Real-Time Tracking of Human Neck Postures and Movements

Improper neck postures and movements are the major causes of human neck-related musculoskeletal disorders. To monitor, quantify, analyze, and detect the movements, remote and non-invasive based methods are being developed for prevention and rehabilitation. The purpose of this research is to provide a digital platform for analyzing the impact of human neck movements on the neck musculoskeletal system. The secondary objective is to design a rehabilitation monitoring system that brings accountability in the treatment prescribed, which is shown in the use-case model. To record neck movements effectively, a Smart Neckband integrated with the Inertial Measurement Unit (IMU) was designed. The initial task was to find a suitable position to locate the sensors embedded in the Smart Neckband. IMU-based real-world kinematic data were captured from eight research subjects and were used to extract kinetic data from the OpenSim simulation platform. A Random Forest algorithm was trained using the kinetic data to predict the neck movements. The results obtained correlated with the novel idea proposed in this paper of using the hyoid muscles to accurately detect neck postures and movements. The innovative approach of integrating kinematic data and kinetic data for analyzing neck postures and movements has been successfully demonstrated through the efficient application in a rehabilitation use case with about 95% accuracy. This research study presents a robust digital platform for the integration of kinematic and kinetic data that has enabled the design of a context-aware neckband for the support in the treatment of neck musculoskeletal disorders.

Plasma channel dynamics in sub- and microsecond discharges in water

Simulation results of a fast electric discharge and a strong acoustic wave in the water is performed. A theoretical model of a high-current plasma channel is presented. The model accounts for the energy ratio between the input electric power and the plasma channel conductivity, and adiabatic expansion mechanism of this channel in water. It allows you to calculate the dynamics of the expansion of the channel and the generation of a radially diverging acoustic wave. The presented study makes it possible to estimate the probable parameters of the phenomenon: when electric energy is introduced into the channel, its expansion velocity reaches 1.9 km/s, electrons number density in the plasma is up to 2·1020 cm−3. In this case, a strong acoustic wave propagates with a sonic speed (~ 1500 m/s), and the pressure amplitude in the vicinity of the plasma channel can reach 200 MPa. The stability of the model in relation to variations in the initial task parameters has been analyzed. The calculated data for the acoustic wave are in good agreement with the measurements.

Movement generalization of variable initial task state based on Euclidean transformation dynamical movement primitives

Robots need the ability to tackle problems of movement generalization in variable task state and complex environment. Dynamical movement primitives can effectively endow robots with humanoid characteristics. However, when the initial state of tasks changes, the generalized trajectories by dynamical movement primitives cannot retain shape features of demonstration, resulting in the loss of imitation quality. In this article, a modified dynamical movement primitives based on Euclidean transformation is proposed to solve this problem. It transforms the initial task state to a virtual situation similar to the demonstration and then utilizes the dynamical movement primitive method to realize movement generalization. Finally, it reverses the movement back to the real situation. Besides, the information of obstacles is added to Euclidean transformation based dynamical movement primitives framework to endow robots with the ability of obstacle avoidance. The normalized root-mean-square error is proposed as the criterion to evaluate the imitation similarity. The feasibility of this method is verified through writing letters, wiping whiteboard in two-dimensional task, and stirring mixture in three-dimensional task. The results show that the similarity of movement imitation in the proposed method is higher than dynamical movement primitives when the initial state changes. Meanwhile, Euclidean transformation based dynamical movement primitives can still greatly retain shape feature of demonstration while avoiding obstacles in an unstructured environment.

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.

Comparative Analysis for Content Defined Chunking Algorithms in Data Deduplication

Data deduplication works on eliminating redundant data and reducing storage consumption. Nowadays more data generated and it was stored in the cloud repeatedly, due to this large volume of storage will be consumed. Data deduplication tries to reduce data volumes disk space and network bandwidth can be to reduce costs and energy consumption for running storage systems. In the data deduplication method, data broken into small size of chunk or block. Hash ID will be calculated for all the blocks then it’s compared with existing blocks for duplication. Blocks may be fixed or variable size, compared with a fixed size of block variable size chunking gives a better result. So the chunking process is the initial task of deduplication to get an optimal result. In this paper, we discussed various content defined chunking algorithms and their performance based on chunking properties like chunking speed, processing time, and throughput.

