Wireless Multi-sensor System Research Articles

Development of a hierarchical control strategy for a soft knee exoskeleton based on wearable multi-sensor system

Soft exoskeleton is a newly emerging approach for providing assistive forces to the movement of disabled individuals, which has aroused the interest of many researchers due to its promising prospects in rehabilitation training and motion assistance. In this article, a wearable wireless multi-sensor system and a hierarchical control architecture are developed for a soft knee exoskeleton. First, the major components of the soft knee exoskeleton are introduced, including the flexible Bowden-cable actuators, the soft wearable components, and the sensing modules and the control system. The wearable wireless multi-sensor system consists of an inertial measurement unit–based knee flexion–extension angle sensing module, a surface electromyogram–based joint torque estimation module, a miniature pulling force sensing module, and a central communication unit. And then, a three-level hierarchical control strategy—which fuses a high-level motion recognition and torque estimation algorithm, a mid-level knee trajectory generation strategy, and a low-level fuzzy impedance controller—is proposed to realize human motion assistance. Finally, experiments are carried out to verify the effectiveness of the proposed control strategy. The experimental setup is established with the soft knee exoskeleton, a treadmill, a wearable robotic rotary joint with an encoder, and an inelastic rope. The experimental result indicates that the root mean square errors of the joint angle estimation strategy and joint torque estimation strategy are less than 4.3° and 4.8 Nm, while the surface electromyogram–based assistance efficiency and torque-based assistance efficiency under different loading conditions are higher than 10.2% and 18.2%, respectively.

An Integrated Wireless Multi-Sensor System for Monitoring the Water Quality of Aquaculture.

Water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and salinity levels are the critical cultivation factors for freshwater aquaculture. This paper proposes a novel wireless multi-sensor system by integrating the temperature, pH, DO, and EC sensors with an ESP 32 Wi-Fi module for monitoring the water quality of freshwater aquaculture, which acquires the sensing data and salinity information directly derived from the EC level. The information of water temperature, pH, DO, EC, and salinity levels was displayed in the ThingSpeak IoT platform and was visualized in a user-friendly manner by ThingView APP. Firstly, these sensors were integrated with an ESP32 Wi-Fi platform. The observations of sensors and the estimated salinity from the EC level were then transmitted by a Wi-Fi network to an on-site Wi-Fi access point (AP). The acquired information was further transmitted to the ThingSpeak IoT and displayed in the form of a web-based monitoring system which can be directly visualized by online browsing or the ThingView APP. Through the complete processes of pre-calibration, in situ measurement, and post-calibration, the results illustrate that the proposed wireless multi-sensor IoT system has sufficient accuracy, reliable confidence, and a good tolerance for monitoring the water quality of freshwater aquaculture.

Virtual Therapeutics – Requirements to deliver value with virtual reality and biofeedback applications for alcohol addiction therapy

Abstract The application of virtual reality (VR) as a supportive tool in psychotherapy has gained great popularity in recent years. Especially for addiction therapy, a combination of virtual exposure and learning or training coping skills by using biofeedback has a high potential to improve conventional therapy. To add value, the new therapy system has to meet the needs of patients and practitioners likewise. Added values consistently named by experts included, but were not limited to, new possibilities of creating individual exposition or coping scenarios, enhanced psychoeducation, a shorter duration of treatment, telemedical aspects, the possibility of measuring and predicting craving and finally an improvement in abstinence. Besides literature research, we evaluated existing technical solutions in the field of virtual addiction treatment, surveyed experts and evolved a concept that led to a first prototype. The prototype consists of a wireless VR headset and a wireless multi-sensor system for measuring the physiological reaction to stimuli or the effectiveness of coping strategies by means of biofeedback. For further studies we developed both, a virtual exposure and a coping scenario and tested the hardware and software in a pilot study in order to elaborate factors that could negatively affect the therapy adherence, the effectiveness of exposition (immersion) and possible hurdles in practical use. Cybersickness and the lack of haptic feedback turned out to be the main limiting factors. Concepts for the next iteration of the therapy system will reflect these points for upcoming clinical studies. In our proof of concept, we demonstrated that virtual therapy can be implemented with a reasonable effort of time and costs. The combination of software and hardware, that supplements the traditional therapeutic approach, lays the foundation for upcoming clinical use and trials to prove the better outcome of VR enhanced addiction therapy.

