Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum is proposed. The original vibration signal is decomposed by variational mode decomposition (VMD), and four components with narrow bands and independent central frequencies are decomposed. In order to retain the feature spectrum of the original vibration signal as far as possible, the corresponding feature bands are intercepted from the frequency spectrum of original vibration signal based on the central frequency of each component. Then, the feature images of fault signals are constructed as the inputs of the convolution neural network (CNN), and the parameters of the neural network are optimized by sample training. Finally, the optimized CNN is used to identify fault signals. The overall fault recognition rate is up to 98.75%. Compared with the feature bands extracted directly from the component spectrums, the extraction method of the feature bands proposed in this paper needs fewer iterations under the same network structure. The method of planetary gear fault diagnosis proposed in this paper is effective. As a classic gear transmission mode, the planetary gear train is widely used in the transmission system of engineering machinery, aerospace and ship vehicles for its advantages of compact structure, large transmission ratio and strong bearing capacity. Poor conditions greatly increase the failure probability of planetary gear . The traFault diagnosis can be roughly divided into two directions: fault pattern recognition and fault location. Compared with fault pattern recognition, different location faults have different fault frequencies, which make it a little easier. Fault feature frequency can be extracted based on signal de-noising and signal decomposition technology . HoweverAs a signal feature, the sidebands can well reflect the differences between different fault signals. VMD can intercept the feature frequency band from the original vibration signal effectively . HoweverExtracting feature values based on basic data is the key step of fault diagnosis. A reasonable selection of sensitive features and high-quality classification neural network can achieve a better fault recognition result . Chen etIn this paper, a method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and original vibration signal frequency spectrum is proposed. The method is roughly divided into four steps. Firstly, VMD is used to acquire modal components with prominent features. Secondly, based on the central frequency of modal component, the feature frequency band is extracted from the original vibration signal spectrum. Then, the feature frequency band is split up and reorganized to get the signal feature spectrum image. Finally, the image samples are used to train CNN and realize fault diagnosis.K modal functions with the least sum of bandwidth, As a non-recursive adaptive decomposition method, VMD combines classical Wiener filtering, Hilbert transform and frequency mixing technology . Supposee, number of iterations N, quantity of components K and moderate bandwidth constraint Step 1: Initialize modal component t is time and Step 2: Each modal function Step 3: Establish a constrained variational model. The two-penalty factor Step 4: The method of alternating direction multiplier algorithm is used to solve the above variational problems. The saddle point of the extended Lagrange expression is obtained by altering the update parameters Step 5: When meeting the iteration termination condition (6) or (7), the update of parameters is stopped, and the VMD is completed.The edge band can well reflect the difference between different fault signals. VMD can be used to intercept the most feature frequency band in the original vibration signal. However, it is found that in the modulation process of modal component and central frequency, the magnitude of the original vibration signal spectrum is changed, which results in the large number of original details that cannot be retained in the sideband. In order to solve this problem, this paper proposes an interception algorithm of feature frequency band based on multi central frequencies and vibration signal frequency spectrum. The algorithm is divided into two steps:Step 1: By decomposing the original vibration signal with VMD, the representative feature components are extracted and the central frequency of each component is obtained.Step 2: The center frequency is set to the center of the feature frequency band, and the feature frequency band is cut from the original spectrum.Step 3: The components are split into several segments, and then each segment is sorted by columns. Finally, a nearly square feature spectrum image is obtained.CNN can effectively establish mapping relationship between feature spectrum and fault types. The network is usually composed of 5 functional networks: input layer, convolution layer, pool layer, full connection layer and output layer. The input layer is used to implement the sample input, the convolution layer and the sampling layer are used to realize the adaptive feature extraction and dimensionality reduction, the full connection layer and the output layer are used to realize pattern recognition . The simThe whole training process is divided into 6 steps:L, convolution kernel size c and full connection layer size z based on input samples and output categories.Step 1: Select appropriate group number of convolution-pooling T and batch size S. Initialize network weights Step 2: Set learning rate l represents the number of layers, and f is the excitation function.Step 3: Use convolution kernel to do convolution operation and increase the bias to the result. Sigmoid function is used to select output features:m fold reduction is produced by adding bias and excitation function.j is the sequence number of the feature map, down (*) is the down sampling function, and max (*) indicates the maximum pooling method.Step 4: The maximum value in each l reaches the level of training N. The mapping relationship between the fully connected layer and the input layer is established to complete the whole network construction.Step 5: Repeat steps 4 and 5 according to network structure until t reaches to the number of iterations T, the network training is completed.Step 6: Unfold all the features and select the classifier. The mapping relationship between the fully connected layer and the input layer is established to complete the whole network construction. Samples are used to train neural network and optimize network parameters. When Planetary gear fault experiments were simulated in the Drivetrain Dynamics Simulator (DDS) mechanical fault comprehensive simulation produced by Spectra Quest Company. The experimental system is composed of a two-stage planetary gearbox, a programmable control motor, a fixed-axis gearbox, a programmable magnetic brake component, a data acquisition system and a portable computer. The simulation bench is shown in The output frequency of the motor controlled is set as 45 Hz. In addition, the load controlled by a programmable magnetic brake component is set as 13.5 nm, In the experiments, the sampling frequency is set as 12,800 Hz. The original vibration signal is mainly composed of motor rotation frequency, gear meshing frequency, planet carrier rotation frequency and fault feature frequency. The frequency of each component is shown in Taking the sun wheel as the research object, four sets of data under normal, wear, broken and crack state were collected. The vibration signals collected from different types of failures are shown in It can be seen that because of the consistent fault location and the high similarity of the components of the signal, the obvious differences cannot be seen from the time domain signal of different faults. While the different types of fault in the meshing condition, the subtle fault difference will affect the frequency and band of the main center of the signal, which makes a certain difference in the spectrum diagram of the vibration signal. Frequency-domain signals of four different gear states are shown in It can be seen from It is not conducive to judging the fault types directly through spectrum. Therefore, it is necessary to carry out further research from the signal spectrum to achieve the final fault diagnosis.Signal decomposition plays an important role in signal processing. Better decomposition can effectively extract representative feature components from the original vibration signal and amplify the features. As a non-recursive adaptive decomposition algorithm, VMD avoids the modal aliasing and endpoint effects in recursive EMD and LMD decomposition. Based on the classical Wiener filtering, Hilbert transform and frequency mixing technique, the frequency center and bandwidth of each decomposition component are determined by iterative searching the optimal solution of the variational model. The frequency domain dissection and the effective separation of the components can be realized adaptively. However, it is found that in the modulation process of modal component and central frequency, the magnitude of the vibration signal frequency spectrum is changed, which results in the large number of original details that cannot be retained in the sideband. K determines the number of components decomposed by signal and k is large, there will be over decomposition phenomena, such as noise components or overlapped components. When k is small, local features will be ignored and cannot appear in separate components. By observing the original vibration signal frequency spectrum of four kind gear states in K value can effectively avoid the appearance of noise components and increase the difference of feature spectrum. Therefore, the number of components is set as 4. In order to avoid the occurrence of modal aliasing, the sideband bandwidth of each component should be controlled as much as possible. When k = 4, the decomposition effects of the signal under different The core parameters of VMD are component number k and sideband constraint It can be seen from Compared with the original vibration signal frequency spectrum, it can be found that the amplitude reduction of spectrum edge is serious. Next, the method of this paper is used to intercept the feature frequency spectrums. After Fourier transform, each component signal contains 1600 frequencies, corresponding to 2048 sampling points. The sideband bandwidth of each component is around 200 Hz, and the number of sampling points corresponding to the 200 Hz bandwidth is 256. The interception criteria of the feature band are as follows: taking the