Surgical Dynamometer to Simultaneously Measure the Tension Forces and the Distance between Wound Edges during the Closure of a Laparotomy.

The closure of the abdominal wall after making a laparotomy is a major challenge for surgeons, since a significant percentage of closures fail and incisional hernias rise. The suture has to withstand the forces required to close the incision, while not hindering the adequate wound healing progression. Currently, there is no surgical measuring device that could be used to determine the required closing forces, which can be very different depending on the patient. This paper presents a dynamometer to measure the tension forces to be applied while closing a surgical incision, and it simultaneously measures the distance between wound edges. It is a compass-like instrument. A mechanism between the two legs incorporates a load cell, whose signal is read by an electronic device that computes the values of the tension forces between wound edges. An angular position sensor at the pin joint between legs provides the distance between both sides of the incision. Measuring capabilities of the instrument prototype were verified at the laboratory. Thereafter, its functionality was demonstrated in experimental surgery tests. Therefore, the instrument could be very useful in clinical applications, assisting personalized surgical techniques.

An embedded real-time red peach detection system based on an OV7670 camera, ARM cortex-M4 processor and 3D look-up tables.

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

Definition of linear color models in the RGB vector color space to detect red peaches in orchard images taken under natural illumination.

This work proposes the detection of red peaches in orchard images based on the definition of different linear color models in the RGB vector color space. The classification and segmentation of the pixels of the image is then performed by comparing the color distance from each pixel to the different previously defined linear color models. The methodology proposed has been tested with images obtained in a real orchard under natural light. The peach variety in the orchard was the paraguayo (Prunus persica var. platycarpa) peach with red skin. The segmentation results showed that the area of the red peaches in the images was detected with an average error of 11.6%; 19.7% in the case of bright illumination; 8.2% in the case of low illumination; 8.6% for occlusion up to 33%; 12.2% in the case of occlusion between 34 and 66%; and 23% for occlusion above 66%. Finally, a methodology was proposed to estimate the diameter of the fruits based on an ellipsoidal fitting. A first diameter was obtained by using all the contour pixels and a second diameter was obtained by rejecting some pixels of the contour. This approach enables a rough estimate of the fruit occlusion percentage range by comparing the two diameter estimates.

Measuring oscillating walking paths with a LIDAR.

This work describes the analysis of different walking paths registered using a Light Detection And Ranging (LIDAR) laser range sensor in order to measure oscillating trajectories during unsupervised walking. The estimate of the gait and trajectory parameters were obtained with a terrestrial LIDAR placed 100 mm above the ground with the scanning plane parallel to the floor to measure the trajectory of the legs without attaching any markers or modifying the floor. Three different large walking experiments were performed to test the proposed measurement system with straight and oscillating trajectories. The main advantages of the proposed system are the possibility to measure several steps and obtain average gait parameters and the minimum infrastructure required. This measurement system enables the development of new ambulatory applications based on the analysis of the gait and the trajectory during a walk.