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"Motion sensors"

Hanne Leirbekk Mjøsund, Eleanor Boyle, Per Kjaer, Rune Mygind Mieritz, Tue Skallgård, Peter Kent
BACKGROUND: Wireless, wearable, inertial motion sensor technology introduces new possibilities for monitoring spinal motion and pain in people during their daily activities of work, rest and play. There are many types of these wireless devices currently available but the precision in measurement and the magnitude of measurement error from such devices is often unknown. This study investigated the concurrent validity of one inertial motion sensor system (ViMove) for its ability to measure lumbar inclination motion, compared with the Vicon motion capture system...
March 21, 2017: BMC Musculoskeletal Disorders
Hwiyoung Kim, Hyunseok Lee, Jong In Park, Chang Heon Choi, So-Yeon Park, Hee Jung Kim, Young Suk Kim, Sung-Joon Ye
Mechanical quality assurance (QA) of medical linear accelerators consists of time consuming and human-error prone procedures. We developed a smartphone-application system for mechanical QA. The system consists of two smartphones; one attached to the gantry to obtain real-time information on mechanical parameters of medical linear accelerator, and the other to display the real-time information by bluetoothing the former. Motion sensors embedded in the smartphone were used to measure gantry and collimator rotations...
March 20, 2017: Physics in Medicine and Biology
Jun Rong Jeffrey Neo, Rana Sagha-Zadeh
BACKGROUND: The lack of user-friendly, accessible, and visible hand sanitizing stations (HSSs) in health care environments are significant factors affecting low hand hygiene compliance rates among caregivers. OBJECTIVE: To determine whether the simulated parameters of visibility and global traffic flow score for an HSS can influence the frequency of use of that HSS. METHODS: Space syntax was used to measure virtual simulation of spatial layouts of 3 units to provide quantitative visibility and global traffic flow scores for each HSS...
March 13, 2017: American Journal of Infection Control
Qian Cheng, Joshua Juen, Shashi Bellam, Nicholas Fulara, Deanna Close, Jonathan C Silverstein, Bruce Schatz
INTRODUCTION: Smartphones are ubiquitous, but it is unknown what physiological functions can be monitored at clinical quality. Pulmonary function is a standard measure of health status for cardiopulmonary patients. We have shown phone sensors can accurately measure walking patterns. Here we show that improved classification models can accurately predict pulmonary function, with sole inputs being motion sensors from carried phones. SUBJECTS AND METHODS: Twenty-five cardiopulmonary patients performed 6-minute walk tests in pulmonary rehabilitation at a regional hospital...
March 16, 2017: Telemedicine Journal and E-health: the Official Journal of the American Telemedicine Association
Erik Vanhoutte, Stefano Mafrica, Franck Ruffier, Reinoud J Bootsma, Julien Serres
For use in autonomous micro air vehicles, visual sensors must not only be small, lightweight and insensitive to light variations; on-board autopilots also require fast and accurate optical flow measurements over a wide range of speeds. Using an auto-adaptive bio-inspired Michaelis-Menten Auto-adaptive Pixel (M 2 APix) analog silicon retina, in this article, we present comparative tests of two optical flow calculation algorithms operating under lighting conditions from 6 × 10 - 7 to 1 . 6 × 10 - 2 W·cm - 2 (i...
March 11, 2017: Sensors
Sung-Ho Shin, Dae Hoon Park, Joo-Yun Jung, Min Hyung Lee, Junghyo Nah
We report a simple method to realize multifunctional flexible motion sensor using ferroelectric lithium-doped ZnO-PDMS. The ferroelectric layer enables piezoelectric dynamic sensing and provides additional motion information to more precisely discriminate different motions. The PEDOT:PSS-functionalized AgNWs, working as electrode layers for the piezoelectric sensing layer, resistively detect a change of both movement or temperature. Thus, through the optimal integration of both elements, the sensing limit, accuracy, and functionality can be further expanded...
