Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.

The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot-ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system.

Using high-resolution microscopy data to generate realistic structures for electromagnetic FDTD simulations from complex biological models.

Finite-difference time-domain (FDTD) electromagnetic simulations are a computational method that has seen much success in the study of biological optics; however, such simulations are often hindered by the difficulty of faithfully replicating complex biological microstructures in the simulation space. Recently, we designed simulations to calculate the trajectory of electromagnetic light waves through realistically reconstructed retinal photoreceptors and found that cone photoreceptor mitochondria play a substantial role in shaping incoming light. In addition to vision research and ophthalmology, such simulations are broadly applicable to studies of the interaction of electromagnetic radiation with biological tissue.

Increased summer temperature is associated with reduced calf mass of a circumpolar large mammal through direct thermoregulatory and indirect, food quality, pathways.

Climate change represents a growing ecological challenge. The (sub) arctic and boreal regions of the world experience the most rapid warming, presenting an excellent model system for studying how climate change affects mammals. Moose (Alces alces) are a particularly relevant model species with their circumpolar range.

Neurexin 3 is Essential for the Specific Wiring of a Color Pathway in the Mammalian Retina.

Precise wiring within sensory systems is critical for the accurate transmission of information. In the visual system, S-cone photoreceptors specialize in detecting short-wavelength light, crucial to color perception and environmental cue detection. S-cones form specific synapses with S-cone bipolar cells (SCBCs), a connection that is remarkably consistent across species.

A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving.

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location.

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review.

When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment.

Class-Aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset.

Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head.

Comparison of the Capacitance of a Cyclically Fatigued Stretch Sensor to a Non-Fatigued Stretch Sensor When Performing Static and Dynamic Foot-Ankle Motions.

Motion capture is the current gold standard for assessing movement of the human body, but laboratory settings do not always mimic the natural terrains and movements encountered by humans. To overcome such limitations, a smart sock that is equipped with stretch sensors is being developed to record movement data outside of the laboratory. For the smart sock stretch sensors to provide valuable feedback, the sensors should have durability of both materials and signal.

WCNN3D: Wavelet Convolutional Neural Network-Based 3D Object Detection for Autonomous Driving.

Three-dimensional object detection is crucial for autonomous driving to understand the driving environment. Since the pooling operation causes information loss in the standard CNN, we designed a wavelet-multiresolution-analysis-based 3D object detection network without a pooling operation. Additionally, instead of using a single filter like the standard convolution, we used the lower-frequency and higher-frequency coefficients as a filter.

Shared antibiotic resistance and virulence genes in Staphylococcus aureus from diverse animal hosts.

The emergence of methicillin-resistant Staphylococcus aureus (MRSA) poses an important threat in human and animal health. In this study, we ask whether resistance and virulence genes in S. aureus are homogeneously distributed or constrained by different animal hosts.

Mitochondria in cone photoreceptors act as microlenses to enhance photon delivery and confer directional sensitivity to light.

Mammalian photoreceptors aggregate numerous mitochondria, organelles chiefly for energy production, in the ellipsoid region immediately adjacent to the light-sensitive outer segment to support the high metabolic demands of phototransduction. However, these complex, lipid-rich organelles are also poised to affect light passage into the outer segment. Here, we show, via live imaging and simulations, that despite this risk of light scattering or absorption, these tightly packed mitochondria "focus" light for entry into the outer segment and that mitochondrial remodeling affects such light concentration.

Accessory Genome Dynamics of Local and Global Populations.

is a major bacterial colonizer and opportunistic pathogen in dogs. Methicillin-resistant (MRSP) continues to emerge as a significant challenge to maintaining canine health. We sought to determine the phylogenetic relationships of across five states in the New England region of the United States and place them in a global context.

Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications.

Wearable technologies are emerging as a useful tool with many different applications. While these devices are worn on the human body and can capture numerous data types, this literature review focuses specifically on wearable use for performance enhancement and risk assessment in industrial- and sports-related biomechanical applications. Wearable devices such as exoskeletons, inertial measurement units (IMUs), force sensors, and surface electromyography (EMG) were identified as key technologies that can be used to aid health and safety professionals, ergonomists, and human factors practitioners improve user performance and monitor risk.

Fatigue Testing of Wearable Sensing Technologies: Issues and Opportunities.

Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors.

Divergence in parturition timing and vegetation onset in a large herbivore-differences along a latitudinal gradient.

In northern environments, the period of access to high-quality forage is limited, exerting strong selective pressure to optimize the timing of parturition. We analysed timing and variation in moose () parturition dates of 555 females at 18 study sites across 12° of latitude (56-68° N, 1350 km) in Sweden. We found evidence for a spatial match of parturition timing to vegetation onset, but no evidence that moose adjust parturition to vegetation onset in a given year.

True S-cones are concentrated in the ventral mouse retina and wired for color detection in the upper visual field.

Color, an important visual cue for survival, is encoded by comparing signals from photoreceptors with different spectral sensitivities. The mouse retina expresses a short wavelength-sensitive and a middle/long wavelength-sensitive opsin (S- and M-opsin), forming opposing, overlapping gradients along the dorsal-ventral axis. Here, we analyzed the distribution of all cone types across the entire retina for two commonly used mouse strains.

Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics.

Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace.

Establishing a cost-effective hemodialysis program in the developing world.

The percentage of the population in low-middle-income countries (LMIC) with hemodialysis availability has gradually increased over the last 8 years. Note that only 3% of the treatments of these countries are done as peritoneal dialysis, which is the more cost-effective modality. The best current estimate of hemodialysis access for Africa is 25%, Southeast Asia 35%, and South America 65% [1].

Response of GNSS-R on Dynamic Vegetated Terrain Conditions.

Global navigation satellite system reflectometry (GNSS-R) has the potential to offer a cost-effective solution for global land observations. In this study, we aim to understand GNSS-R sensitivity to changing land geophysical parameters. For this objective, we performed simulations of a ground-based receiver using a recently developed coherent bistatic vegetation scattering model (SCoBi-Veg) to detect GNSS-R signatures under varying soil moisture (SM), vegetation water content (VWC), and surface roughness during a full corn growing season.

Closing the Wearable Gap-Part II: Sensor Orientation and Placement for Foot and Ankle Joint Kinematic Measurements.

The linearity of soft robotic sensors (SRS) was recently validated for movement angle assessment using a rigid body structure that accurately depicted critical movements of the foot-ankle complex. The purpose of this study was to continue the validation of SRS for joint angle movement capture on 10 participants (five male and five female) performing ankle movements in a non-weight bearing, high-seated, sitting position. The four basic ankle movements-plantar flexion (PF), dorsiflexion (DF), inversion (INV), and eversion (EVR)-were assessed individually in order to select good placement and orientation configurations (POCs) for four SRS positioned to capture each movement type.

Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving.

Since the state-of-the-art deep learning algorithms demand a large training dataset, which is often unavailable in some domains, the transfer of knowledge from one domain to another has been a trending technique in the computer vision field. However, this method may not be a straight-forward task considering several issues such as original network size or large differences between the source and target domain. In this paper, we perform transfer learning for semantic segmentation of off-road driving environments using a pre-trained segmentation network called DeconvNet.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection.

A significant challenge in object detection is accurate identification of an object's position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy.