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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.

Yield estimation is a key point theme for precision agriculture, especially for small fruits and in-field scenarios. This paper focuses on the metrological validation of a novel deep-learning model that robustly estimates both the number and the radii of grape berries in vineyards using color images, allowing the computation of the visible (and total) volume of grape clusters, which is necessary to reach the ultimate goal of estimating yield production. The proposed algorithm is validated by analyzing its performance on a custom dataset. The number of berries, their mean radius, and the grape cluster volume are converted to millimeters and compared to reference values obtained through manual measurements. The validation experiment also analyzes the uncertainties of the parameters. Results show that the algorithm can reliably estimate the number (MPE=−5%, σ=6%) and the radius of the visible portion of the grape clusters (MPE=0.8%, σ=7%). Instead, the volume estimated in px3 results in a MPE=0.4% with σ=21%, thus the corresponding volume in mm3 is affected by high uncertainty. This analysis highlighted that half of the total uncertainty on the volume is due to the camera–object distance d and parameter R used to take into account the proportion of visible grapes with respect to the total grapes in the grape cluster. This issue is mostly due to the absence of a reliable depth measure between the camera and the grapes, which could be overcome by using depth sensors in combination with color images. Despite being preliminary, the results prove that the model and the metrological analysis are a remarkable advancement toward a reliable approach for directly estimating yield from 2D pictures in the field.

Winter is the season of main concern for beekeepers since the temperature, humidity, and potential infection from mites and other diseases may lead the colony to death. As a consequence, beekeepers perform invasive checks on the colonies, exposing them to further harm. This paper proposes a novel design of an instrumented beehive involving color cameras placed inside the beehive and at the bottom of it, paving the way for new frontiers in beehive monitoring. The overall acquisition system is described focusing on design choices towards an effective solution for internal, contactless, and stress-free beehive monitoring. To validate our approach, we conducted an experimental campaign in 2023 and analyzed the collected images with YOLOv8 to understand if the proposed solution can be useful for beekeepers and what kind of information can be derived from this kind of monitoring, including the presence of Varroa destructor mites inside the beehive. We experimentally found that the observation point inside the beehive is the most challenging due to the frequent movements of the bees and the difficulties related to obtaining in-focus images. However, from these images, it is possible to find Varroa destructor mites. On the other hand, the observation point at the bottom of the beehive showed great potential for understanding the overall activity of the colony.

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.

Background: Crutches are the most common walking aids prescribed to improve mobility in subjects with central nervous system (CNS) lesions. To increase adherence to the appropriate level of crutch usage, providing load-related auditory feedback (aFB) may be a useful approach. We sensorized forearm crutches and developed a custom software to provide aFB information to both user and physical therapist (PhT).Aim: Evaluate aFB effects on load control during gait by a self-controlled case series trial.Methods: A single experimental session was conducted enrolling 12 CNS lesioned participants. Load on crutch was recorded during 10 Meter Walk Test performed with and without aFB. In both cases, crutch load data, and gait speed were recorded. Usability and satisfaction questionnaires were administered to participants and PhTs involved.Results: Reliable data were obtained from eight participants. Results showed that compared to the no FB condition, aFB yielded a significant reduction in the mean load on the crutches during gait (p = 0.001). The FB did not influence gait speed or fatigue (p > 0.05). The experience questionnaire data indicated a positive experience regarding the use of aFB from both participants' and PhTs' perspectives.Conclusion: aFB significantly improves compliance with crutch use and does not affect gait speed or fatigue by improving the load placed on crutches. The FB is perceived by users as helpful, safe, and easy to learn, and does not interfere with attention or concentration while walking. Furthermore, the PhTs consider the system to be useful, easy to learn and reliable.

The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor.Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to Depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms.This dataset has been used in [1] and it is freely available at http://dx.doi.org/10.17632/ndrczc35bt.1.

This paper presents the validation of a marker-less motion capture system used to evaluate the upper limb stress of subjects using exoskeletons for locomotion. The system fuses the human skeletonization provided by commercial 3D cameras with forces exchanged by the user to the ground through upper limbs utilizing instrumented crutches. The aim is to provide a low cost, accurate, and reliable technology useful to provide the trainer a quantitative evaluation of the impact of assisted gait on the subject without the need to use an instrumented gait lab. The reaction forces at the upper limbs’ joints are measured to provide a validation focused on clinically relevant quantities for this application. The system was used simultaneously with a reference motion capture system inside a clinical gait analysis lab. An expert user performed 20 walking tests using instrumented crutches and force platforms inside the observed volume. The mechanical model was applied to data from the system and the reference motion capture, and numerical simulations were performed to assess the internal joint reaction of the subject’s upper limbs. A comparison between the two results shows a root mean square error of less than 2% of the subject’s body weight.