sampling points corresponding to the central frequency of each component as the center, 127 sampling points are fetch forward and 128 sampling points are taken backwards. When the sampling point corresponding to the center frequency is less than 128, the first 256 sampling points are taken. The frequency spectrum of each component obtained by this method is shown in By comparing the two graphs of Feature extraction is the core component of fault diagnosis technology. The quality of feature extraction often determines the final fault recognition effect. However, traditional features such as energy features, entropy features and so on, depend on their data strongly and have poor universality. The same method applied to different working conditions is often not ideal. The deep neural network based on multi-layer CNNS has good generality and high recognition rate. This method is widely used in the field of image recognition with its ultra-high recognition rate. LeNet-5 is a classical CNN model which is the first CNN successfully applied to handwritten numeral recognition. On the Modified National Institute of Standards and Technology (MNIST) data set, the recognition rate of LeNet-5 model can reach about 99.2%. The LeNet-5 model has 7 layers, where the convolution kernel size is The original vibration signal is processed by VMD, and the feature frequency band with a length of 256 is intercepted based on the central frequency of each component. The feature matrix of dimension In order to facilitate network operation, the amplitudes of feature matrix are normalized. The normalization of the characteristic matrix provides a great help for the later convolution network training. The same amplitude range reduces the fluctuation of weights, bias and other parameters in the network, which reduces the amount of computation and improves the quality of the network effectively. The feature matrix of the four faults is displayed in the form of three-dimensional bar graph, as shown in As can be seen from The output is the four state of gear failure, the number of neurons in output layer is 4. The other parameters remain unchanged. In this experiment, four groups of vibration signal samples were collected from normal, broken, wear and crack states. Each group has 120 segments of signals, and the four groups have 480 segments of signals. 320 segments are used for training deep neural network, and the 160 is for testing. At the same time, two kinds of feature band extraction algorithms were tested by CNN. One is to intercept the feature frequency band from the frequency spectrum of each component based on the central frequency of each component. The other is to intercept the feature frequency band from the frequency spectrum of original vibration signal based on the central frequency of each component. The application effects of two methods are shown in As can be seen from This paper proposes a method of planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum. Considering the high similarity of the vibration signals of different gear types, this paper starts with the study of the sideband of the signal. Firstly, in view of strong non-linear non-stationary features of signals caused by the complex components and the variable transmission path, VMD is used to decompose original vibration signal and four representative feature components and center frequencies are extracted. Secondly, in order to overcome the problem of edge spectrum weakening in component spectrums, an interception method of feature spectrum based on component center frequencies and frequency spectrum of original vibration signal is proposed in this paper. Taking the central frequency of each component as benchmark, the corresponding frequency bands are cut from the frequency spectrum of original vibration signal, which effectively preserves the features of the original sidebands near the center frequencies. In addition, the four most featured frequency bands with a length of 256 are obtained from the original frequency spectrum. Then, the intercepted feature bands are used to construct a feature matrix with a size of


In this paper, we introduce an in-depth application of high-resolution disparity map estimation using stereo images from Mars Curiosity rover’s Mastcams, which have two imagers with different resolutions. The left Mastcam has three times lower resolution as that of the right. The left Mastcam image’s resolution is first enhanced with three methods: Bicubic interpolation, pansharpening-based method, and a deep learning super resolution method. The enhanced left camera image and the right camera image are then used to estimate the disparity map. The impact of the left camera image enhancement is examined. The comparative performance analyses showed that the left camera enhancement results in getting more accurate disparity maps in comparison to using the original left Mastcam images for disparity map estimation. The deep learning-based method provided the best performance among the three for both image enhancement and disparity map estimation accuracy. A high-resolution disparity map, which is the result of the left camera image enhancement, is anticipated to improve the conducted science products in the Mastcam imagery such as 3D scene reconstructions, depth maps, and anaglyph images. Due to hardware limitation in data storage, scarce bandwidth in data downlink and cost of sensor, different imagers have chosen to have different priorities with respect to image resolution. As an example, onboard the Mars rover Curiosity, there are two mast cameras (Mastcam), which act as eyes for the rover . The MasThere are several research papers that discuss about using Mastcams for anomaly detection, rock composition estimation, classification and finding interesting destinations for the rover. In , the rigIn this paper, we introduce an in-depth application of image resolution enhancement to the low-resolution left camera image in the Mastcam image pair and evaluate the impact of the left camera image enhancement on the disparity map estimation quantitatively. The motivation of this work is to have a disparity map in higher resolution than before by enhancing the low-resolution image in the image pair and estimating the disparity map using this enhanced left image and the high-resolution right image. To the best of our knowledge, even though stereo images had been generated using the Mastcam instruments , examiniFor the low-resolution left camera image enhancement, we used the bicubic interpolation  as the bThis work demonstrates that a high-resolution disparity map estimation is possible for stereo image pairs with different camera resolutions such as the case in Mars Curiosity rover’s Mastcams. This is the main contribution of this work. It is shown that this can be achieved by enhancing the low-resolution left camera image. The right camera image content is utilized for enhancement with the pansharpening-based method and EDSR. For the demonstrations, 20 Mastcam stereo image pairs and a stereo image pair from the Middlebury dataset were used. Among the three investigated methods, overall, the deep learning-based method, EDSR, is found to perform better than others in the image enhancement and in accurate disparity map estimation performance. The extensive comparison study using actual Mastcam imagery in this paper is considered as another contribution of this work. In The two Mastcams of Curiosity are shown in The Mastcams are multispectral imagers with nine bands in each. Here, we only focus on the use of the RGB bands for stereo image and disparity map generations. The left and right cameras of the Mastcam imager have different resolutions. Moreover, the cameras are not calibrated for stereo image formation, as they normally work independent of each other. To generate stereo images from these two cameras with different resolutions, a common practice is to downsample the high-resolution right camera image to the same as the lower one. After that, the stereo images are formed by following some standard procedures. It is well known that the downsampling of the high-resolution camera image is more economical but less effective, as the resulting stereo images will have lower resolution. The resulting disparity map derived from the resulting stereo images also has a low resolution. The motivation of this work is to improve the disparity map estimation by enhancing the low-resolution image and then estimating the disparity map using the enhanced left camera image and the right camera image.Bicubic interpolation ;Pansharpening-based method ,12;EDSR (Deep-learning based image super resolution method) .We briefly introduce the three image enhancement methods applied to enhance the low-resolution left camera image in this work. These three methods are:Bicubic interpolation is provided as an image resizing tool in image editing programs like Photoshop . In imagThe pansharpening-based image enhancement method ,12, of wThe aligned right camera image from the diffeomorphic registration step of the two-step image registration technique becomes the high-resolution panchromatic (pan) image and it is used to enhance the low-resolution left camera image using the Gram–Schmidt Adaptive (GSA) pansharpening technique . DiffereIn recent years, image super resolution using deep neural networks, also known as deep learning, has gained a lot of attention ,20,21,22The single-scale baseline EDSR architecture is used in this work. The components of the EDSR baseline architecture and the parameters for these components can be seen in detail in When training an EDSR model for Mastcam images, the corresponding high-resolution right camera images of the test images (low-resolution left camera images) are used in the training dataset. The down-sampled high-resolution right camera images at the image resolution scale of interest, which is two (×2), are used in training a model as well. Once the EDSR model is trained, the testing is straightforward by feeding the test low-resolution left camera image into the deep learning network with the trained EDSR model. The simplified block diagram of the processing steps of EDSR is shown in x is a point in the stereo image of the first camera, for this point, the epipolar line in the second stereo image can be considered as the projection of the ray from point x to the center of the first camera to the second camera image. Any point x’ in the second stereo image that matches to the first camera image must be on this epipolar line [In this section, we outline the processing