March 22, 2017: ACS Applied Materials & Interfaces
Maram Maheedhar, Aman Gaurav, Vivek Jilla, Vijay N Tiwari, Rangavittal Narayanan
Comprehensive fitness training involves both cardiorespiratory and power components. Often power/muscle strength training is confused with cardiorespiratory endurance training. However, each of them target different physiological aspects of fitness. Although, wearable based fitness trackers designed towards cardiorespiratory endurance training are available in the market, a dedicated wearable based fitness application designed for power training/tracking is still not readily available to fitness enthusiasts...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ramin Fallahzadeh, Samaneh Aminikhanghahi, Ashley Nichole Gibson, Diane J Cook
Intervention strategies can help individuals with cognitive impairment to increase adherence to instructions, independence, and activity engagement and reduce errors on everyday instrumental activities of daily living (IADLs) and caregiver burden. However, to be effective, intervention prompts should be given at a time that does not interrupt other important user activities and is more convenient. In this paper, we propose an intelligent personalized intervention system for smartphones. In our approach, we use context and activity awareness to time prompts when they will most likely be viewed and used...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Sofia Assis, Pedro Costa, Maria Jose Rosas, Rui Vaz, Joao Paulo Silva Cunha
Intraoperative evaluation of the efficacy of Deep Brain Stimulation includes evaluation of the effect on rigidity. A subjective semi-quantitative scale is used, dependent on the examiner perception and experience. A system was proposed previously, aiming to tackle this subjectivity, using quantitative data and providing real-time feedback of the computed rigidity reduction, hence supporting the physician decision. This system comprised of a gyroscope-based motion sensor in a textile band, placed in the patients hand, which communicated its measurements to a laptop...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Hui Huang, Xian Li, Ye Sun
The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Naoteru Nakamura, Takashi Nagata, Masato Miyatake, Akira Yuuki, Hiroyuki Yomo, Takashi Kawabata, Shinsuke Hara
This paper focuses on oxygen consumption (VO2) estimation using 6-axis motion data (3-axis acceleration and 3-axis angular velocity) that are obtained from small motion sensors attached to people playing sports with different intensities. In order to achieve high estimation accuracy over a wide range of intensities of exercises, we apply neural network that is trained by experimental data consisting of the measured VO2 and motion sensing data of people with a wide range of intensities of exercises. We first investigate the gain brought by applying neural network by comparing its accuracy with an approach based on the linear regression model...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Takashi Nagata, Naoteru Nakamura, Masato Miyatake, Akira Yuuki, Hiroyuki Yomo, Takashi Kawabata, Shinsuke Hara
In this paper, we focus on oxygen consumption (VO2) estimation using 6-axis motion sensor (3-axis accelerometer and 3-axis gyroscope) for people playing sports with diverse intensities. The VO2 estimated with a small motion sensor can be used to calculate the energy expenditure, however, its accuracy depends on the intensities of various types of activities. In order to achieve high accuracy over a wide range of intensities, we employ an estimation framework that first classifies activities with a simple machine-learning based classification algorithm...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yuchao Ma, Hassan Ghasemzadeh
In this paper, we introduce an Asynchronous Multiview Learning (AML) approach to allow accurate transfer of activity classification models across asynchronous sensor views. Our study is motivated by the highly dynamic nature of health monitoring using wearable sensors. Such dynamics include changes in sensing platform (e.g., sensor upgrade) and platform settings (e.g., sampling frequency, on-body sensor location), which result in failure of the machine learning algorithms if they remain untrained in the new setting...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ihor Vasyltsov, Changgyu Bak
In this paper there is proposed an approach for seamless unlock security function for mobile application. The method combines the biomedical signals measured from human body and motion signals acquired from the devices. For this purpose a wearable device and a mobile device can be securely synchronized. It is shown that entropy extracted from biomedical ECG signal is comparable to the strength of the PIN-code security, the same time giving the easiness, flexibility, and seamlessness of the usage to the user...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yizhou Zhong, Yun Pan, Ling Zhang, Kwang-Ting Cheng
This paper presents a wearable signal acquisition system, which can measure physiological signs, i.e., electrocardiogram (ECG) and photoplethysmogram (PPG). The system is comprised of two parts: (1) an ECG sensor implemented in the master board which will be mounted on the chest and, (2) a combined PPG and motion sensor implemented in the slave board which will be worn around the neck area. The single-lead ECG, the single-channel PPG, and the 3-channel accelerometer signals are all sampled at 200Hz, and transmitted to an Android app through Bluetooth® low energy (BLE) in real time...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Simon Fong, Wei Song, Kyungeun Cho, Raymond Wong, Kelvin K L Wong
In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier...
February 27, 2017: Sensors
Yin Cheng, Ranran Wang, Haitao Zhai, Jing Sun
Electronic skin (E-skin) has been attracting great research interest and effort due to its potential applications in wearable health monitoring, smart prosthetics, robot skins and so on. To expand its applications, two key challenges lie in the realization of device stretchability, and independent sensing of pressure and multidirectional lateral strain. Here we made a combination of rational device structure and artfully engineered sensing materials to fulfill the mentioned demands. The as-prepared E-skin took a simple orthogonal configuration to enable both capacitive mode for pressure sensing and resistive mode for multidirectional strain sensing, independently...
March 2, 2017: Nanoscale
Jimi Eom, Rawat Jaisutti, Hyungseok Lee, Woobin Lee, Jae-Sang Heo, Jun-Young Lee, Sung Kyu Park, Yong-Hoon Kim
Emulation of diverse electronic devices on textile platform is considered as a promising approach for implementing wearable smart electronics. Of particular, the development of multifunctional polymeric fibers and their integration in common fabrics have been extensively researched for human friendly wearable platforms. Here we report a successful emulation of multifunctional body-motion sensors and user-interface (UI) devices in textile platform by using in situ polymerized poly(3,4-ethylenedioxythiophene) (PEDOT)-coated fibers...
March 22, 2017: ACS Applied Materials & Interfaces
Qian Zhang, Qijie Liang, Qingliang Liao, Fang Yi, Xin Zheng, Mingyuan Ma, Fangfang Gao, Yue Zhang
Triboelectric nanogenerators (TENGs) or TENG-based self-charging systems harvesting energy from ambient environment are promising power solution for electronics. The stable running remains a key consideration in view of potential complex application environment. In this work, a textile-based tailorable multifunctional TENG (T-TENG) is developed. The T-TENG is used as self-powered human body motion sensor, water energy harvester, and formed all textile-based flexible self-charging system by integrating with textile-based supercapacitors...
March 1, 2017: Advanced Materials
Matthew Longley, Ethan L Willis, Cindy X Tay, Hao Chen
We created an easy-to-use device for operant licking experiments and another device that records environmental variables. Both devices use the Raspberry Pi computer to obtain data from multiple input devices (e.g., radio frequency identification tag readers, touch and motion sensors, environmental sensors) and activate output devices (e.g., LED lights, syringe pumps) as needed. Data gathered from these devices are stored locally on the computer but can be automatically transferred to a remote server via a wireless network...
2017: PeerJ
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