Brain white matter correlates of learning ankle tracking using a wearable device: importance of the superior longitudinal fasciculus II.

BACKGROUND: Wearable devices have been found effective in training ankle control in patients with neurological diseases. However, the neural mechanisms associated with using wearable devices for ankle training remain largely unexplored. This study aimed to investigate the ankle tracking performance and brain white matter changes associated with ankle tracking learning using a wearable-device system and the behavior-brain structure relationships in middle-aged and older adults. METHODS: Twenty-six middle-aged and older adults (48-75 years) participated in this study. Participants underwent 5-day ankle tracking learning with their non-dominant foot using a custom-built ankle tracking system equipped with a wearable sensor and a sensor-computer interface for real-time visual feedback and data acquisition. Repeated and random sequences of target tracking trajectories were both used for learning and testing. Ankle tracking performance, calculated as the root-mean-squared-error (RMSE) between the target and actual ankle trajectories, and brain diffusion spectrum MR images were acquired at baseline and retention tests. The general fractional anisotropy (GFA) values of eight brain white matter tracts of interest were calculated to indicate their integrity. Two-way (Sex × Time) mixed repeated measures ANOVA procedures were used to investigate Sex and Time effects on RMSE and GFA. Correlations between changes in RMSE and those in GFA were analyzed, controlling for age and sex. RESULTS: After learning, both male and female participants reduced the RMSE of tracking repeated and random sequences (both p < 0.001). Among the eight fiber tracts, the right superior longitudinal fasciculus II (R SLF II) was the only one which showed both increased GFA (p = 0.039) after learning and predictive power of reductions in RMSE for random sequence tracking with its changes in GFA [ß = 0.514, R2 change = 0.259, p = 0.008]. CONCLUSIONS: Our findings implied that interactive tracking movement learning using wearable sensors may place high demands on the attention, sensory feedback integration, and sensorimotor transformation functions of the brain. Therefore, the SLF II, which is known to perform these brain functions, showed corresponding neural plasticity after such learning, and its plasticity also predicted the behavioral gains. The SLF II appears to be a very important anatomical neural correlate involved in such learning paradigms.

Towards intelligent and integrated pest management through an AIoT-based monitoring system.

BACKGROUND: Main bottleneck in facilitating integrated pest management (IPM) is the unavailability of reliable and immediate crop damage data. Without sufficient insect pest and plant disease information, farm managers are unable to make proper decisions to prevent crop damage. This work aims to present how an integrated system was able to drive farm managers towards sustainable and data-driven IPM. RESULTS: A system called Intelligent and Integrated Pest and Disease Management (I2 PDM) system was developed. Edge computing devices were developed to automatically detect and recognize major greenhouse insect pests such as thrips (Frankliniella intonsa, Thrips hawaiiensis, and Thrips tabaci), and whiteflies (Bemisia argentifolii and Trialeurodes vaporariorum), to name a few, and measure environmental conditions including temperature, humidity, and light intensity, and send data to a remote server. The system has been installed in greenhouses producing tomatoes and orchids for gathering long-term spatiotemporal insect pest count and environmental data, for as long as 1368 days. The findings demonstrated that the proposed system supported the farm managers in performing IPM-related tasks. Significant yearly reductions in insect pest count as high as 50.7% were observed on the farms. CONCLUSION: It was concluded that novel and efficient strategies can be achieved by using an intelligent IPM system, opening IPM to potential benefits that cannot be easily realized with a traditional IPM program. This is the first work that reports the development of an intelligent strategic model for IPM based on actual automatically collected long-term data. The work presented herein can help in encouraging farm managers, researchers, experts, and industries to work together in implementing sustainable and data-driven IPM. © 2022 Society of Chemical Industry.

Honey Bee Colony Population Daily Loss Rate Forecasting and an Early Warning Method Using Temporal Convolutional Networks.