steps to estimate the disparity map using the enhanced left camera image and the right camera image. A block diagram of the disparity map estimation processing steps can be seen in lar line  and thislar line . After tlar line  is applilar line . The low-resolution camera image enhancement and disparity map estimation are first demonstrated using an image section from the motorcycle image pair in the Middlebury stereo dataset . The tecTo imitate the different resolution stereo image pair scenario in the Middlebury motorcycle stereo image pair, the original left camera image is intentionally down-sampled four times (×4) and this down-sampled image is used for image enhancement with the three methods. It is worth mentioning that since the left and right camera images in the Middlebury stereo image pair are already rectified, no rectification process is applied and the disparity map is estimated directly from the stereo image pair. For the EDSR model training, no custom EDSR model is trained and the authors’ baseline training model for a scale of four (×4)  is used.The high resolution left and right camera images of the motorcycle image pair are shown in When computing the image quality measures, we used the original high-resolution left camera image as the reference (ground truth) image. The grayscale versions of the images are used when computing SSIM and RMSE scores. After generating the enhanced left camera images with the three methods, the disparity maps are estimated using the enhanced left camera images and the original right camera image. N is the total number of pixels in D. The location of the pixels in N disparity map pixel estimates for the applied method B. The "average absolute error” for method B is computed as: For comparing the disparity map estimation performances, an “average absolute error” measure is used to compare the accuracy of the estimated disparity maps of the three methods with respect to the ground truth disparity map. The ground truth disparity map is obtained using the original left and right camera images. Suppose To better visualize the differences in the estimated disparity maps of the three methods with respect to the ground truth disparity map and see how the errors relate to the scene, we generated four plots as shown in We enhanced the 20 original low resolution (LR) left camera images with the three methods. The list of the Mastcam image pairs used in this study can be seen in For EDSR, a custom EDSR baseline model  with 16 residual blocks was trained for 300 epochs. The 20 training images included in the training data set correspond to the high-resolution right camera images of the 20 low-resolution left camera images that are going to be enhanced. The loss plot from the EDSR training can be seen in Since the original left camera images in the 20 Mastcam image pairs are enhanced, there are no ground truth images to assess these enhanced images by the three methods. For this reason, a blind image quality assessment, natural image quality evaluator (NIQE)  is used It is however worth mentioning that even though the pansharpening-based method provides the lowest NIQE values (best performance) and provides visually very appealing enhanced images, it is noticed that some pixel regions in the enhanced images do not seem to be registered in the sub-pixel level. Since the NIQE metric does not take into consideration issues related to registration in its assessment, it clearly favors the pansharpening-based method over others. Similar to the investigation with the motorcycle image pair, the left camera images in the 20 Mastcam image pairs are intentionally down-sampled by two times (×2) and the down-sampled left camera images are enhanced with the three methods and the disparity maps are estimated using the enhanced left camera images and right camera images. This enabled assessing the image enhancement performances with image quality measures such as PSNR, RMSE since the original left camera images are used as the ground truth image. Moreover, the disparity map estimation performances are also evaluated since the disparity map which is estimated using the original left camera image is considered as the ground truth disparity map. Regarding the EDSR method in this investigation, we fine-tuned its architecture with respect to the number of residual blocks to see which EDSR architecture would perform better. We considered the number of residual blocks for fine-tuning since the residual blocks make the most crucial parts in EDSR’s architecture. We trained three other EDSR models with four, eight and 32 residual blocks for scale two (×2). We used the same 20 high resolution right Mastcam images in training these EDSR models for 300 epochs. With the previously trained EDSR model of 16 residual blocks, there is a total of four different EDSR models. We enhanced the “down-sampled (×2) left camera images” with these four EDSR models. We then applied five image quality measures to the 20 enhanced images from four EDSR models. In the disparity map estimation investigation, we first used the 20 original left camera images to generate ground truth disparity maps that will be used in performance comparisons with the average absolute error measure. We then intentionally down-sampled the original left camera images by a scale of two (×2) and applied the bicubic interpolation, pansharpening-based method, and EDSR (nresblock = 8) to enhance these downsampled left camera images. We then estimated the disparity maps using these enhanced downsampled left camera images and the right camera images. The rectification process in the disparity map estimation changes the view of the rectified stereo images and each rectification by the three methods could have a slightly different geometric transformation. The view of the rectified images could thus vary for each of the three investigated methods and the groundtruth case. As a result of of registration issues in the pansharpening-based method, the variation in the view of the rectified images is even greater in the pansharpening-based method with respect to the other two methods. In order to have a fixed view that align the estimated disparity maps and also allow to conduct performance comparisons, the estimated disparity maps using the rectified images are warped to the left camera image view using the inverse rectification transformation matrix that was initially applied in the rectification process. The overlapping sections in the warped disparity maps (from the three methods and the groundtruth) to the left camera image view are considered as a mask and the pixels in this mask are used only to compute the average absolute error measure for the disparity map.Since the two-step image registration in the pansharpening-based method utilizes the random sample consensus (RANSAC) algorithm , each siSSIM, RMSE, PSNR, HVS and HVSm values are computed for measuring the image quality of the enhanced “down-sampled left Mastcam images” with respect to the ground truth left camera image. Additionally, the NIQE measure is also applied. The plots of the six image quality values for 20 enhanced “downsampled Mastcam left camera images” can be seen in This paper introduced an in-depth study for the high-resolution disparity map estimation using the stereo Mastcam images with different resolutions acquired from the right and left Mastcam imagers of the Mars Curiosity rover. Among the three investigated methods, it is observed that the deep learning-based method, EDSR, had a better image resolution enhancement performance than the pansharpening-based method and bicubic interpolation. The impact of the low-resolution image enhancement on the disparity map estimation is examined and it is found out that a high resolution thus a more accurate disparity map estimation could be obtained after enhancing the low-resolution left camera image with EDSR. The pansharpening-based method, which is an adaptation of the two-step image registration technique, is observed to provide visually very appealing images as this was also confirmed quantitatively from the resultant NIQE measures. However, the performance of the pansharpening-based method heavily depends on the registration accuracy of the stereo images, which can be difficult when the image scene in the stereo image pair has varying depth of field. Other than improving the quality of the stereo products and 3D scene reconstruction in the Mastcam imagery, this work can also benefit to cellphones with dual cameras. Enhancing the low-resolution camera image in a dual camera cellphone setup can certainly result in better quality 3D cell phone imagery while reducing the cost of the cellphone. That is, instead of using two sophisticated cellphone cameras, the cellphone manufacturers can use only one high-resolution camera while enhancing the cheaper low-resolution cellphone camera via image enhancement.


In the absence of traditional communication infrastructures, the choice of available technologies for building data collection and control systems in remote areas is very limited. This paper reviews and analyzes protocols and technologies for transferring Internet of Things (IoT) data and presents an architecture for a hybrid IoT-satellite network, which includes a long range (LoRa) low power wide area network (LPWAN) terrestrial network for data collection and an Iridium satellite system for backhaul connectivity. Simulation modelling, together with a specialized experimental stand, allowed us to study the applicability of different methods of information presentation for the case of transmitting IoT data over low-speed satellite communication channels. We proposed a data encoding and packaging scheme called GDEP (Gateway Data Encoding and Packaging). It is based on the combination of data format conversion at the connection points of a heterogeneous network and message packaging. GDEP enabled the reduction of the number of utilized Short Burst Data (SBD) containers and the overall transmitted data size by almost five times. The task of deploying data collection networks in regions without any existing infrastructure poses a number of challenges for engineers. With the Internet of things (IoT) spreading its influence in cities and their suburbs, it is still an open question of how IoT can further extend in regions such as the Arctic or Antarctic, unpopulated deserts or tropical forests. The concept of smart autonomous devices operating in remote undeveloped areas has recently gained its own name—the Internet of Remote Things (IoRT) .There are a number of IoRT application areas, in which satellite communication (SATCOM) is of primary importance, being the only feasible option of integrating a local data collection system into the global network—shipping, vessel tracking, marine engineering , smart gApart from the obvious technical difficulties related to placing sensors in an unsupervised aggressive environment, the key problems in satellite IoRT are the high cost of sending messages and the occurrence of collisions and significant delays ,19,23,24Energy efficiency remains the main requirement for data transmission in the Internet of things as it directly affects the lifetime of autonomous devices. One of the factors that influence energy efficiency is the amount of transmitted data or the ratio between service data and useful payload. Optimizing such a ratio can be done by changing the existing network algorithms and protocols. In a heterogeneous structure, novel algorithms and approaches can be applied on the edge of the two networks, which is done in our work and which we find as the main distinctive feature compared with other existing approaches.We propose an architecture of a hybrid IoT-terrestrial-satellite network and a two-step Gateway Data Encoding and Packaging (GDEP) method for the LoRa-Iridium configuration that transforms a sequence of incoming LoRa Message Queuing Telemetry Transport (MQTT) messages into an output sequence of Protocol Buffers (Protobuf) Short Burst Data (SBD) containers.We present our results in the following order: De Sanctis et al.  provide In , the autPalma and Birkeland  propose In terms of the engineering design idea,  providesThe research of Bacco et al.  focuses In summary, all of the aforementioned works study the feasibility of satellite communication for IoT, formulating many open issues and addressing some of them. We see a potential gap in data transfer optimization on the edge of the two network parts—the terrestrial part and the satellite part. That is the primary aim of our study.A conceptual architecture of an IoT-terrestrial-satellite network that will be taken as the basis for our further discussion is shown in The scalability, reliability and energy efficiency of the network all depend on the choice of data transfer technologies and underlying protocols. Such choice also defines constraints on the amount of information transmitted in a single message and the format of transmitted messages. Currently, there are many technologies and protocols that differ in communication range, speed, frequency bands and modulation used during data transmission.energy efficiency;area coverage from a single base station;cost of service;possibility of using unlicensed radio frequency ranges.When choosing data transfer technologies and protocols in the heterogeneous IoRT setup, we consider the following key criteria:long data transmission distance. In many implementations it can reach 10 km or even more;higher energy efficiency compared with standard data transfer protocols.Low Power Wide Area Networks (LPWANs)  are usuaThe limitations of the technology are low speed and possible significant delays in data transmission. One of the commonly used LPWAN specifications is LoRa , with thAn alternative technology used in transmission of IoT data is Narrowband-IoT (NB-IoT) . The tecWith respect to satellite backhaul, there are two main providers of digital data transmission as of today: Inmarsat  and IridInmarsat satellites belong to the class of GEO systems, providing coverage up to 70° north and south latitude, that is, they reach almost the entire globe except for “snow caps”. The satellite system offers an Internet connection at speeds of up to 384 Kbps. A key feature of the platform is the presence of a controller, which allows implementing applications in a short time. There are ready-made Machine to Machine (M2M) , IsatDatThe Iridium satellite system, in turn, is a LEO constellation that provides data rates of up to 128 Kbps. The operator company offers M2M equipment based on Short Burst Data (SBD) 9602, 9603N and 9523 satellite modems, which allow transmitting short data messages. The key features of SBD satellite modems are their low power consumption and text messaging capabilities.The requirement to provide communication services in remote areas leads to the need for a coverage zone in the regions of the Far North and the Arctic. The only SATCOM that meets this criterion is the Iridium satellite system. Connecting an SBD Iridium modem to each IoT sensor is not feasible both in terms of cost and energy consumption. Thus, to organize the connection of IoT sensors via satellite communication channels, it is necessary to provide a gateway between the LPWAN network and the satellite communication channels. This design aligns with our conceptual model of Section 3.4During information transmission through a terrestrial network and further through a LEO satellite constellation, the method of information encoding is of great importance, since it affects the ratio of application and service data in the overall information stream. In our work, we focus entirely on the upper OSI layers, leaving the transport part of the stack unchanged.During the transfer of information between a single IoT sensor and the LoRaWAN gateway, only sensor data with a minimal payload can be transmitted. In general, an IoT device can be used to transmit readings of more than one sensor. In this case, each sensor’s value is complimented with an identifier or parameter name. When further transmitting information from the LoRaWAN gateway to the central data collection and analysis system, the end device identifier and the network parameters should be added to the data. As a result, most IoT systems use a key-value format for information encoding. With the modern trend of using open standards, we will omit some of the proprietary binary solutions and instead discuss the following open specifications designed for M2M connectivity: JavaScript Object Notation (JSON), binary JavaScript Object Notation (BSON), Concise Binary Object Representation (CBOR), MsgPack, JSONC and Protobuf.The JavaScript Object Notation (JSON) format  is a subThe binary JavaScript Object Notation (BSON) format  is a binThe Concise Binary Object Representation   and MsgPStructured information can also be transmitted using the Protocol Buffers (Protobuf) format . ProtobuThe summary of data presentation formats with their key properties is presented in Considering the predetermined nature of data structures transmitted from IoT sensors, Protobuf seems to be the best choice, as it minimizes the amount of service information while still preserving the key-value approach. The key technical task, however, is to seamlessly integrate the protocol into an existing network infrastructure.Endpoint data collection devices. One or several sensors can be connected to these devices; data transmission is performed according to the LoRa specification.Terrestrial LoRaWAN gateway.Terrestrial LoRa-Iridium gateway. MQTT broker is co-located with this element.Iridium satellite system for hybrid terrestrial-satellite network organization.Terrestrial Iridium gateway.Terrestrial Iridium-MQTT gateway.General data collection and analysis system.We considered a network architecture  to studyWe performed the task decomposition and defined the main stages of information transfer from endpoint data collection devices (1) to the data collection and analysis system (7).Each of the endpoint data collection devices (1) can have one or multiple sensors , and transmit data in the LoRa binary format to the LoRaWAN gateway (2). The LoRaWAN gateway (2) acts as a transparent bridge passing the information to a connected MQTT service through a broker. This information is encoded in the JSON format. The number of published MQTT messages corresponds to the number of received packets on the LoRa transmission channel. The LoRa-Iridium gateway (3) retrieves messages from the LoRaWAN gateway (2) through an MQTT broker. We assumed there were no constraints on the channel width and on the energy efficiency of transmission between (2) and (3) as they were equipped with a stationary power supply and communicated with each other via a high-speed connection.We selected MQTT as one of the standard and widely used protocols for data transfer in the IoT. The protocol was initially developed at IBM  and ArcoThe main task of the LoRa-Iridium gateway (3) is to convert messages coming from the terrestrial IoT network into the appropriate format of the SBD service.Each SBD message is transmitted via the satellite constellation (4) and the Iridium terrestrial gateway (5) to the Iridium MQTT gateway (6), which decodes messages back from SBD to JSON and publishes them through another MQTT broker to the data collection system (7).The GDEP method aims to optimize the amount of data transferred through a satellite link, which is the architectural bottleneck both in terms of speed and cost. The method consists of two connected steps . At the Step 1. Data encodingThe maximum payload size of the LoRaWAN message is 222 bytes when sending messages through an intermediate device and 242 bytes when sending information directly between the endpoint device and the gateway ;The maximum SBD payload size is 340 bytes ,53;The raw transmission delay in the LoRa network can vary from 60 to 1250 ms.To evaluate the effectiveness of the first step, we performed a network simulation taking into account the following constraints associated with network standards, protocols and hardware:The technical details of how JSON messages were encoded in Protobuf can be found on our GitHub project page .One thousand LoRa messages from endpoint devices with different amounts of sensors—from 1 to 13—were generated, then we compared the amount of transmitted data using the JSON and Protobuf formats. The generation of events by endpoint devices was carried out independently of each other with a fixed average probability value of an event occurring, so we used the Poisson distribution to determine the number of messages from the endpoint devices in one message from the sensor . We assuFormat conversion reduces the size of transmitted data by an average of 4.8 times. When using the Google Protobuf data presentation format, the size of the resulting IoT message on the terrestrial network varies from 70 to 126 bytes depending on the number of sensors (from 1 to 13). The maximum size of the SBD transmission is determined by the hardware, and in our case is 320 bytes; thus, it becomes possible to pack several Internet