Data fusion of wireless sensor network for prognosis and diagnosis of mechanical systems

With the promotion of the latest technologies and the new requirement of humanitarian, the wireless multi-sensor system is applied broadly. This paper studies the data fusion of the industrial wireless sensor networks (IWSNs), in order to acquire more thoughtful data for the prognosis and diagnosis of the monitored device. These authors propose a combination of back propagation neural network (BP NN) and Wavelet Packet algorithm for data fusion. This proposed algorithm is based on each cluster head, which is modelled with a three layers NN. A case study using the ball bearing test data, which is from the Bearing Data Center of the Case Western Reserve University, and to verify the effectiveness of the proposed algorithm. With MATLAB 2016b version, the raw data feature is extracted by the Wavelet Packet and the feature fusion is based on BP NN at sink node. The simulation results show that the proposed algorithm is effective in fault diagnosis of wind turbine.

A Cluster-Based Data Fusion Technique to Analyze Big Data in Wireless Multi-Sensor System

With the development of the latest technologies and changes in market demand, the wireless multi-sensor system is widely used. These multi-sensors are integrated in a way that produces an overwhelming amount of data, termed as big data. The multi-sensor system creates several challenges, which include getting actual information from big data with high accuracy, increasing processing efficiency, reducing power consumption, providing a reliable route toward destination using minimum bandwidth, and so on. Such shortcomings can be overcome by exploiting some novel techniques, such as clustering, data fusion, and coding schemes. Moreover, data fusion and clustering techniques are proven architectures that are used for efficient data processing; resultant data have less uncertainty, providing energy-aware routing protocols. Because of the limited resources of the multi-sensor system, it is a challenging task to reduce the energy consumption to survive a network for a longer period. Keeping challenges above in view, this paper presents a novel technique by using a hybrid algorithm for clustering and cluster member selection in the wireless multi-sensor system. After the selection of cluster heads and member nodes, the proposed data fusion technique is used for partitioning and processing the data. The proposed scheme efficiently reduces the blind broadcast messages but also decreases the signal overhead as the result of cluster formation. Afterward, the routing technique is provided based on the layered architecture. The proposed layered architecture efficiently minimizes the routing paths toward the base station. Comprehensive analysis is performed on the proposed scheme with state-of-the-art centralized clustering and distributed clustering techniques. From the results, it is shown that the proposed scheme outperforms competitive algorithms in terms of energy consumption, packet loss, and cluster formation.

An Internet-Enabled Wireless Multi-Sensor System for Continuous Monitoring of Landslide Processes

Monitoring and early warning systems, although being capable of continuously collecting field data related to landslide processes, are usually unable to autonomously detect and analyze signs of landslides in real time. This paper presents the design and experimental implementation of an autonomous landslide monitoring system. Besides reliably issuing early warnings in case of detected slope anomalies, the monitoring system is primarily designed to support human individuals in assessing the risk of landslide and to improve the understanding of the slope behavior, which may help to reduce economic losses and fatalities caused by landslides. Specifically, intelligent wireless sensor nodes are distributed in the observed slope to autonomously collect, analyze and communicate relevant environmental parameters in real time. Supporting remote analyses of the collected field data, a web application, which is installed on a computer connected to the on-site sensor nodes, enables an automated dissemination of slope parameters through the Internet. Last but not least, geospatial information stemming from external sources is integrated into the monitoring system to provide a comprehensive overview of landslide-related slope conditions.

A Passive Wireless Multi-Sensor SAW Technology Device and System Perspectives

This paper will discuss a SAW passive, wireless multi-sensor system under development by our group for the past several years. The device focus is on orthogonal frequency coded (OFC) SAW sensors, which use both frequency diversity and pulse position reflectors to encode the device ID and will be briefly contrasted to other embodiments. A synchronous correlator transceiver is used for the hardware and post processing and correlation techniques of the received signal to extract the sensor information will be presented. Critical device and system parameters addressed include encoding, operational range, SAW device parameters, post-processing, and antenna-SAW device integration. A fully developed 915 MHz OFC SAW multi-sensor system is used to show experimental results. The system is based on a software radio approach that provides great flexibility for future enhancements and diverse sensor applications. Several different sensor types using the OFC SAW platform are shown.