulta fondamentale nei processi industriali automatici in cui sono presenti numerose celle robotiche tra una macchina utensile e l’altra. Il problema viene spesso affrontato tramite tecniche basate su visione 2D che, però, presentano dei limiti quando i pezzi meccanici da prelevare possiedono una geometria tale da uscire dal dominio bidimensionale. Parallelamente, l’approccio 3D presenta una problematica legata soprattutto alla geometria variabile, che non consente lo sviluppo di un algoritmo robusto per l’identificazione del posizionamento del pezzo. Per superare queste limitazioni, negli ultimi anni sono state sviluppate tecniche di misura basate su machine learning che consentono di arginare i problemi legati alla variabilità della geometria. La presente memoria descrive lo sviluppo di un algoritmo di misura della posizione e dell’orientamento di pezzi meccanici di geometria variabile. I pezzi meccanici considerati sono stati ricavati da operazioni di stampaggio e presentano bave sul contorno che rendono gli approcci standard inefficaci e poco accurati nella misura. Per questo motivo, è stato sviluppato un algoritmo di misura che sfrutta una combinazione di tecniche di machine learning e tecniche classiche di visione 3D che permette di ottenere la matrice di rototraslazione dei pezzi oggetti della misura rispetto al relativo modello CAD di progettazione. Grazie alla matrice di rototraslazione ottenuta, è possibile fornire al robot la posizione accurata di alcuni punti scelti manualmente e utilizzati dal robot stesso per effettuare la presa del pezzo. L’algoritmo sviluppato opera su una nuvola di punti 3D del pezzo meccanico comprensivo di bave. Una volta effettuata la scansione sono previste diverse fasi: (i) ritaglio automatico della nuvola in modo da ricavarne solamente il pezzo in esame, (ii) rimozione automatica delle blob di punti identificate come outlier rispetto alla nuvola del pezzo, (iii) identificazione della posa del pezzo meccanico tramite classificatore basato su machine learning, (iv) allineamento grossolano tra pezzo meccanico (SCAN) e il relativo modello di riferimento (RIF) tramite analisi PCA (Principal Component Analysis) e (v) allineamento fine tra pezzo meccanico e modello CAD tramite algoritmo ICP (Iterative Closest Point)

Effects of Knee Joint Misalignments on Human-Exoskeleton Interaction Dynamics

One aspect that characterizes exoskeletons is their close physical contact with the user. Physical human-exoskeleton interaction (pHEI) is strongly affected by kinematic incompatibilities generated in the human-exoskeleton kinematic chain, often resulting in undesired joint misalignments and interaction forces. However, a systematic analysis to highlight this relation is difficult to execute. In this work we use a methodology for the study of pHEI based on an active dummy leg named Leg Replica. Thanks to this system, our aim is to highlight the relations between key pHEI metrics such as joint misalignment, interaction forces and relative displacements, underlining the role and effects of joint misalignment in controlled conditions. We first validated a kinematic model available in the literature relating human-exoskeleton knee misalignment with the resulting device's motion relative to the limb. The model was further used to investigate which is the relation between the kinematic of the leg-device system and the forces produced between the two bodies. We discovered that the model predictions were consistent with the experimental kinematic observations and strongly related with the peaks reached by shear forces during the motion. Shear forces were found to play a key role in the overall interaction and related with the lost of energy during the motion. These results underscore the strong influence that misalignments can exert on the generation of force and motion, even under simplified and controlled conditions

Validation of a smart mirror for gesture recognition in gym training performed by a vision-based deep learning system

This paper illustrates the development and validation of a smart mirror for sports training. The application is based on the skeletonization algorithm MediaPipe and runs on an embedded device Nvidia Jetson Nano equipped with two fisheye cameras. The software has been evaluated considering the exercise biceps curl. The elbow angle has been measured by both MediaPipe and the motion capture system BTS (ground truth), and the resulting values have been compared to determine angle uncertainty, residual errors, and intra-subject and inter-subject repeatability. The uncertainty of the joints’ estimation and the quality of the image captured by the cameras reflect on the final uncertainty of the indicator over time, highlighting the areas of improvement for further development

Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements

Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras' error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device