A WEARABLE BIOFEEDBACK SYSTEM SUPPORTING REAL-TIME PACED BREATHING TRAINING AND PHYSIOLOGICAL MONITORING

2013 ◽  
Vol 25 (02) ◽  
pp. 1350018 ◽  
Author(s):  
Zheng-Bo Zhang ◽  
Hao Wu ◽  
Jie-Wen Zheng ◽  
Wei-Dong Wang ◽  
Bu-Qing Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Breathing Pattern ◽  
Detection Algorithm ◽  
Biofeedback Training ◽  
Physiological Data ◽  
Time Signal ◽  
Physiological Monitoring ◽  
Breathing Rate ◽  
Beneficial Effects ◽  
Biofeedback System

Slow and regular breathing can generate beneficial effects on cardiovascular system and reduce stress. Breathing pacer is usually helpful for a user to learn to control breathing and restore an optimal breathing pattern. In this paper, a wearable physiological monitoring system supporting real-time breathing biofeedback is presented. An elastic T-shirt with two inductive bands integrated in the positions of rib cage (RC) and abdomen (AB) is used as a motherboard both for physiological monitoring and respiratory biofeedback. Physiological signals such as RC and AB respiration, electrocardiography (ECG), photoplethysmograph (PPG) and artery pulse wave (APW) can be sampled, stored and transmitted wirelessly. When this system is used in biofeedback applications, respiratory signals are processed in real-time by a peak-detection algorithm to recognize the concurrent breathing pattern. By comparing the actual breathing rate with the guiding breathing rate, an audio biofeedback is generated by playing music audios stored in the Micro-SD card through an MP3 decoder chip VS1053. With this design, multiple functions of physiological monitoring, real-time signal processing and audio biofeedback were integrated in one wearable system. Experiment showed that through audio biofeedback this system can guide the user to practice a slow and regular breathing effectively. Physiological data recorded from a Yoga practitioner during meditation demonstrated the capability of the system to acquire cardiopulmonary physiological data during slow breathing. This system is a useful tool both for breathing biofeedback training and its related scientific researches.

An automatic seismic signal detection algorithm based on the Walsh transform

1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin
Keyword(s):  
Signal Detection ◽  
Real Time ◽  
Seismic Signal ◽  
Detection Algorithm ◽  
Machine Language ◽  
Walsh Transform ◽  
Time Signal ◽  
Data Systems ◽  
Detection Capability ◽  
Short Period

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.

Real-Time Biofeedback to Target Risk of Anterior Cruciate Ligament Injury: A Technical Report for Injury Prevention and Rehabilitation

2015 ◽  
Vol 24 (2) ◽  
Author(s):  
Kevin R. Ford ◽  
Christopher A. DiCesare ◽  
Gregory D. Myer ◽  
Timothy E. Hewett
Keyword(s):  
Anterior Cruciate Ligament ◽  
Real Time ◽  
Anterior Cruciate Ligament Injury ◽  
Cruciate Ligament ◽  
Biofeedback Training ◽  
Ligament Injury ◽  
Physiological Data ◽  
Knee Abduction ◽  
Anterior Cruciate ◽  
Vertical Jumps

Context: Biofeedback training enables an athlete to alter biomechanical and physiological function by receiving biomechanical and physiological data concurrent with or immediately after a task. Objective: To compare the effects of 2 different modes of real-time biofeedback focused on reducing risk factors related to anterior cruciate ligament injury. Design: Randomized crossover study design. Setting: Biomechanics laboratory and sports medicine center. Participants: Female high school soccer players (age 14.8 ± 1.0 y, height 162.6 ± 6.8 cm, mass 55.9 ± 7.0 kg; n = 4). Intervention: A battery of kinetic- or kinematic-based real-time biofeedback during repetitive double-leg squats. Main Outcome Measures: Baseline and posttraining drop vertical jumps were collected to determine if either feedback method improved high injury risk landing mechanics. Results: Maximum knee abduction moment and angle during the landing was significantly decreased after kinetic-focused biofeedback (P = .04). The reduced knee abduction moment during the drop vertical jumps after kinematic-focused biofeedback was not different (P = .2). Maximum knee abduction angle was significantly decreased after kinetic biofeedback (P < .01) but only showed a trend toward reduction after kinematic biofeedback (P = .08). Conclusions: The innovative biofeedback employed in the current study reduced knee abduction load and posture from baseline to posttraining during a drop vertical jump.