of things messages in one SBD transmission session. Upon receipt of the incoming messages, the LoRa-Iridium gateway buffers them and, when the buffer is larger than a specified size, packages the messages to perform transmission using the Iridium SBD communication channel.Step 2. Message packagingThe packaging step can be defined as a standard optimization problem. Let us define the following variables:Subject to the following constraints:The total size of messages fitted into one container cannot exceed the corresponding container sizeSince all messages of each type have to be transmitted,We define the lower limit of the number of messages Additional constraints can be defined in simulation modelling. These can be the lifetime of a single IoT device, the rate at which new messages arrive and, as a result, the number of simultaneously open SBD containers.p. The main task will be to choose the most suitable algorithm that uses the smallest number of containers and introduces a delay in the process of sending messages no more than Tp.The total transmission time of each message from endpoint devices to the data collection system should not exceed the threshold value—TSince the packing problem is NP-complete, the minimum number of required containers can be determined only by the exhaustive search method. We consider several existing approximate packaging algorithms ,57:Algorithm 1: “First suitable with one open container”. Messages are selected sequentially from the buffer and placed in the first container. If a new message cannot be placed in the current container, then a new container is opened. The filled container is closed and shipped.Algorithm 2: “First suitable with multiple open containers”. Messages are selected sequentially from the buffer and placed in the first container. If a new message cannot be placed in the current container, then the next free container is used. Unlike the previous algorithm, all containers remain open and are closed only after all messages from the buffer are placed.Algorithm 3: “First suitable with multiple open containers and message sorting”. The messages in the buffer are sorted in descending order of size. After that, messages are selected sequentially from the buffer and placed in the first container that has sufficient space. All containers remain open and close only after all messages from the buffer are placed.We compared the operation of the three algorithms using the architecture of a heterogeneous network . One thoIt can be concluded that Algorithm 1 is no more than 2% worse than algorithm 3. The difference in contained utilization between algorithms 1 and 3 is less than 1%. Given the limitations on the delay in sending messages and the resource-intensiveness of the algorithm, such a difference can be neglected.The delay associated with the process of packing depends on the number of open containers and is equal to the number of messages in one container multiplied by the number of open containers. Thus, to reduce the delay, we can only use algorithms with a minimum number of open containers.To verify the obtained results, an experimental stand consisting of the Laird DVK-RM186 kit, the LinkLab LoRaWAN Gateway shield, a Raspberry Pi3-based IoT MQTT station, the Iridium 9602 SBD modem and DirectIP (SBD) and MQTT servers was built . The latDuring the experiment the stand components interacted as follows:1. An end device from the Laird DVK-RM186 kit generated LoRa messages with a variable inter-arrival time from 1 to 20 s and a payload from 18 to 20 bytes. The messages themselves consisted of a pseudo-random sequence of characters emulating sensor data.2. Messages were transferred through the LoRaWAN gateway and persisted at a local MQTT server installed on the Raspberry PI based IoT station .3. Messages were then retrieved from the MQTT server into a First-In First-Out (FIFO) queue with the format converted from JSON to Protobuf.4. In the first scenario, each new message from the queue was placed in a new SBD container and immediately passed to the Iridium satellite modem. In the second scenario, a packaging algorithm was additionally applied.5. The Iridium 9602 modem normally responded with a confirmation within 5 s, indicating either successful transmission or failure. If the message transmission failed, an attempt was repeated up to 10 times. After 10 transmission failures, the message was deleted from the queue.6. After sending the message, the main characteristics related to the data transfer process—send status, size of the initial JSON message, size of the encoded Protobuf message, total transfer time—were recorded on the IoT station side, as well as on the server side.7. Steps 4–6 were repeated for subsequent messages from the queue.The experiment was carried out in an open area in the Moscow region. The data transfer process within each experimental setup lasted for about 12 min, then the LoRa message generation frequency was increased and the experiment was repeated. Within the 12-minute interval, about 40 SBD containers were sent, with 95% of them successfully arriving at the server side. The message transmission time related to passing data onto the Iridium network varied from 6 to 27 s see a, averagThese data can be interpreted from a different perspective, as shown in The second key characteristic was the maximal queue size formed of incoming LoRa messages b. It wasA packaging algorithm can be evaluated by the container utilization rate. In our work, we studied the case of a fixed container size, which on average accumulated three incoming LoRa messages. As shown in In this paper, we have proposed an architecture of a hybrid LPWAN-satellite communication network that takes into account the present state-of-the-art of the Internet of things with its standards and protocols, and makes it possible to deploy IoT systems in remote areas. Focusing on data transfer optimization, we proposed a data format change on the presentation layer that can be implemented in the gateway supplemented with a packaging algorithm.Our experimental results related to the Iridium network align well with the previous findings of other authors ,13,15,17With sensors deployed in an area without any existing network infrastructure, the LoRa technology seems to be one of the best choices currently available. The task of aggregating data from IoT sensors and transmitting them to the global network over satellite involves many levels of optimizations. In the present paper, we only discuss those which happen on the edge of the two networks: LPWAN and satellite.Optimizing data transfer enables the ability to either increase the number of sensors using the same gateway or to lower their power consumption, which is extremely important for scenarios of autonomous battery-powered devices or devices that use alternative energy sources.It has to be mentioned that using satellite communication has very significant limitations—high cost of service, low throughput and long delays. However, we still believe it to be the most feasible option when building a data acquisition system in remote regions without any terrestrial communication infrastructure.As a result of this work, an architecture of an IoT technological stack in hard-to-reach areas was presented. Information encoding methods were identified that allow for the ability to solve the problem of collecting information from remote sensors located in the absence of traditional communication channels, and practical verification of received results was obtained. The paper used simulation modelling to study the applicability of different methods of presenting information in the case of transmitting IoT data over low-speed satellite communications channels.A novel architecture of a heterogeneous data collection network for remote areas of the world, which includes energy-efficient technologies of the Internet of things at the data collection level, with low-speed satellite communication channels targeted at M2M and IoT being proposed.A new method (GDEP) of encoding information at the OSI presentation layer of a heterogeneous IoT network was developed, which by combining several encoding and packaging techniques, increases the efficiency of data transmission by 4.8 times.The proposed GDEP method was validated on a simulation model and on experimental equipment.We see the following main contributions of this paper:The GDEP method proposed in the paper allows for the use of IoT technology stack in remote regions by integrating it with the SBD satellite short message service. The GDEP method allowed for the reduction in the volume and number of SBD messages during data transmission via low-speed satellite communication channels, which made it possible to reduce the size of transmitted data by almost five times. Reducing the cost of data transmission leads to an increase in the economic efficiency of SATCOM for organizing data transmission networks in remote areas, which do not have a telecommunication infrastructure.The Internet of remote things (IoRT) is a recently introduced paradigm describing monitoring and control networks in hard-to-reach areas. These networks usually have very limited throughput and tend to be heterogeneous, which opens a way to new models, methods and algorithms for optimizing data transfer, energy efficiency and traffic balancing. We have identified several tasks that need to be addressed within the framework of IoRT and that can benefit from the findings of the present paper.We believe that the GDEP method can be further improved and generalized for the satellite-IoT scenario with any combination of initial data transfer technologies, encoding formats and container sizes. This will require additional simulation and experimental studies.The effect of data transfer optimization in a heterogeneous satellite-terrestrial network on the lifetime of battery-powered devices is another important issue. Normally, the question is raised in relation to end devices running LoRa, IEEE 802.15.4 and similar energy-efficient protocol stacks, while network gateways are considered to be supplied with a constant energy source. With the active development of micro-energy harvesters, more complicated and resource-intensive network devices can become fully autonomous as well.Finally, network clustering can be additionally applied to divide a complex data acquisition network into multiple components, each comprising a satellite gateway. Within each component, incoming data streams can be aligned in such a way that the GDEP container utilization rate is maximized.


