Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

In this study, a sports parameter acquisition model based on the internet of things and wavelet analysis is studied to improve the accuracy and timeliness of human sports parameter acquisition. A motion parameter acquisition model including a sensing layer, transmission layer, and application layer is designed. The acceleration sensor and temperature sensor in the information acquisition node in the sensing layer are used to collect the motion parameter data, which are uploaded to the application layer by the network in the transmission layer. The received data are denoised by the wavelet analysis method through the data processing unit in this layer and then sent to the ZigBee coordinator for coordination. The results show that the model can achieve the effective acquisition of different sports parameters of different moving objects and analyze the actual movement of moving objects according to the acquisition results. In the acquisition process, the signal burr can be effectively removed, the signal noise can be reduced, the high signal-to-noise ratio signal can be output, and the accuracy of acquisition is improved. It has high timeliness, stable performance, and strong practical application, which can provide an effective guarantee for users to monitor sports parameter data in real time.

Quantifying Intra- and Interlimb Coordination in Persons With Hemiparesis Post-stroke

BackgroundIndividuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb coordination, yet methods to quantify both are limited. Objective: To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb coordination. MethodsTwenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0-1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM). ResultsA significant group x task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality. ConclusionsOur algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians.

Unsteady squeezing flow of a magnetized nano-lubricant between parallel disks with Robin boundary conditions

The aim of the present work is to examine the impact of magnetized nanoparticles (NPs) in enhancement of heat transport in a tribological system subjected to convective type heating (Robin) boundary conditions. The regime examined comprises the squeezing transition of a magnetic (smart) Newtonian nano-lubricant between two analogous disks under an axial magnetism. The lower disk is permeable whereas the upper disk is solid. The mechanisms of haphazard motion of NPs and thermophoresis are simulated. The non-dimensional problem is solved numerically using a finite difference method in the MATLAB bvp4c solver based on Lobotto quadrature, to scrutinize the significance of thermophoresis parameter, squeezing number, Hartmann number, Prandtl number, and Brownian motion parameter on velocity, temperature, nanoparticle concentration, Nusselt number, factor of friction, and Sherwood number distributions. The obtained results for the friction factor are validated against previously published results. It is found that friction factor at the disk increases with intensity in applied magnetic field. The haphazard (Brownian) motion of nanoparticles causes an enhancement in thermal field. Suction and injection are found to induce different effects on transport characteristics depending on the specification of equal or unequal Biot numbers at the disks. The main quantitative outcome is that, unequal Biot numbers produce significant cooling of the regime for both cases of disk suction or injection, indicating that Robin boundary conditions yield substantial deviation from conventional thermal boundary conditions. Higher thermophoretic parameter also elevates temperatures in the regime. The nanoparticles concentration at the disk is boosted with higher values of Brownian motion parameter. The response of temperature is similar in both suction and injection cases; however, this tendency is quite opposite for nanoparticle concentrations. In the core zone, the resistive magnetic body force dominates and this manifests in a significant reduction in velocity, that is damping. The heat build-up in squeeze films (which can lead to corrosion and degradation of surfaces) can be successfully removed with magnetic nanoparticles leading to prolonged serviceability of lubrication systems and the need for less maintenance.