scholarly journals Data Acquisition Glove for Hand Movement Impairment Rehabilitation

2016 ◽  
Vol 12 (04) ◽  
pp. 52 ◽  
Author(s):  
Rafael Tavares ◽  
Paulo Abreu ◽  
Manuel Rodrigues Quintas
Keyword(s):  
Data Acquisition ◽  
Hand Movement ◽  
Wearable Device ◽  
Sensor Data ◽  
Motion Processing ◽  
Processing Unit ◽  
Hand Rehabilitation ◽  
Peripheral Device ◽  
Serial Peripheral Interface ◽  
Electronic Hardware

The present paper describes a data acquisition wearable device for hand rehabilitation. The main goal of this glove is to be used by patients with hand movement impairment. It has position sensors to measure the bending of synovial joints and sensors to measure the fingertip contact pressure. There is a coin motor and a LED placed on each finger to produce a vibratory and visual stimulus. The glove also tracks the hand rotation and translation using a MPU (Motion Processing Unit) that contains an accelerometer and a gyroscope. A graphical application for an HMI module was developed in order to create rehabilitation game like exercises where sensor data can be logged for further analysis by a therapist. The wearable device electronic hardware comprises a Glove module and an HMI module that communicate through SPI protocol (Serial Peripheral Interface). The wearable device supports USB connection to send data to a computer or to be used as a peripheral device in virtual or augmented reality applications.

scholarly journals Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6851
Author(s):  
Vincent Roberge ◽  
Mohammed Tarbouchi
Keyword(s):  
Data Acquisition ◽  
Unmanned Aerial Vehicles ◽  
Satellite Communication ◽  
Graphics Processing Unit ◽  
Parallel Implementation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Processing Unit ◽  
Aerial Vehicles ◽  
Sensor Data Acquisition

This paper proposes a framework for the wireless sensor data acquisition using a team of Unmanned Aerial Vehicles (UAVs). Scattered over a terrain, the sensors detect information about their surroundings and can transmit this information wirelessly over a short range. With no access to a terrestrial or satellite communication network to relay the information to, UAVs are used to visit the sensors and collect the data. The proposed framework uses an iterative k‑means algorithm to group the sensors into clusters and to identify Download Points (DPs) where the UAVs hover to download the data. A Single-Source–Shortest-Path algorithm (SSSP) is used to compute optimal paths between every pair of DPs with a constraint to reduce the number of turns. A genetic algorithm supplemented with a 2-opt local search heuristic is used to solve the multi-travelling salesperson problem and to find optimized tours for each UAVs. Finally, a collision avoidance strategy is implemented to guarantee collision-free trajectories. Concerned with the overall runtime of the framework, the SSSP algorithm is implemented in parallel on a graphics processing unit. The proposed framework is tested in simulation using three UAVs and realistic 3D maps with up to 100 sensors and runs in just 20.7 s, a 33.3× speed-up compared to a sequential execution on CPU. The results show that the proposed method is efficient at calculating optimized trajectories for the UAVs for data acquisition from wireless sensors. The results also show the significant advantage of the parallel implementation on GPU.

Sensor Based Human Movement Controlled Hydraulic and Electrical Robotic Arm

2016 ◽  
Vol 860 ◽  
pp. 1-6 ◽  
Author(s):  
Md Shad Rahman ◽  
Rasel A. Sultan ◽  
N.M. Hasan
Keyword(s):  
Data Acquisition ◽  
Hand Movement ◽  
Human Movement ◽  
Sensor Data ◽  
Robotic Arm ◽  
Human Hand ◽  
Robotic Hand ◽  
Precise Control ◽  
Data Application ◽  
Environmental Work

This system is designed for advance Robotic control. It based on sensor data acquisition and software data processing. With those systems controlling a robotic hand by hydraulic and electric means. It is separated by two different sections. First, data acquisition section with differential sensor data (Gyro sensor, Flex sensor, Pressure sensor). Second, software processed data application system consisting of robotic hand. Specialty of this system is it gives precise control of robotic arm following human hand movement. It also gives touch and pressure feelings in robotic hand. A lot of work can be done easily with the help of it. Like this system gives remote bomb disposal, hazardous environmental work remotely, remote operation, remote medical help and so on.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

scholarly journals Design of Early Warning System Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 66
Author(s):  
Gan Bo ◽  
Jin Shan
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Monitoring System ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Sensor Data ◽  
Landslide Monitoring ◽  
Remote Transmission ◽  
Sensor Data Acquisition