Measurements of forces during machining, especially thin-walled structures typical of the aviation industry, are important in the aspect of instability caused by vibration. One of the last stages of manufacturing by machining is the finishing treatment and deburring of the product’s edges. Brushes with ceramic fibres are often employed in deburring, especially for large-sized elements specific to the aviation industry due to the possibility of automatic machining directly on machining centres. This study set out to analyse the effect of variable brushing conditions on axial forces and the selected surface layer properties of AW-7075 aluminium alloy. Experimental studies have examined factors such as surface roughness and topography, axial cutting force in ceramic brush treatment and surface free energy in the aspect of adhesive joints. The tested variable process parameters were the fibre material and the adjustment sleeve spring stiffness. Based on the tests, it was found that the axial force applied by the brush was more strongly connected with the spring stiffness rather than the type of bristle. For most cases, an increase in the value of free surface energy after brushing was observed compared to the initial machining which was milling. Brushing is a type of surface treatment with a rotary tool. Brushing tools are used in surface treatment processes as well as for deburring, edge rounding, fine shining, and broadly understood surface cleaning. Brushing is found in a number of industries requiring high-quality machined surfaces ,2. AnalyBurr is found at the tool exit from the cutting zone . Given tThe effectiveness of using special brushes in deburring the Inconel 718 alloy and the titanium alloy Ti6Al4V following drilling was proven in earlier studies [Diamond-abrasive grain tools were proven to be a suitable solution in frosting and other metal treatment operations in a study by Novotný et al. , the prip, feed rate) and the number of brush passes on the surface roughness of the test material. It was shown that brushing, compared to grinding, provides a cost-effective and efficient solution for reducing the surface roughness and removing machining marks after turning.In their work, Rodríguez et al.  presenteKitahara et al. ,12 examiThe cutting forces and their change during machining are among the key machinability indices, which is why we have already attempted to approach them in our previous scientific works . AnalysiTherefore, the analysis of real cutting force components  and vibrThe concept of automatic lapping of surfaces with ceramic brushes after milling was presented by Kim et al. , who anaComputer modelling is a cost- and time-saving tool which reduces the number of iterations in the optimisation of cutting data by reducing the amount of machining error and waste. Moreover, the distribution of cutting forces is of great importance in the aerospace and medical industries, demanding high-accuracy of complex thin-walled elements . Monies As the cutting force is, to a great extent, affected by accidental adhesion and built-up edge, the resulting fluctuations could substantially decrease the quality of surface finish and the shape and dimensional accuracy  while inThe energy state of the surface layer of modern industrial-grade structural materials  found, for example, in the aviation industry, is essential in technologies where effectiveness heavily relies on proper adhesion. These technologies include adhesive bonding, sealing, and applying a protective or decorative coating. Adhesive joining of structural materials therefore must be preceded by proper surface preparation of substrates. Burrs typically appear on the edges of machined parts, i.e., the area which is critical to the formation of a strong joint among adherents. Therefore, deburring by brush treatment could constitute the last operation prior to joining, adhesive bonding or coating. Therefore, it is of great importance to verify the surface free energy after brushing in order to decide whether brushing technology is necessary. The preparation of the surface layer of adhered substrates is absolutely critical, particularly in a situation when the bonded assemblies are expected to perform well under variable temperature cycle loading ,29,30,31Ceramic brushes are a relatively recent solution introduced for deburring and surface layer properties modification. By analysing surface roughness, topography, surface free energy, and forces occurring during brushing, the effect of tool on the workpiece can be determined.The material tested was AW 7075-T651 aluminium alloy which offers good machinability, strength, thermal conductivity, and corrosion resistance. These properties earmark it for use in the production of thin-walled components. The chemical composition and physical properties of the substrate material used in the tests are given in The treatment was performed with the use of a XH712G vertical machining centre and auxiliary equipment.The readings were carried out by the dynamometer fixed in a machine vice holding the test specimens. The specimens were perpendicular to the brush axis. c = 500 m/min, fz = 0.1 mm/tooth, ap = 0.5 mm) were employed in order to ensure that the finish of the surfaces exhibited constant roughness. Thus, produced surfaces were analysed to determine the effect of brushing on the value of surface roughness parameters in relation to milling.Prior to ceramic brush treatment, all specimens were subjected to milling with an Iscar solid carbide milling cutter (diameter D = 20 mm) with the tool geometry dedicated for light alloy machining. Constant milling parameters  (Ra and Rz) whereas the surface topography was measured using a Hommel-Etamic 3D T8000 RC120-400 device .The texture measurements after brushing were performed with a Keyence VHX-5000 digital microscope . The results from the measurements provided the data for the assessment of the impact of particular fibre types and brush adjustment sleeves on the intensity of the impact on the machined surface.The contact angle measurements of the surfaces under tests were carried out with distilled water and diiodomethane. The measuring liquids with a constant volume of 4 mL were automatically deposited on the test surface with the PGX goniometer . Ten measurements of the contact angle were performed for each measuring liquid and on each tested surface. Subsequently, the obtained values were averaged and served to estimate the free surface energy of specimens.Following five repetitions for each set of cutting data, we obtained the results represented in the graph in By establishing stable machining zones, we managed to filter out the non-machining results.Characteristically, the graphs showing characteristics of fibres that were more rigid contained typical cutting force peaks when the tool was entering into the cut .The first point of contact of the less flexible brushes was characterised by high dynamics, ergo the peaks in the graphs, as opposed to the less aggressive tools, entered gently into the workpiece. The surface roughness measurements after brush treatment assessed the effectiveness of the process, leading to the reduction of the roughness parameters after milling as a result. z.Surface topography analysis allows for a more complete analysis compared to 2D parameters. Considering the blue fibres and the “high” spring stiffness in the brush adjustment sleeve, characteristic grooves on the surface are visible which are typical products of machining with rigid fibres.From the obtained test results, it can be seen that the type of brush employed in treatment has a great effect on the values of the measuring liquid contact angle measured on the surface of aluminium alloy substrates in relation to the specimens prior to brushing. The highest drop in the contact angle for distilled water was recorded for the surface treated with the “pink” fibre brush together with “low” spring stiffness in the brush adjustment sleeve compared to the samples before brushing. The decrease was approximately 12%.The most substantial increase in the value of the surface free energy was observed for samples subjected to treatment with a pink-fibre brush and “low” spring stiffness in the adjustment sleeve, which was approximately 15% compared with the specimens prior to brushing.Machining with high-stiffness fibres led to peaks in the axial force applied by the brush entering into the cut (not observed in the case of the most flexible brush—pink); the peaks might indicate the unsuitability of stiff-fibre brushes for thin-walled flexible substrate applications;When machining with the low-stiffness spring in the adjustment sleeve, the axial forces were substantially lower with increasing fibre stiffness;Brush stiffness affected the axial force in brushing with the high-stiffness spring only to a small degree;The tool entry into the cut showed higher standard deviations owing to the highly dynamic character of the process;The lowest surface roughness values were obtained after brush treatment with red fibres, for both “low” and “high” spring stiffness settings.The use of rigid fibres  may lead to carving micro-grooves on treated surfaces, which results in an increase in the surface roughness parameters (particularly Sz);The use of “pink” brushes and “low” spring stiffness settings in the adjustment sleeve resulted in a significant increase in the value of the surface free energy by approximately 15% compared to the samples prior to brushing. This is of paramount importance, particularly in the aviation industry where adhesion technologies—highly sensitive to improper bonding—are employed in adhesive joint assemblies and in various types of protective or decorative coatings;The effect of the spring type on axial force was higher than that of the brush fibre stiffness. Nevertheless, it is more relevant for other effects of treatment The aim of the tests on 7075 aluminium alloy was to determine the effect of various cutting conditions of brushing on the values of axial force, surface roughness, and surface free energy. the conclusions are as follows:Given the reported results, it must not be forgotten that the forces emerging during brushing with ceramic fibre tools are considerably smaller than in other finishing treatments. Low forces translate to a lower risk of vibration, which is among the key risks in the machining of low-stiffness, thin-walled parts found in various applications in the aviation industry.


