Aerobics Exercise Posture Tracking and Recognition System Based on Wireless Smart Sensors

Wireless sensors are a new, high-end, and popular exploration technology. The positioning and tracking of the human body are two important research issues in sensors. Aerobics is a widely popular sports item that is well-loved by the general public and integrates gymnastics, dance, music, fitness, and entertainment. The application of smart sensors helps to improve the coordination and flexibility of movements. In order to deeply study whether wireless smart sensors can play a role in tracking and recognizing aerobics exercise postures, this article uses sensor design methods, motion analysis, and software and hardware equipment architecture methods to collect samples, analyze smart sensors, and streamline algorithms. And it creates a sensor model and system for posture tracking and recognition. When testing the best position for the placement of the sensor, first fix the sensor at a distance of 2.5 cm from the wrist section and at the middle of the lower arm, keep the upper arm still, and do the stretching and contraction movements of the lower arm. Repeat the exercise 5 times and measure the wrist. The movement curve of the juncture, the result shows that the measured juncture curve at a distance of 2.5 cm is basically the same as the actual winding curve, and the fixed position in the middle deviates more from the normal curve point, and its average error is within 0.9 cm, which is correct. To test the effectiveness of the algorithm again, a total of 8 volunteers’ data were collected during the experiment, and the duration was more than 25 seconds, and each normal posture tried to maintain stability and lasted for about 6 seconds. The statistical accuracy rate is 90.6%. It shows that the algorithm or the system designed in this paper has extremely high accuracy. It is basically realized that from the theory of wireless sensor network, a system model that can be used for posture tracking and recognition of aerobics is designed.

Big Data Issues

Various unimaginable opportunities and applications can be attained by the development of internet-connected automation. The network system with numerous wired or wireless smart sensors is called as IoT. It is showing various enhancement for past few years. Without proper security protection, various attacks and threats like cyberattacks threat causes serious disaster to IoT from the day it was introduced. Hence, IoT security system is improvised by various security and the management techniques. There are six sections in security management of IoT works. IoT security requirement is described intensively. The proposed layered of security management architecture is being defined and explained. Thus, this proposed architecture shows the security management system for IoT network tight security management for a network of the IoT which is elaborately explained with examples and about GDPR. In information security, intrusion recognizable proof is the showing of placing exercises that attempt to deal the protection, respectability, or availability of a benefit.

Automated wireless monitoring system for cable tension forces using deep learning

As demand for long-span bridges is increasing worldwide, effective maintenance has become a critical issue to maintain their structural integrity and prolong their lifetime. Given that a stay-cable is the principal load-carrying component in cable-stayed bridges, monitoring tension forces in stay-cables provides critical data regarding the structural condition of bridges. Indeed, various methodologies have been proposed to measure cable tension forces, including the magneto-elastic effect-based sensor technique, direct measurement using load cells, and indirect tension estimation based on cable vibration. In particular, vibration-based tension estimation has been widely applied to systems for tension monitoring and is known as a cost-effective approach. However, full automation under different cable tension forces has not been reported in the literature thus far. This study proposes an automated cable tension monitoring system using deep learning and wireless smart sensors that enables tension forces to be estimated. A fully automated peak-picking algorithm tailored to cable vibration is developed using a region-based convolution neural network to apply the vibration-based tension estimation method to automated cable tension monitoring. The developed system features embedded processing on wireless smart sensors, which includes data acquisition, power spectral density calculation, peak-picking, post-processing for peak-selection, and tension estimation. A series of laboratory and field tests are conducted on a cable to validate the performance of the proposed automated monitoring system.

Development and full-scale validation of high-fidelity data acquisition on a next-generation wireless smart sensor platform

Although wireless smart sensor platforms have been available over a decade, only a limited number of full-scale wireless smart sensor–based structural health monitoring implementations have been realized. Most wireless smart sensor platforms that are validated in full-scale implementations have now become obsolete and are no longer commercially available. While wireless sensing capabilities have grown, presenting significant opportunities, obstacles to wide application of wireless smart sensor for structural health monitoring exist both in terms of hardware and software. This article assesses the efficacy of the Xnode, a new wireless platform whose development has been driven by structural health monitoring requirements, as well as lessons learned from several full-scale wireless smart sensor deployments. The capabilities of the platform are evaluated in comparison with other commercial wireless smart sensors, in terms of hardware, software, and mechanical design. Extensive laboratory and field testing is employed to validate its performance on three aspects: fidelity of data acquisition, reliability of wireless communication, and efficiency of power management. Test results demonstrate the capabilities of the Xnode to support full-scale, high-fidelity data acquisition for civil infrastructure. In addition, the new sensor platform provides several significant benefits to extend the use of wireless smart sensors to a broader class of structural health monitoring applications, such as sudden event monitoring and real-time and control applications.

Sudden Event Monitoring of Civil Infrastructure Using Demand-Based Wireless Smart Sensors

Wireless smart sensors (WSS) have been proposed as an effective means to reduce the high cost of wired structural health monitoring systems. However, many damage scenarios for civil infrastructure involve sudden events, such as strong earthquakes, which can result in damage or even failure in a matter of seconds. Wireless monitoring systems typically employ duty cycling to reduce power consumption; hence, they will miss such events if they are in power-saving sleep mode when the events occur. This paper develops a demand-based WSS to meet the requirements of sudden event monitoring with minimal power budget and low response latency, without sacrificing high-fidelity measurements or risking a loss of critical information. In the proposed WSS, a programmable event-based switch is implemented utilizing a low-power trigger accelerometer; the switch is integrated in a high-fidelity sensor platform. Particularly, the approach can rapidly turn on the WSS upon the occurrence of a sudden event and seamlessly transition from low-power acceleration measurement to high-fidelity data acquisition. The capabilities of the proposed WSS are validated through laboratory and field experiments. The results show that the proposed approach is able to capture the occurrence of sudden events and provide high-fidelity data for structural condition assessment in an efficient manner.

Autonomous main-cable vibration monitoring using wireless smart sensors for large-scale three-pylon suspension bridges: A case study

Suspension bridges are supported by main cables that continue beyond the pillars to deck-level supports and must be anchored at each end of the bridge. The dynamic characteristics of the main cables are key indicators used to assess the structural health status of a bridge. In situ real-time health monitoring is an effective way to assess the dynamic characteristics. This paper presents a case study using vibration-based wireless smart sensors deployed on the main cables of a large-scale three-pylon suspension bridge to obtain its dynamic features. The methods of anti-aliasing filtering, statistical analysis and main cable tension force estimation were proposed and embedded into wireless smart sensors to provide autonomous data processing. According to the analysis of the vibration data from the main cables, the results demonstrate that the main cables have been in a stable state over time, and wireless smart sensors are promising for autonomous main-cable monitoring of large-scale three-pylon suspension bridges.