The population loss rate of a honey bee colony is a critical index to verify its health condition. Forecasting models for the population loss rate of a honey bee colony can be an essential tool in honey bee health management and pave a way to early warning methods in the understanding of potential abnormalities affecting a honey bee colony. This work presents a forecasting and early warning algorithm for the population daily loss rate of honey bee colonies and determining warning levels based on the predictions. Honey bee colony population daily loss rate data were obtained through embedded image systems to automatically monitor in real-time the in-and-out activity of honey bees at hive entrances. A forecasting model was trained based on temporal convolutional neural networks (TCN) to predict the following day's population loss rate. The forecasting model was optimized by conducting feature importance analysis, feature selection, and hyperparameter optimization. A warning level determination method using an isolation forest algorithm was applied to classify the population daily loss rate as normal or abnormal. The integrated algorithm was tested on two population loss rate datasets collected from multiple honey bee colonies in a honey bee farm. The test results show that the forecasting model can achieve a weighted mean average percentage error (WMAPE) of 17.1 ± 1.6%, while the warning level determination method reached 90.0 ± 8.5% accuracy. The forecasting model developed through this study can be used to facilitate efficient management of honey bee colonies and prevent colony collapse.

An Automated and Continuous Plant Weight Measurement System for Plant Factory.

In plant factories, plants are usually cultivated in nutrient solution under a controllable environment. Plant quality and growth are closely monitored and precisely controlled. For plant growth evaluation, plant weight is an important and commonly used indicator. Traditional plant weight measurements are destructive and laborious. In order to measure and record the plant weight during plant growth, an automated measurement system was designed and developed herein. The weight measurement system comprises a weight measurement device and an imaging system. The weight measurement device consists of a top disk, a bottom disk, a plant holder and a load cell. The load cell with a resolution of 0.1 g converts the plant weight on the plant holder disk to an analog electrical signal for a precise measurement. The top disk and bottom disk are designed to be durable for different plant sizes, so plant weight can be measured continuously throughout the whole growth period, without hindering plant growth. The results show that plant weights measured by the weight measurement device are highly correlated with the weights estimated by the stereo-vision imaging system; hence, plant weight can be measured by either method. The weight growth of selected vegetables growing in the National Taiwan University plant factory were monitored and measured using our automated plant growth weight measurement system. The experimental results demonstrate the functionality, stability and durability of this system. The information gathered by this weight system can be valuable and beneficial for hydroponic plants monitoring research and agricultural research applications.

Effect of cooling rate and cryoprotectant concentration on intracellular ice formation of small abalone (Haliotis diversicolor) eggs.

The intracellular ice formation (IIF) behavior of Haliotis diversicolor (small abalone) eggs is investigated in this study, in relation to controlling the cooling rate and the concentration of dimethyl sulfoxide (DMSO). The IIF phenomena are monitored under a self-developed thermoelectric cooling (TEC) cryomicroscope system which can achieve accurate temperature control without the use of liquid nitrogen. The accuracy of the isothermal and ramp control is within ±0.5 °C. The IIF results indicate that the IIF of small abalone eggs is well suppressed at cooling rates of 1.5, 3, 7 and 12 °C/min with 2.0, 2.5, 3.0 and 4.0M DMSO in sea water. As 2.0M DMSO in sea water is the minimum concentration that has sufficient IIF suppression, it is selected as the suspension solution for the cryopreservation of small abalone eggs in order to consider the solution's toxicity effect. Moreover, IIF characteristics of the cumulative probability of IIF temperature distribution are shown to be well fitted by the Weibull probabilistic distribution. According to our IIF results and the Weibull distribution parameters, we conclude that cooling at 1.5 °C/min from 20 to -50 °C with 2.0M DMSO in sea water is more feasible than other combinations of cooling rates and DMSO concentrations in our experiments. Applying this protocol and observing the subsequent osmotic activity, 48.8% of small abalone eggs are osmotically active after thawing. In addition, the higher the cooling rate, the less chance of osmotically active eggs. A separate fertility test experiment, with a cryopreservation protocol of 1.5 °C/min cooling rate and 2.0M DMSO in sea water, achieves a hatching rate of 23.7%. This study is the first to characterize the IIF behavior of small abalone eggs in regard to the cooling rate and the DMSO concentration. The Weibull probabilistic model fitting in this study is an approach that can be applied by other researchers for effective cryopreservation variability estimation and analysis.

A modified multi-channel EMG feature for upper limb motion pattern recognition.