The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets”  from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%,  Diabetes Mellitus is a disease characterized by a dysregulation of the natural homeostasis of glucose concentration in the body. It has a projected prevalence of 9.9% of the global population by 2045 (629 million people affected), and the estimated yearly cost in healthcare expenditure by the same year is 776 billion USD . ApproxiAn Artificial Pancreas (AP) is a device that feeds continuous glucose measurements into an insulin pump in real time and continuously adjusts insulin dosage delivered to the patient. It is one the most promising solutions to the complications of T1D, and several prototypes have been under development over the last few years, including commercially-available  devices.Exercise monitoring is widespread today in most parts of the world, with many different devices designed and marketed to provide estimation of the intensity, type, and duration of physical activity. Integration of all available signals from wearable physical activity monitors (wearables) remains an open issue , and manThe motivation for this work arises from the possibility of compensating the greater estimation error shown during exercise periods. Our working hypothesis is that exercise monitoring devices could provide information to a CGM device that allows for a real-time correction of the shift in glucose estimation during physical activity. The work presented in this paper analyzes the influence of the different signals provided by three different wearables  on the accuracy of a CGM device during aerobic exercise. Each wearable signal is critically selected or discarded as input for a multiple regression model designed to compensate the measurement error induced by the exercise session. The final regression model improves the accuracy of the CGM signal significantly during the exercise period, and baseline accuracy is maintained during the resting period. It is shown here that only two wearable signals are necessary to enhance the CGM measurements, and that redundancy of sensors only marginally increases the accuracy of the CGM estimations.This paper is structured as follows. First, a breakdown of the methods, models, protocol, and datasets used is provided, including a brief description of all the devices employed in the study. Secondly, the results of the validation of the model utilized will be provided, along with the critical selection of signals to be used in the model. Then, a critical discussion of the results will be exposed, followed by the final conclusions.The dataset used here was obtained from a longitudinal, prospective, interventional study with the goal of analyzing the performance of an exercise-challenged closed-loop controller in T1D people . A total® insulin pumps, and two Enlite-2®  CGM sensors were inserted in different parts of the abdomen the day before the trial by the subjects. Plasma glucose (PG) as a reference value was measured using the YSI 2300 Stat Plus Glucose Analyzer , with a frequency of 15 min. The exercise schedules were as follows:Aerobic routine: Patients exercised doing three bouts of 15 min on a cycloergometer at 60% of the patients maximum capacity with five minutes of rest between them.Anaerobic (resistance) routine: Patients exercised doing five bouts of eight repetitions of four different exercise sets of 15 min at 70% of the patients’ maximum capacity with 90 s of rest between sets.Each participant underwent three aerobic and three anaerobic exercise tests. The order of the type of exercise was randomized, but the same type was carried out for three consecutive trials. The subjects used Paradigm Veoi, i, defined as i in years. Lastly, i as measured at the beginning of the experiment.The exercise intensity was converted into units of heart rate, which was different for each patient and calculated as follows:Fitbit Charge HR™: a physical activity monitor that tracks several exercise signals: (1) heart rate, (2) steps, (3) floor level, (4) Metabolic equivalents (METs), and (5) calories burned.® Band 2 : a multipurpose device that tracks physiological variables such as: (1) heart rate, (2) steps, (3) galvanic skin response, (4) skin temperature and (5) movements.Microsoft® : a commercially-established heart rate monitor.Polar heart rate monitor, Model RCX3Off-the-shelf physical activity monitors were used to obtain the different biometric variables registered in the study. Patients wore three different devices simultaneously:FHR stands for F from the Fitbit Device and HR from the Heart Rate signal.The above listed variables were recorded with a sample rate of one minute and downloaded at the end of each trial. Each device provided a different number of signals, which will be listed next, for a total of 11 signals. The nomenclature of each signal needs to be defined in advance, since they will be referred to using this notation from here on. For each signal, a three-letter code was defined representative of the nature of that signal. The first letter of the triplet was the initial letter of the device that provided that signal, and the second and third letters were an abbreviation of the name of the signal itself. For example, the signal named FHR: Fitbit Heart Rate; measured in beats per minute; can be used to estimate exercise intensity.FST: Fitbit STeps; the number of steps walked.FLV: Fitbit LeVel; the number of floors of stairs climbed.FME: Fitbit MEts; the metabolic equivalent of tasks; an estimation of exercise intensity normalized for each patient.FCA: Fitbit CAlories; calories burned.MHR: Microsoft Band Heart Rate.MTM: Microsoft Band TeMperature; skin temperature.MGS: Microsoft Band Galvanic Skin Response; an estimation of the skin electrodermal activity, also known as the conductance level of the skin.MST: Microsoft Band STeps; number of steps walked.MMO: Microsoft Band MOvements; accumulated movement magnitude registered by the accelerometer.PHR: Polar Heart Rate.i at timestamp k. Similarly, i is a time series corresponding to a particular experiment and CGM signal.Thirty six trials were completed, and data were collected for all of them. In order to align CGM and YSI data, CGM data were linearly interpolated and rounded to one sample per minute. Following the rationale described in , data ari and n is the total number of samples available for i.CGM accuracy was evaluated using the Mean Absolute Relative Deviation (MARD), calculated as:baseline value of each wearable signal and trial from the signal itself. The baseline of each signal was calculated as the median value of that particular variable for each data stream evaluated only in the resting period. In an out-of-clinic environment, this value is trivial to obtain since it would be the equilibrium value of the signal during resting or sleeping periods.Wearable data were pre-processed by removing the The availability of the different wearable variables, defined as the complementary value of the percentage of missing samples, varied from signal to signal. In n. Finally, the new enhanced CGM (eCGM) estimations, corrected using the new estimated error, is calculated as follows:i, Linear regression models were used to fit the CGM error data, using only the wearable signals as inputs. The intercept of the model was forced to be zero in order to better compensate only the error observed in the exercise period. The linear regression model follows the following equation:Lack of availability of synchronous data to the CGM-YSI available pairs.Lack of representation of the type of physical activity performed in the study: cycloergometer aerobic exercise.High correlation between wearable signals. A high correlation between two signals implies that the amount of information carried by each one of those signals is similar; thus, one of them can be eliminated without hindering the predictive capabilities of the model.MARD improvement evaluated in the validation sets see  below. AIn order to determine which of the wearable signals in the study were more relevant for our purposes, a backwards elimination of variables approach was followed, in which variables were sequentially discarded as inputs to the correction model, based on:If a variable complied with the above rules, it was removed from the inputs of the multiple regression model, and the model was fit again to the data and the outputs re-evaluated, until no improvement of the error was achieved by removing any of the inputs.The predictive power of the model was validated by performing a non-exhaustive leave nine-out cross-validation of the model. Out of the set of 27 streams of data, two thirds  were selected as a fitting set, and the remaining data were used to validate the fitted model. Model accuracy was evaluated on the validation set, the results stored, and the process was repeated, randomly selecting new fitting and validation sets. This process was repeated 20 times. All the accuracy metrics shown onward were those averaging the results throughout all the 20 validation sets of nine streams of data.Any wearable with 30% or more samples not usable to fit the model, such as missing samples, disconnections, or out of bounds samples, was discarded from the fit. Following the backwards elimination philosophy previously detailed, the first two wearable signals discarded from the model fit were MST and PHR due to their low availability in Next, the correlation between the remaining signals was computed, with the results in The correlations between FLV, FME, and FCA  showed that those three variables carried essentially the same information. FME was deemed a more relevant variable than the other two due to its continuous nature (FLV is discrete by definition) and the fact that it was normalized by the baseline energy consumption of each patient, which suggests that a linear model using this variable as an input would require less individualization than one using the other variables. Thus, it was decided to discard FLV and FCA from the model fit.Similarly, both FHR and MHR were highly correlated  rendered the patient static for most of the exercising period, which would result in inaccurate estimations of the steps. Furthermore, the histogram of the signal FST in Using the five remaining signals as inputs, the linear model was fit, and the results of the MARD improvement in the validation sets were recorded. A single input was removed one at a time, the model’s performance recorded, and the cycle repeated, removing another input and using the others. The input elimination that further worsened the MARD in the exercise period, or the one that most worsened the MARD for the whole trial period in case of equal exercise MARD values, was permanently eliminated. The sequence of eliminations and the results on the MARD reduction are shown in The model improved plasma glucose estimations as inputs were removed from the system, especially when looking at the whole period of monitoring . However, the global MARD reduction from removing the two last signals, MTM and MGS, was minimal. MARD in the exercise period was also reduced by removing the same signals except when removing MTM. Thus, the final selected list of inputs for the model is:all available data and not separating any for validations.The final order of the model was two, since only two signals were used as the input. The average values of coefficients for the model fit are shown in MARD results for the proposed model are shown in MARD during the exercise period was reduced drastically and significantly, from 17.46% to 13.8%, which was much closer to the average MARD of the original CGM of 13.61%. The total MARD  was reduced slightly to 12.97%, and the error during the resting period was increased marginally to 12.88%, but both figures were not significantly different than those presented by the original CGM.The validation data were evaluated using different acceptability criteria. The Clarke error grid analysis of the data  is depicThe numeric values of the results for the Clarke EGA can be seen in In addition to the Clarke EGA analysis, In The first outcome that can be extracted from the results is that further integration of physiological signals in CGM devices is desired. CGM devices use algorithmic rules to fit the chemical measurements of the subcutaneous probe into relatively accurate plasma glucose values. If the data extracted from wearable devices were to be integrated in those algorithms, glucose estimation would benefit greatly.The proposed linear regression model (composed of only two parameters) achieved a reduction in the MARD during aerobic exercise that practically nullified the influence of the exercise on the CGM estimation error. Indeed, the MARD was significantly reduced from 17.46% to 13.8%, much closer to the global average MARD value for the original CGM readings of 13.61%. This achievement was the main reason to choose a regression model for error compensation, which validated the starting hypothesis.The regression model presented here differed from other studies involving wearables in the AP see ,12) in t in t12])The Clarke EGA shown in The results shown in This work did not focus on the nature of the signals used in the model. Rather, the focus here was on the user-level data that can be easily extracted from a device, which translates into a more straightforward implementation of the model in portable devices. This implies that some of the signals that were estimated from each of the devices using hidden algorithms might not be reproducible from other raw data, e.g., FME was an energy expenditure estimation by the Fitbit device calculated from its sensors, which can differ from implementations in other devices. In a recent study comparing different research and consumer devices, an error range of 10% was found for heart rate and energy expenditure measurements .It must be noted that the current approach has some drawbacks due to its intentional simplicity. First, while the CGM error was reduced significantly in the exercise period, this was achieved by means of a direct algebraic link to the signals of the wearable devices, which may transport noise from the wearable into the CGM, but that problem was not observed in our data. Additionally, it must be mentioned that more complex dynamic models might have further enhanced CGM accuracy, but the design of such models is beyond the scope of this work.This paper showed that continuous glucose estimation can benefit from the integration of signals readily available from wearables. CGM estimation error was improved during the exercise period and was not significantly increased otherwise. The model proposed was composed of two parameters; however, a simpler model composed of only one parameter can also yield acceptable results, and the technology is readily available with off-the-self devices.


