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Home sensor

Omid Bazgir, Seyed Amir Hassan Habibi, Lorenzo Palma, Paola Pierleoni, Saba Nafees
Background: Tremor is one of the most common symptoms of Parkinson's disease (PD), which is widely being used in the diagnosis procedure. Accurate estimation of PD tremor based on Unified PD Rating Scale (UPDRS) provides aid for physicians in prescription and home monitoring. This article presents a robust design of a classification system to estimate PD patient's hand tremors and the results of the proposed system as compared to the UPDRS. Methods: A smartphone accelerometer sensor is used for accurate and noninvasive data acquisition...
April 2018: Journal of Medical Signals and Sensors
Rong Zhang, Yajun Gu, Zhongrong Wang, Yueguo Li, Qingjie Fan, Yunfang Jia
Enlightened by the emerging cell-ion detection based on ion-selective-electrode (ISE), an aptamer capturing and ISE transducing (AC&IT) strategy is proposed on the porous graphene oxide (PGO) decorated ISE (PGO-ISE), its performances in both cell and ion detections are examined by use of AS1411 targeted A549 cell detection and iodide-ISE as proof-of-concept. Firstly, GO flakes, exfoliated from graphite by modified Hummers method, are cross-linked by thiourea mediated hydrothermal process, to 3-dimension networked PGO which is identified by scanning-electron-microscope, UV-visible absorbance and X-ray photoelectron spectroscopy; its enhancing effect for cell capturing is evaluated by microscopy...
June 15, 2018: Biosensors & Bioelectronics
Giovanni D'Addio, Luigi Iuppariello, Paolo Bifulco, Bernardo Lanzillo, Nicola Pappone, Mario Cesarelli
OBJECTIVES: Smart fabrics and interactive textiles are a relatively new area of research, with many potential applications in the field of biomedical engineering. The ability of smart textiles to interact with the body provides a novel means to sense the wearer's physiology and respond to the needs of the wearer. Physiological signals, such as heart rate, breathing rates, and activity levels, are useful indicators of health status. These signals can be measured by means of textile-based sensors integrated into smart clothing which has the ability to keep a digital record of the patient's physiological responses since his or her last clinical visit, allowing doctors to make a more accurate diagnosis...
December 2017: Giornale Italiano di Medicina del Lavoro Ed Ergonomia
Niccolò Mora, Guido Matrella, Paolo Ciampolini
Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data...
June 15, 2018: Sensors
Ali Mirzaei, Sang Sub Kim, Hyoun Woo Kim
Gas sensors play an undeniable role in most fields of technology in the modern world; they are broadly used for public safety, pollution monitoring, quality control, breath analysis, smart homes and automobiles, and so on. Due to their low cost, high sensitivity, compact size, online detection, ease of use, portability, and low power consumption, metal oxide (MO) gas sensors have exceptional potential for detection of more than 150 gases. This paper reviews the current state-of-the-art H2 S conductometric MO gas sensors...
June 6, 2018: Journal of Hazardous Materials
Andrew M Simons, Theresa Beltramo, Garrick Blalock, David I Levine
This data in brief article includes estimated time cooking based on temperature sensor data taken every 30 min from three stone fires and introduced fuel-efficient Envirofit stoves in approximately 168 households in rural Uganda. These households were part of an impact evaluation study spanning about six months to understand the effects of fuel-efficient cookstoves on fuel use and pollution. Daily particulate matter (pollution) and fuelwood use data are also included. This data in brief file only includes the weeks prior to, during, and after an in-person measurement team visited each home...
June 2018: Data in Brief
Mouhannad Ali, Fadi Al Machot, Ahmad Haj Mosa, Midhat Jdeed, Elyan Al Machot, Kyandoghere Kyamakya
Machine learning approaches for human emotion recognition have recently demonstrated high performance. However, only/mostly for subject-dependent approaches, in a variety of applications like advanced driver assisted systems, smart homes and medical environments. Therefore, now the focus is shifted more towards subject-independent approaches, which are more universal and where the emotion recognition system is trained using a specific group of subjects and then tested on totally new persons and thereby possibly while using other sensors of same physiological signals in order to recognize their emotions...
