scholarly journals Real-Time Social Distancing for Tackling COVID-19 in Workplaces Using Wearable Inertial Sensor

2021 ◽  
Vol 11 (3) ◽  
pp. 58-64
Author(s):  
Ahmed M. M. Almassri ◽  
◽  
Natsuki Shirasawa ◽  
Hiroaki Wagatsuma
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Social Distancing

scholarly journals An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2869
Author(s):  
Jiaen Wu ◽  
Kiran Kuruvithadam ◽  
Alessandro Schaer ◽  
Richie Stoneham ◽  
George Chatzipirpiridis ◽  
...  
Keyword(s):  
Real Time ◽  
Inertial Sensor ◽  
Neurological Diseases ◽  
Principal Component ◽  
Sampling Frequency ◽  
Powerful Analytical Tool ◽  
Optical Motion ◽  
Analysis System ◽  
Optical Motion Capture ◽  
Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

scholarly journals Practicing in a pandemic

2020 ◽  
pp. 10.1212/CPJ.0000000000000882 ◽  
Author(s):  
Christopher G. Tarolli ◽  
Julia M. Biernot ◽  
Peter D. Creigh ◽  
Emile Moukheiber ◽  
Rachel Marie E. Salas ◽  
...  
Keyword(s):  
Real Time ◽  
Virus Disease ◽  
Social Distancing ◽  
The Past ◽  
Corona Virus ◽  
The World ◽  
Patient Preparation ◽  
Ongoing Care ◽  
Remote Care

Neurologists around the country and the world are rapidly transitioning from traditional in-person visits to remote neurologic care because of the corona virus disease 2019 pandemic. Given calls and mandates for social distancing, most clinics have shuttered or are only conducting urgent and emergent visits. As a result, many neurologists are turning to teleneurology with real-time remote video-based visits with patients, to provide ongoing care. Although telemedicine utilization and comfort has grown for many acute and ambulatory neurologic conditions in the past decade, remote visits and workflows remain foreign to many patients and neurologists. Here, we provide a practical framework for clinicians to orient themselves to the remote neurologic assessment, offering suggestions for clinician and patient preparation prior to the visit; recommendations to manage common challenges with remote neurologic care; modifications to the neurologic exam for remote performance, including subspecialty-specific considerations for a variety of neurologic conditions; and a discussion of the key limitations of remote visits. These recommendations are intended to serve as a guide for immediate implementation as neurologists transition to remote care. These will be relevant not only for practice today, but also for the likely sustained expansion of teleneurology following the pandemic.

scholarly journals Inertial-Robotic Motion Tracking in End-Effector-Based Rehabilitation Robots

2020 ◽  
Vol 7 ◽  
Author(s):  
Arne Passon ◽  
Thomas Schauer ◽  
Thomas Seel
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Rehabilitation Robotics ◽  
Measurement Information ◽  
Indoor Environments ◽  
Upper Arm ◽  
End Effector ◽  
Upper Limb Rehabilitation

End-effector-based robotic systems provide easy-to-set-up motion support in rehabilitation of stroke and spinal-cord-injured patients. However, measurement information is obtained only about the motion of the limb segments to which the systems are attached and not about the adjacent limb segments. We demonstrate in one particular experimental setup that this limitation can be overcome by augmenting an end-effector-based robot with a wearable inertial sensor. Most existing inertial motion tracking approaches rely on a homogeneous magnetic field and thus fail in indoor environments and near ferromagnetic materials and electronic devices. In contrast, we propose a magnetometer-free sensor fusion method. It uses a quaternion-based algorithm to track the heading of a limb segment in real time by combining the gyroscope and accelerometer readings with position measurements of one point along that segment. We apply this method to an upper-limb rehabilitation robotics use case in which the orientation and position of the forearm and elbow are known, and the orientation and position of the upper arm and shoulder are estimated by the proposed method using an inertial sensor worn on the upper arm. Experimental data from five healthy subjects who performed 282 proper executions of a typical rehabilitation motion and 163 executions with compensation motion are evaluated. Using a camera-based system as a ground truth, we demonstrate that the shoulder position and the elbow angle are tracked with median errors around 4 cm and 4°, respectively; and that undesirable compensatory shoulder movements, which were defined as shoulder displacements greater ±10 cm for more than 20% of a motion cycle, are detected and classified 100% correctly across all 445 performed motions. The results indicate that wearable inertial sensors and end-effector-based robots can be combined to provide means for effective rehabilitation therapy with likewise detailed and accurate motion tracking for performance assessment, real-time biofeedback and feedback control of robotic and neuroprosthetic motion support.

scholarly journals Social Distancing for Covid-19 Monitoring System

2021 ◽  
pp. 49-61
Author(s):  
Raj Kushwaha ◽  
Kismat Khatri ◽  
Yogesh Mahato
Keyword(s):  
Real Time ◽  
Safe Distance ◽  
Public Places ◽  
Cascade Algorithm ◽  
Facial Mask ◽  
Social Distancing ◽  
Video Footage ◽  
Camera Systems ◽  
Haar Cascade ◽  
Increasing Demand

The battle of corona-virus and mankind is possible to be tackled as long as we maintain the basic norm of social distancing and wearing masks amongst ourselves as it is through our droplets from the respiratory tract that the virus spreads. With the increasing demand for man-force and people requiring to go to their workplaces post lockdown, it is very necessary that we save each other from the virus. In this project, we will go through a detailed explanation of how we can use Python, AI and Deep Learning to monitor social distancing at public places and workplaces are keeping a safe distance from each other by analyzing real-time video streams from the camera and also detect facial mask monitoring using OpenCV and Python. To ensure if people are following social distancing protocols in public places and workplaces, we wanted to develop a tool that can monitor if people are keeping a safe distance from one another, wearing masks or not by processing real-time video footage from the camera. People at workplaces, factories, shops can integrate this tool into their security camera systems and can monitor whether people are keeping a safe distance from each other or not along with that we detect facial mask monitoring using Python with help of haar-cascade algorithm to see whether a person is wearing a mask or not. We are also planning to include thermal screening detection to measure the temperature of the subjects, a dashboard which will display a live report of corona cases around the world. We will also include an alert system that will send a notification to the authorities if the social distancing is not followed or if the temperature exceeds the threshold. The authorities can take suitable measures to isolate the subject and thus prevent the spread of Covid-19.

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