Several reports indicate that spatial perception in blind individuals can be impaired as the lack of visual experience severely affects the development of multisensory spatial correspondences. Despite the growing interest in the development of technological devices to support blind people in their daily lives, very few studies have assessed the benefit of interventions that help to refine sensorimotor perception. In the present study, we directly investigated the impact of a short audio-motor training on auditory and proprioceptive spatial perception in blind individuals. Our findings indicate that auditory and proprioceptive spatial capabilities can be enhanced through interventions designed to foster sensorimotor perception in the form of audio-motor correspondences, demonstrating the importance of the early introduction of sensorimotor training in therapeutic intervention for blind individuals. Recent evidence suggests that some spatial capabilities in blind individuals may be delayed or compromised . This haDespite their potential usefulness for rehabilitation purposes, the benefit of interventions based on sensorimotor contingencies, such as audio-motor correspondence, has been barely studied in the blind population. Conversely, the use of auditory information coupled with visual or motor feedback has been mainly studied in robotic therapy systems to motivate or guide patients in the execution of performance tasks , generalTo date, research investigating the effect of auditory information to improve spatial perception in the case of blindness mainly focused on the evaluation of sensory substitution devices which tend to substitute vision with audition without specifically providing sensorimotor correspondences . Only feFor this reason, in the present study, we assessed the impact of an audio-motor training on spatial capabilities in visually impaired individuals, to test whether experiencing an auditory feedback of body movements can refine spatial mapping across multiple domains, namely auditory and proprioceptive domains. With this aim, we compared auditory and proprioceptive localization accuracy before and after a short sensorimotor training in which passive movements of the dominant arm of participants were enriched with a continuous or discrete audio feedback that creates a spatial audio-motor association. To assess the presence of generalization effects, we examined whether auditory and proprioceptive functions were improved also on the untrained side of the body, namely the non-dominant arm.n = 7; age: 32 ± 4) and a blind training group  who performed the same training. A t-test confirmed that the two groups did not differ in terms of chronological age . Blind participants have been considered as early blind since the loss of vision occurred within the third year of age despite the fact that diagnosis was known at birth. The clinical details of the early blind participants are reported in The study involved 16 participants with no known neuromuscular disorders and naïve to the task. The participants were divided into two groups: a sighted training group (Assessment phase) where two different aspects of spatial cognition were investigated  and one training session (Training phase) performed between the pre-test and post-test sessions. The first assessment task is related to the auditory domain and investigated participants’ ability to localize sounds in space (Reaching of auditory cue task) while the second assessment task is related to the proprioceptive domain and investigated the participants’ ability to reproduce a position in space (Joint position matching task). The tasks included in the Assessment phase have been already presented in Training phase, the trained arm was always the dominant one. This allowed us to assess whether the training effect generalizes to the untrained (non-dominant) arm.The protocol consisted of one pre-test and one post-test session  continuous back-and-forth movement along the setup; (b) discrete back-and-forth movements where the participants’ arm was positioned for 1 s over each of the sixteen loudspeakers embedded in the setup. The main aim of the training was to couple the proprioceptive feedback deriving from arm displacement with the auditory feedback deriving from the auditory source positioned on their wrist. The differentiation between continuous and discrete movements helped participants to respectively explore the setup and understand where each target position was placed by combining auditory and proprioceptive information. The ABBI was programmed in remote control, therefore, the audio command was triggered by the experimenter using a mobile phone. The wearable device produced a continuous pink noise sound.Reaching of Auditory Cue and the Joint Position Matching tasks, we computed the distance error in millimeters between each target and indicated position and then averaged across all target positions, extracting two variables: Matching Error (ME) and the Variability (SD).In order to evaluate the accuracy and the precision of participants in both the Matching Error represents a measure of accuracy or its inverse, bias. It is defined as the Euclidean distance between the target and the final arm position.xEE is the participants’ final position and xTG is the Target position. This variable is then averaged across targets.where N is the number of Target repetitions (5), The Variability (SD) is a measure of precision and it is evaluated as the standard deviation of the error positions.xEE−xTG, and N is the number of target repetitions. SD is evaluated for each target and then averaged.where d is the error distance For both variables, we performed the incremental difference pre-post training (Δ), as follows:varpre represents the performance at the pre-training assessment session and varpost represents the performance at the post-training assessment session.where p < 0.05), we applied the post hoc t-test with Bonferroni correction. F = 10.33, p = 0.003) while neither main effect of side  nor interaction between group and side  exist, suggesting that overall sighted individuals are less accurate than blind individuals for audio spatial localization . Moreover, as can be seen in t(6) = 5.3, p = 0.002] and blind individuals  improved their performance. Similarly, for the proprioceptive domain, there is a significant main effect of group  and time  but neither main effect of side  nor interactions among factors . t(15) = 5.76, p < 0.0001]. Moreover, as can be seen in t(8) = 4.9, p < 0.01] while sighted individuals showed a weaker improvement .For what concerns the analysis related to spatial accuracy (ME), we found that for the auditory domain, there is a significant main effect of group  while neither main effect of group nor interaction between factors has been found. p > 0.05) but it is much higher for the dominant compared to the non-dominant side in the proprioceptive domain  = 2.7, p = 0.016).For what concerns the analysis related to the incremental difference (Δ) of ME, which is the change of accuracy between pre and post training scaled by the initial error, we performed a two-way ANOVA with group  and side  as main factors. The statistical analysis reported a significant side effect for the proprioceptive domain  nor for the proprioceptive domain . Results for SD are depicted in For what concerns SD, the statistical analysis revealed that no effect of time on SD is present neither for the auditory domain  side for both sighted and blind individuals, while generalization effects much evident for the auditory domain in both groups. This result is in line with previous findings showing that auditory spatial perception in the blind can be enhanced with a proper training based on multisensory feedback  and thatThe main aim of this study was to assess whether a training based on multisensory (audio-motor) feedback can improve spatial perception, more specifically can calibrate altered proprioceptive function. Participants were trained to couple the proprioceptive feedback deriving from arm displacement with the auditory feedback provided by the external source positioned on their wrist. On the contrary, most of the studies conducted so far have investigated the effect of more artificial training based on the use of sensory substitution devices. These approaches typically require to learn how to transform visual properties of a stimulus into auditory or tactile information. Specifically, for the blind, visual-to-auditory sensory substitution devices artificially translate visual properties of a stimulus into auditory information by means of specifically developed devices that mimic the physiological functions of the visual modality . For exaIn conclusion, we demonstrated that spatial perception can improve in blind individuals thanks to training based on audio-motor contingencies, confirming the importance of multisensory experiences to acquire spatial competence. Overall the findings of the present study confirmed the importance of visual experience in the construction and calibration of non-visual spatial maps and stressed the importance of early therapeutic intervention to support the acquisition of fundamental spatial competencies from infancy.The datasets generated for this study are available on request to the corresponding author.The research conformed to the ethical standards laid down in the 1964 Declaration of Helsinki and was approved by the local ethics committee (ASL3 Ligure). Each participant signed a consent form conforming to these guidelines.AC, GC, and MG developed the design of the study. AC collected most of the data. GC helped in the final part of data collection. AC and GC analyzed the data and wrote the manuscript. MG reviewed the manuscript and provided feedback for discussion of results.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.