Generic Design and Advances in Wearable Sensor Technology

2018 ◽  
pp. 155-171
Author(s):  
Siddig Gomha ◽  
Khalid M. Ibrahim
Keyword(s):  
Sensor Technology ◽  
Wearable Sensor ◽  
Generic Design

Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review

Hand ◽  
2021 ◽  
pp. 155894472110146
Author(s):  
Francisco R. Avila ◽  
Rickey E. Carter ◽  
Christopher J. McLeod ◽  
Charles J. Bruce ◽  
Davide Giardi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Range Of Motion ◽  
Health Care Professionals ◽  
Inertial Sensors ◽  
Measurement Techniques ◽  
Infrared Camera ◽  
Wearable Devices ◽  
Sensor Technology ◽  
Accuracy Evaluation ◽  
Wearable Sensor

Background Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

scholarly journals Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1393
Author(s):  
Uduak Z. George ◽  
Kee S. Moon ◽  
Sung Q. Lee
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Spline Interpolation ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Lung Sound ◽  
Health Monitoring System ◽  
Electrocardiogram Ecg

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

scholarly journals Fabrication of Polymer Nanofiber-Conducting Polymer Fabric and Noncontact Motion Sensing Platform

2018 ◽  
Vol 915 ◽  
pp. 207-212
Author(s):  
Mustafa Umut Mutlu ◽  
Osman Akin ◽  
Mustafa M. Demir ◽  
Ümit Hakan Yildiz
Keyword(s):  
Electrostatic Interactions ◽  
Conductive Polymer ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Motion Sensing ◽  
Free Standing ◽  
Sensing Platform ◽  
Sensor Platform ◽  
Polymer Nanofiber ◽  
Device Miniaturization

Conductive polymer-electrospun polymer nanofiber network was combined to host iron oxide nanoparticles providing micrometer thick sensing interface. The sensor has fabricated as free-standing fabric exhibiting 10 to 100 KOhm base resistivity upon bias applied. The moving object has been sensed through the electrostatic interactions between fibers and object. The sensing range has been found to be 1-5 cm above the surface of fabric. By the controlled combination of conductive polymers electrospun polymer nanofibers effective device miniaturization has been provided without loss of performance. The noncontact motion sensor platform has unique flexibility and light weight holding a potential for wearable sensor technology.

scholarly journals Editorial: Wearable Sensor Technology for Monitoring Training Load and Health in the Athletic Population

2020 ◽  
Vol 10 ◽  
Author(s):  
Billy Sperlich ◽  
Kamiar Aminian ◽  
Peter Düking ◽  
Hans-Christer Holmberg
Keyword(s):  
Training Load ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Athletic Population ◽  
Monitoring Training

scholarly journals Objective fall risk detection in stroke survivors using wearable sensor technology: a feasibility study

2016 ◽  
Vol 23 (6) ◽  
pp. 393-399 ◽  
Author(s):  
Ruth E. Taylor-Piliae ◽  
M. Jane Mohler ◽  
Bijan Najafi ◽  
Bruce M. Coull
Keyword(s):  
Feasibility Study ◽  
Fall Risk ◽  
Sensor Technology ◽  
Stroke Survivors ◽  
Wearable Sensor ◽  
Risk Detection

Wearable Sensor Technology Efficacy in Peripheral Vascular Disease (wSTEP)

2018 ◽  
Vol 268 (6) ◽  
pp. 1113-1118 ◽  
Author(s):  
Pasha Normahani ◽  
Richard Kwasnicki ◽  
Colin Bicknell ◽  
Louise Allen ◽  
Mike P. Jenkins ◽  
...  
Keyword(s):  
Vascular Disease ◽  
Peripheral Vascular Disease ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Peripheral Vascular
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