In order to solve the shortcomings of the landslide monitoring technology method, a set of landslides monitoring and early warning system is designed. It can achieve real-time sensor data acquisition, remote transmission and query display. In addition, aiming at the harsh environment of landslide monitoring and the performance requirements of the monitoring system, an improved minimum hop routing protocol is proposed. It can reduce network energy consumption, enhance network robustness, and improve node layout and networking flexibility. In order to realize the remote transmission of data, GPRS wireless communication is used to transmit monitoring data. Combined with remote monitoring center, real-time data display, query, preservation and landslide warning and prediction are realized. The results show that the sensor data acquisition system is accurate, the system is stable, and the node network is flexible. Therefore, the monitoring system has a good use value.

scholarly journals Implementation of Oxygen Saturation Sensor Data Acquisition Based on Bluetooth Low Energy Protocol

2021 ◽  
Author(s):  
Muhammad Fahmi Ali Fikri ◽  
Dany Primanita Kartikasari ◽  
Adhitya Bhawiyuga
Keyword(s):  
Data Acquisition ◽  
Data Center ◽  
Low Energy ◽  
Sensor Data ◽  
Data Delivery ◽  
Bluetooth Low Energy ◽  
Data Redundancy ◽  
Iot Devices ◽  
Store And Forward ◽  
Sensor Data Acquisition

Sensor data acquisition is used to obtain sensor data from IoT devices that already provide the required sensor data. To acquire sensor data, we can use Bluetooth Low Energy (BLE) protocol. This data acquisition aims to process further data which will later be sent to the server. Bluetooth Low Energy (BLE) has an architecture consisting of sensors, gateways, and data centers, but with this architecture, there are several weaknesses, namely the failure when sending data to the data center due to not being connected to internet network and data redundancy at the time of data delivery is done. The proposed solution to solve this problem is to create a system that can acquire sensor data using the Bluetooth Low Energy (BLE) protocol with use a store and forward mechanism and checking data redundancy. The proposed system will be implemented using sensors from IoT devices, the gateway used is Android devices, and using the Bluetooth Low Energy protocol to acquire data from sensors. Then the data will be sent to the cloud or server. The results of the test give the results of the system being successfully implemented and IoT devices can be connected to the gateway with a maximum distance of 10 meters. Then when the system stores, for every minute there is an increase in data of 4 kb. Then there is no data redundancy in the system.

scholarly journals MULTI-SENSOR DATA ACQUISITION AND INTEGRATION PROCESSES FOR THE STUDY AND DOCUMENTATION OF THE CHURCH OF SANTA MARIA DEGLI ANGELI IN PIZZOFALCONE IN NAPLES

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 571-578
Author(s):  
M. Pulcrano ◽  
S. Scandurra ◽  
E. Fragalà ◽  
D. Palomba ◽  
A. di Luggo
Keyword(s):  
Comparative Analysis ◽  
Data Acquisition ◽  
Critical Analysis ◽  
3D Models ◽  
Sensor Data ◽  
Complete Representation ◽  
Passive Sensors ◽  
Santa Maria ◽  
The Church ◽  
3D Digital Models

Abstract. The paper presents the results of a research carried out on the Church of Santa Maria degli Angeli in Pizzofalcone in Naples, in which multi-sensor surveys have been performed in order to assess the architectonical, geometrical and colorimetric characteristics of the majestic basilica. The use of integrated technologies made it possible to realize 3D digital models that allowed the complete representation of the building, integrating data and filling the gaps of the different previous surveys. The performances of the various reality-based technologies employed have been subjected to critical analysis in order to maximize their potential, optimize survey and data elaboration phases, and obtain the expected results. These latter have been defined through the derived digital re-elaborations and representations. Hence, the objective of the research is to carry out a comparative analysis on the 3D models generated through the different active and passive sensors employed in order to proceed with their integration and achieve an accurate, original and updated methodology of building survey.

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