Developing a multimodal biosensor for remote physiological monitoring

2021 ◽  
pp. bmjmilitary-2020-001629
Author(s):  
Michael Smith ◽  
R Withnall ◽  
J Blackadder-Coward ◽  
N Taylor
Keyword(s):  
Real Time ◽  
Point Of Care ◽  
Near Field ◽  
Research Centre ◽  
Physiological Data ◽  
Physiological Monitoring ◽  
Technical Refinement ◽  
Physiological Sensors ◽  
The Individual ◽  
The Uk

IntroductionSeveral UK military expeditions have successfully used physiological sensors to monitor participant’s physiological responses to challenging environmental conditions. This article describes the development and trial of a multimodal wearable biosensor that was used during the first all-female unassisted ski crossing of the Antarctic land mass. The project successfully transmitted remote real-time physiological data back to the UK. The ergonomic and technical lessons identified have informed recommendations for future wearable devices.MethodThe biosensor devices were designed to be continuously worn against the skin and capture: HR, ECG, body surface temperature, bioimpedance, perspiration pH, sodium, lactate and glucose. The data were transmitted from the devices to an android smartphone using near-field technology. A custom-built App running on an android smartphone managed the secure transmission of the data to a UK research centre, using a commercially available satellite transceiver.ResultsReal-time physiological data, captured by the multimodal device, was successfully transmitted back to a UK research control centre on 6 occasions. Postexpedition feedback from the participants has contributed to the ergonomic and technical refinement of the next generation of devices.ConclusionThe future success of wearable technologies lies in establishing clinical confidence in the quality of the measured data and the accurate interpretation of those data in the context of the individual, the environment and activity being undertaken. In the near future, wearable physiological monitoring could improve point-of-care diagnostic accuracy and inform critical medical and command decisions.

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

Real-time signal processing in field programmable gate array based digital gamma-ray spectrometer

2020 ◽  
Vol 91 (10) ◽  
pp. 104707
Author(s):  
Yinyu Liu ◽  
Hao Xiong ◽  
Chunhui Dong ◽  
Chaoyang Zhao ◽  
Quanfeng Zhou ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Gamma Ray ◽  
Time Signal ◽  
Gamma Ray Spectrometer ◽  
Field Programmable ◽  
Gate Array ◽  
Real Time Signal Processing

scholarly journals Real-time 2D–3D door detection and state classification on a low-power device

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
João Gaspar Ramôa ◽  
Vasco Lopes ◽  
Luís A. Alexandre ◽  
S. Mogo
Keyword(s):  
Low Power ◽  
Real Time ◽  
Object Classification ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Power Device ◽  
Indoor Environments ◽  
State Classification ◽  
Segmentation Algorithms ◽  
Indoor Spaces

AbstractIn this paper, we propose three methods for door state classification with the goal to improve robot navigation in indoor spaces. These methods were also developed to be used in other areas and applications since they are not limited to door detection as other related works are. Our methods work offline, in low-powered computers as the Jetson Nano, in real-time with the ability to differentiate between open, closed and semi-open doors. We use the 3D object classification, PointNet, real-time semantic segmentation algorithms such as, FastFCN, FC-HarDNet, SegNet and BiSeNet, the object detection algorithm, DetectNet and 2D object classification networks, AlexNet and GoogleNet. We built a 3D and RGB door dataset with images from several indoor environments using a 3D Realsense camera D435. This dataset is freely available online. All methods are analysed taking into account their accuracy and the speed of the algorithm in a low powered computer. We conclude that it is possible to have a door classification algorithm running in real-time on a low-power device.

Sign in / Sign up

Export Citation Format

Share Document