The EMG signal is a well-known and useful biomedical signal. Much information related to muscles and human motions is included in EMG signals. Many approaches have proposed various methods that tried to recognize human motion via EMG signals. However, one of the critical problems of motion pattern recognition is that the performance of recognition is easily affected by the normalization procedure and may not work well on different days. In this paper, a modified feature of the multi-channel EMG signal is proposed and the normalization procedure is also simplified by using this modified feature. To recognize motion pattern, we applied the support vector machine (SVM) to build the motion pattern recognition model. In training and validation procedures, we used the 2-DoF exoskeleton robot arm system to do the designed pose, and the multi-channel EMG signals were obtained while the user resisted the robot. Experiment results indicate that the performance of applying the proposed feature (94.9%) is better than that of conventional features. Moreover, the performances of the recognition model, which applies the modified feature to recognize the motions on different days, are more stable than other conventional features.

A wearable walking monitoring system for gait analysis.

In this paper, both hardware and software design to develop a wearable walking monitoring system for gait analysis are presented. For hardware, the mechanism proposed is adaptive to different individuals to wear, and the portability of the design makes it easy to perform outdoor experiments. Four force sensors and two angle displacement sensors were used to measure plantar force distribution and the angles of hip and knee joints. For software design, a novel algorithm was developed to detect different gait phases and the four gait periods during the stance phase. Furthermore, the center of ground contact force was calculated based on the relationships of the force sensors. The results were compared with the VICON motion capture system and a force plate for validation. Experiments showed the behavior of the joint angles are similar to VICON system, and the average error in foot strike time is less than 90 ms.

An infrared range camera-based approach for three-dimensional locomotion tracking and pose reconstruction in a rodent.

We herein introduce an automated three-dimensional (3D) locomotion tracking and pose reconstruction system for rodents with superior robustness, rapidity, reliability, resolution, simplicity, and cost. An off-the-shelf composite infrared (IR) range camera was adopted to grab high-resolution depth images (640×480×2048 pixels at 20Hz) in our system for automated behavior analysis. For the inherent 3D structure of the depth images, we developed a compact algorithm to reconstruct the locomotion and body behavior with superior temporal and solid spatial resolution. Since the range camera operates in the IR spectrum, interference from the visible light spectrum did not affect the tracking performance. The accuracy of our system was 98.1±3.2%. We also validated the system, which yielded strong correlation with automated and manual tracking. Meanwhile, the system replicates a detailed dynamic rat model in virtual space, which demonstrates the movements of the extremities of the body and locomotion in detail on varied terrain.

Robust ellipse detection based on hierarchical image pyramid and Hough transform.

In this research we propose a fast and robust ellipse detection algorithm based on a multipass Hough transform and an image pyramid data structure. The algorithm starts with an exhaustive search on a low-resolution image in the image pyramid using elliptical Hough transform. Then the image resolution is iteratively increased while the candidate ellipses with higher resolution are updated at each step until the original image resolution is reached. After removing the detected ellipses, the Hough transform is repeatedly applied in multiple passes to search for remaining ellipses, and terminates when no more ellipses are found. This approach significantly reduces the false positive error of ellipse detection as compared with the conventional randomized Hough transform method. The analysis shows that the computing complexity of this algorithm is Θ(n(5/2)), and thus the computation time and memory requirement are significantly reduced. The developed algorithm was tested with images containing various numbers of ellipses. The effects of noise-to-signal ratio combined with various ellipse sizes on the detection accuracy were analyzed and discussed. Experimental results revealed that the algorithm is robust to noise. The average detection accuracies were all above 90% for images with less than seven ellipses, and slightly decreased to about 80% for images with more ellipses. The average false positive error was less than 2%.

A post-processing method for correction and enhancement of chemical shift images.

Chemical shift imaging (CSI) relies on a strong and homogeneous main field. Field homogeneity ensures adequate coherence between the precessions of individual spins within each voxel. Variation of field strength between different voxels causes geometric distortion and intensity variation in chemical shift images, resulting in errors when analyzing the spatial distribution of specific chemical compounds. A post-processing method, based on detection of the spectral peak of water and baseline subtraction with Lorentzian functions, was developed in this study to automatically correct spectra offsets caused by field inhomogeneity, thus enhancing the contrast of the chemical shift images. Because this method does not require prior field plot information, it offers advantages over existing correction methods. Furthermore, the method significantly reduces geometric distortion and enhances signals of chemical compounds even when the water suppression protocol was applied during the CSI data acquisition. The experimental results of the water and glucose phantoms showed a considerable reduction of artifacts in the spectroscopic images when this post-processing method was employed. The significance of this method was also demonstrated by an analysis of the spatial distributions of sugar and water contents in ripe and unripe bananas.