Design of a body energy harvesting system for the upper extremity

Converting energy from human upper limb motions into electrical energy is a challenge, as low frequency movements have to be converted into repetitive movements to effectively drive electromechanical generators. The prototype of an electromagnetic linear generator with gyrating mass is presented. The mechanical motion model first was simulated and the design was evaluated during different activities. An average power output of about 50 μW was determined with a maximum power output of 2.2 mW that is sufficient to operate sensors for health monitoring

Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition

DiversityScanner 4K: A High Resolution Extended Focus Camera Setup as Extension for the DiversityScanner

Manual examination of invertebrate species diversity and abundance in Malaise trap samples is a time-consuming and costly task that requires expert knowledge. Automated solutions based on robotics and artificial intelligence can assist experts in evaluating the large number of samples collected, especially when the phenotype of individual species in a sample needs to be assessed and classified. Therefore, we have developed the DiversityScanner, a robotic solution that provides the ability to automatically image, measure, classify, and sort invertebrates (< 3 mm) into 96-well microplates for barcoding. Because it is necessary to document even the smallest details, such as tiny bristles, on a specimen, we have significantly improved the image quality of the detailed images in the DiversityScanner 4k. This is achieved by using an extended focus system and a 12-megapixel camera. By using an electrically focus tunable lens from Optotune, extended focus images can be created from multiple z-stack images with different focus planes. An algorithm then automatically aligns the images, detecting sharp areas in each image, and produces high-resolution extended-focus images. Finally, the object can be classified by a convolutional neural network and the biomass of the insect can be estimated from the image

How automation, machine learning, and DNA barcoding can accelerate species discovery in “dark taxa”: Robotics and AI

Robotics and artificial intelligence are two methods that are suitable for improving processes that are normally done manually. Therefore, these techniques also can be used when examining specimen-rich invertebrate samples, where traditional sorting methods are to slow and require expert knowledge. For that reason, we developed the DiversityScanner: a classification, sorting, and measurement robot for invertebrates. The 500 x 500 x 500 mm robot has three linear axes that enable a camera unit and an automated pipette to be moved over a square Petri dish, containing up to 150 specimens. After starting the DiversityScanner the image taken by an overview camera mounted directly above the Petri dish is utilized to calculate the position of the insects. Then the camera unit is moved over one specimen to capture high resolution detailed images. Convolutional neuronal networks (CNNs) are then used to classify the specimen into 14 different insect taxa (mostly families) and the specimen length and volume are estimated. In a final step, the specimen is moved into a microplate using an automated pipette. In this talk we show how the DiversityScanner uses automation and artificial intelligence to take advantage of previously nearly untapped resources in the study of specimen-rich invertebrate samples

Semi-automated detection of fractional shortening in zebrafish embryo heart videos

Quantifying cardiac functions in model organisms like embryonic zebrafish is of high importance in small molecule screens for new therapeutic compounds. One relevant cardiac parameter is the fractional shortening (FS). A method for semi-automatic quantification of FS in video recordings of zebrafish embryo hearts is presented. The software provides automated visual information about the end-systolic and end-diastolic stages of the heart by displaying corresponding colored lines into a Motion-mode display. After manually marking the ventricle diameters in frames of end-systolic and end-diastolic stages, the FS is calculated. The software was evaluated by comparing the results of the determination of FS with results obtained from another established method. Correlations of 0.96 < r < 0.99 between the two methods were found indicating that the new software provides comparable results for the determination of the FS

Simulation and evaluation of a body energy harvesting device for arm and leg swing motions

Body energy harvesting (BEH), especially for wearable devices, is an emerging and promising technology to improve the battery capacity and to avoid regular maintenance in terms of energy supply. A broad application of BEH increases sustainability and thus offers an advantage from an environmental point of view. We present a light weight BEH device for non-resonant arm and leg swing motions. The design was kept as simple and robust as possible and is based on an electrical generator. The generator is moved by an oscillating mass, which was previously simulated in a model, so that in this generator model, the kinetic energy is optimally transformed into electrical energy. Additionally, an ultra-low voltage power conditioning circuit, based on a step-up converter, was adapted to the BEH generator. The BEH generator and the power conditioning circuit were evaluated in a real test setup for arm and leg movements during walking and jogging with the BEH device worn on the wrist or ankle. An effective power of ∼11.3 mW was generated. This provides a constant voltage to charge a battery or supercapacitor

Recent Developments in Ozone Sensor Technology for Medical Applications

There is increasing interest in the utilisation of medical gases, such as ozone, for the treatment of herniated disks, peripheral artery diseases, and chronic wounds, and for dentistry. Currently, the in situ measurement of the dissolved ozone concentration during the medical procedures in human bodily liquids and tissues is not possible. Further research is necessary to enable the integration of ozone sensors in medical and bioanalytical devices. In the present review, we report selected recent developments in ozone sensor technology (2016–2020). The sensors are subdivided into ozone gas sensors and dissolved ozone sensors. The focus thereby lies upon amperometric and impedimetric as well as optical measurement methods. The progress made in various areas—such as measurement temperature, measurement range, response time, and recovery time—is presented. As inkjet-printing is a new promising technology for embedding sensors in medical and bioanalytical devices, the present review includes a brief overview of the current approaches of inkjet-printed ozone sensors