Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices.

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

Online spike sorting via deep contractive autoencoder.

Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets.

Automated soccer head impact exposure tracking using video and deep learning.

Head impacts are highly prevalent in sports and there is a pressing need to investigate the potential link between head impact exposure and brain injury risk. Wearable impact sensors and manual video analysis have been utilized to collect impact exposure data. However, wearable sensors suffer from high deployment cost and limited accuracy, while manual video analysis is a long and resource-intensive task. Here we develop and apply DeepImpact, a computer vision algorithm to automatically detect soccer headers using soccer game videos. Our data-driven pipeline uses two deep learning networks including an object detection algorithm and temporal shift module to extract visual and temporal features of video segments and classify the segments as header or nonheader events. The networks were trained and validated using a large-scale professional-level soccer video dataset, with labeled ground truth header events. The algorithm achieved 95.3% sensitivity and 96.0% precision in cross-validation, and 92.9% sensitivity and 21.1% precision in an independent test that included videos of five professional soccer games. Video segments identified as headers in the test data set correspond to 3.5 min of total film time, which can be reviewed through additional manual video verification to eliminate false positives. DeepImpact streamlines the process of manual video analysis and can help to collect large-scale soccer head impact exposure datasets for brain injury research. The fully video-based solution is a low-cost alternative for head impact exposure monitoring and may also be expanded to other sports in future work.

Towards User-Friendly Wearable Platforms for Monitoring Unconstrained Indoor and Outdoor Activities.

Developing wearable platforms for unconstrained monitoring of limb movements has been an active recent topic of research due to potential applications such as clinical and athletic performance evaluation. However, practicality of these platforms might be affected by the dynamic and complexity of movements as well as characteristics of the surrounding environment. This paper addresses such issues by proposing a novel method for obtaining kinematic information of joints using a custom-designed wearable platform. The proposed method uses data from two gyroscopes and an array of textile stretch sensors to accurately track three-dimensional movements, including extension, flexion, and rotation, of a joint. More specifically, gyroscopes provide angular velocity data of two sides of a joint, while their relative orientation is estimated by a machine learning algorithm. An Unscented Kalman Filter (UKF) algorithm is applied to directly fuse angular velocity/relative orientation data and estimate the kinematic orientation of the joint. Experimental evaluations were carried out using data from 10 volunteers performing a series of predefined as well as unconstrained random three-dimensional trunk movements. Results show that the proposed sensor setup and the UKF-based data fusion algorithm can accurately estimate the orientation of the trunk relative to pelvis with an average error of less than 1.72 degrees in predefined movements and a comparable accuracy of 3.00 degrees in random movements. Moreover, the proposed platform is easy to setup, does not restrict body motion, and is not affected by environmental disturbances. This study is a further step towards developing user-friendly wearable sensor systems than can be readily used in indoor and outdoor settings without requiring bulky equipment or a tedious calibration phase.

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks.

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles.

Wearable electronics are recognized as a vital tool for gathering in situ kinematic information of human body movements. In this paper, we describe the production of a core-sheath fiber strain sensor from readily available materials in a one-step dip-coating process, and demonstrate the development of a smart sleeveless shirt for measuring the kinematic angles of the trunk relative to the pelvis in complicated three-dimensional movements. The sensor's piezoresistive properties and characteristics were studied with respect to the type of core material used. Sensor performance was optimized by straining above the intended working region to increase the consistency and accuracy of the piezoresistive sensor. The accuracy of the sensor when tracking random movements was tested using a rigorous 4-h random wave pattern to mimic what would be required for satisfactory use in prototype devices. By processing the raw signal with a machine learning algorithm, we were able to track a strain of random wave patterns to a normalized root mean square error of 1.6%, highlighting the consistency and reproducible behavior of the relatively simple sensor. Then, we evaluated the performance of these sensors in a prototype motion capture shirt, in a study with 12 participants performing a set of eight different types of uniaxial and multiaxial movements. A machine learning random forest regressor model estimated the trunk flexion, lateral bending, and rotation angles with errors of 4.26°, 3.53°, and 3.44° respectively. These results demonstrate the feasibility of using smart textiles for capturing complicated movements and a solution for the real-time monitoring of daily activities.

Optimization of Emulsification-based Liquid Phase Microextraction of Chromium in Seawater of Chabahar Bay for its Speciation by High-Performance Liquid Chromatography.

After complexation of Cr(III) and Cr(VI) species with diethyldithiocarbamate (0.2 mmol/L), effective parameters of emulsification-based dispersive liquid microextraction procedure was optimized for its preconcentration in artificial seawater. Triton X-305 as the emulsifying disperser and mixture of the chloroform and carbon tetrachloride as the extraction solvents show a better behavior at sample pH of 6.5. The method was applied for extraction and UV detection (λ = 254 nm) of chromium species of the Chabahar Bay seawater prior to high-performance liquid chromatography (conditions: C18, methanol: acetic acid solution 2% v (85:15), flow rate of 0.8 mL min-1). Characteristics of the method such as enrichment factor (210 and 228), linear range (10-300 µg L-1), limit of detection (0.017 and 0.597 µg L-1) and repeatability, (N = 5, concentration of 100 µg L-1 %relative standard deviation = 2.6% and 0.45%) were evaluated for Cr(III) and Cr(VI), respectively.