Examining the Effect of Interruptions at Different Breakpoints and Frequencies Within a Task

Objective The purpose was to explore how event segmentation theory (EST) can be used to determine optimal moments for an interruption relying on hierarchical task analysis (HTA) to identify coarse and fine event boundaries. Background Research on the effects of interruptions shows that they can be either disruptive or beneficial, depending on which aspects of an interruption are manipulated. Two important aspects that contribute to these conflicting results concern when and how often interruptions occur. Method Undergraduates completed a trip planning task divided into three subtasks. The within-subjects factor was interruption timing with three levels: none, coarse breakpoints, and fine breakpoints. The between-subjects factor was interruption frequency with two levels: one and three. The dependent measures included resumption lag, number of errors, mental workload, and frustration. Results Participants took longer to resume the primary task and reported higher mental workload when interruptions occurred at fine breakpoints. The effect of interruptions at coarse breakpoints was similar to completing the task without interruption. Interruption frequency had no effect on performance; however, participants spent significantly longer attending to interruptions in the initial task, and within a task, the first and second interruptions were attended to significantly longer than the third interruption. Conclusion The disruptiveness of an interruption is tied to the point within the task hierarchy where it occurs. Application The performance cost associated with interruptions must be considered within the task structure. Interruptions occurring at coarse breakpoints may not be disruptive or have a negative effect on mental workload.

Do Open or Closed Postures Boost Creative performance? The Effects of Postural Feedback on Divergent and Convergent Thinking

Can an individual’s body posture (expansive or contractive) affect their creative thinking (divergent or convergent)? Based on embodied cognition and the debate about the impact of nonverbal physical postures expressing power on psychological and behavioral outcomes, five experiments were conducted. We tested the prediction that expansive postures would have a positive effect on creativity tasks that have no right or wrong answer or optimal solution (divergent thinking), whereas contractive postures would have a positive effect on tasks with a right answer or an optimal solution (convergent thinking). As predicted, results revealed a positive effect of expansive postures on performance of creativity tasks requiring divergent thinking, such as producing original ideas (Study 1) or objects, either by combining shapes to create an original toy (Study 2) or by combining fragments to produce an original drawing (Study 3). Conversely, a positive effect of contractive postures was found on performance of insight tasks requiring convergent thinking, in which participants had to associate elements to discover a unifying and correct solution (Study 4) or overcome initial task constraints to find an optimal solution to a problem (Study 5). These findings open up new avenues for research in embodied creativity.

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

Great expectations: minor differences in initial instructions have a major impact on visual search in the absence of feedback

Professions such as radiology and aviation security screening that rely on visual search—the act of looking for targets among distractors—often cannot provide operators immediate feedback, which can create situations where performance may be largely driven by the searchers’ own expectations. For example, if searchers do not expect relatively hard-to-spot targets to be present in a given search, they may find easy-to-spot targets but systematically quit searching before finding more difficult ones. Without feedback, searchers can create self-fulfilling prophecies where they incorrectly reinforce initial biases (e.g., first assuming and then, perhaps wrongly, concluding hard-to-spot targets are rare). In the current study, two groups of searchers completed an identical visual search task but with just a single difference in their initial task instructions before the experiment started; those in the “high-expectation” condition were told that each trial could have one or two targets present (i.e., correctly implying no target-absent trials) and those in the “low-expectation” condition were told that each trial would have up to two targets (i.e., incorrectly implying there could be target-absent trials). Compared to the high-expectation group, the low-expectation group had a lower hit rate, lower false alarm rate and quit trials more quickly, consistent with a lower quitting threshold (i.e., performing less exhaustive searches) and a potentially higher target-present decision criterion. The expectation effect was present from the start and remained across the experiment—despite exposure to the same true distribution of targets, the groups’ performances remained divergent, primarily driven by the different subjective experiences caused by each groups’ self-fulfilling prophecies. The effects were limited to the single-targets trials, which provides insights into the mechanisms affected by the initial expectations set by the instructions. In sum, initial expectations can have dramatic influences—searchers who do not expect to find a target, are less likely to find a target as they are more likely to quit searching earlier.

Fuzzy remodeling and synchronization of nonlinear systems

Synchronization is one of the crucial control problems in coordinated behavior systems. A wide range of applications necessitates the development and improvement of approaches to solving the linearization problem for non-linear systems with complex behavior. Hence the paper offers an approach to solving the problem of chaotic system coordinate synchronization based on the use of fuzzy remodeling and superstability conditions. It is shown that transition from general (non-linear) synchronization problem statement to fuzzy description with fuzzy Takagi-Sugeno models allows transforming the initial task to a well understood problem of fuzzy T-S system stabilization that describes the dynamics synchronization error. As a candidate solution for the problem we offer a way of implementing superstability conditions that reduce the task of finding fuzzy regulator to solving a linear programming task. It is shown that implementation of superstability conditions not only presents a simple way of solving the problem, but also has practical value. Superstability provides the monotonesness of the transient process of synchronization without sharp spikes of solution norm. The efficiency of the offered approach is demonstrated by the example of synchronization of two hyperchaotic systems. To prove the efficiency of superstability conditions the solution of the robust synchronization problem with parametric uncertainty in system matrices is given. The presented results can be applied for synchronization of various nonlinear systems demonstrating chaotic dynamics.