June 11, 2018: Sensors
Paolo Barsocchi, Antonello Calabrò, Erina Ferro, Claudio Gennaro, Eda Marchetti, Claudio Vairo
Smart Home has gained widespread attention due to its flexible integration into everyday life. Pervasive sensing technologies are used to recognize and track the activities that people perform during the day, and to allow communication and cooperation of physical objects. Usually, the available infrastructures and applications leveraging these smart environments have a critical impact on the overall cost of the Smart Home construction, require to be preferably installed during the home construction and are still not user-centric...
June 8, 2018: Sensors
Christopher J Todd, Patrick P Hubner, Philipp Hubner, Michael C Schubert, Americo A Migliaccio
The vestibulo-ocular reflex (VOR) is the primary mechanism for stabilizing vision during rapid head movements. We have developed a training technique that typically increases the VOR response a minimum of 15% after 15 mins of training. This technique relies on subjects tracking a visual target that moves as a function of head motion, but at a different speed, so that the VOR is challenged to increase in order to stabilize the retinal image of the target. We have developed a portable device, StableEyes, which implements this technique so that unassisted training can be performed at home by patients with VOR hypofunction...
June 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Julian Ramírez, Daniel Rodriquez, Fang Qiao, Julian Warchall, Jasmine Rye, Eden Aklile, Andrew S-C Chiang, Brandon C Marin, Patrick P Mercier, C K Cheng, Katherine A Hutcheson, Eileen H Shinn, Darren J Lipomi
There is a need to monitor patients with cancer of the head and neck postradiation therapy, as diminished swallowing activity can result in disuse atrophy and fibrosis of the swallowing muscles. This paper describes a flexible strain sensor comprising palladium nanoislands on single-layer graphene. These piezoresistive sensors were tested on 14 disease-free head and neck cancer patients with various levels of swallowing function: from nondysphagic to severely dysphagic. The patch-like devices detected differences in (1) the consistencies of food boluses when swallowed and (2) dysphagic and nondysphagic swallows...
June 8, 2018: ACS Nano
Sumiyakhand Dagdanpurev, Guanghao Sun, Toshikazu Shinba, Mai Kobayashi, Nobutoshi Kariya, Lodoiravsal Choimaa, Suvdaa Batsuuri, Seokjin Kim, Satoshi Suzuki, Takemi Matsui
Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis...
2018: Frontiers in Bioengineering and Biotechnology
Stavros I Dimitriadis, Avraam D Marimpis
A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( N = 5) flickering BCI system...
2018: Frontiers in Neuroinformatics
Maria Lindén, Mats Björkman
The demography is changing towards older people, and the challenge to provide an appropriate care is well known. Sensor systems, combined with IT solutions are recognized as one of the major tools to handle this situation. Embedded Sensor Systems for Health (ESS-H) is a research profile at Mälardalen University in Sweden, focusing on embedded sensor systems for health technology applications. The research addresses several important issues: to provide sensor systems for health monitoring at home, to provide sensor systems for health monitoring at work, to provide safe and secure infrastructure and software testing methods for physiological data management...
2018: Studies in Health Technology and Informatics
Roger Gassert, Volker Dietz
The past decades have seen rapid and vast developments of robots for the rehabilitation of sensorimotor deficits after damage to the central nervous system (CNS). Many of these innovations were technology-driven, limiting their clinical application and impact. Yet, rehabilitation robots should be designed on the basis of neurophysiological insights underlying normal and impaired sensorimotor functions, which requires interdisciplinary collaboration and background knowledge.Recovery of sensorimotor function after CNS damage is based on the exploitation of neuroplasticity, with a focus on the rehabilitation of movements needed for self-independence...
June 5, 2018: Journal of Neuroengineering and Rehabilitation
Claudia Gonzalez Viejo, Sigfredo Fuentes, Damir D Torrico, Frank R Dunshea
Traditional methods to assess heart rate (HR) and blood pressure (BP) are intrusive and can affect results in sensory analysis of food as participants are aware of the sensors. This paper aims to validate a non-contact method to measure HR using the photoplethysmography (PPG) technique and to develop models to predict the real HR and BP based on raw video analysis (RVA) with an example application in chocolate consumption using machine learning (ML). The RVA used a computer vision algorithm based on luminosity changes on the different RGB color channels using three face-regions (forehead and both cheeks)...
June 3, 2018: Sensors
(no author information available yet)
No abstract text is available yet for this article.
2018: Journal of Alzheimer's Disease: JAD
Ju Wang, Jing Wang, Hongyu Miao, Michael Marschollek, Klaus-Hendrik Wolf, Kerry A Lynch, Yang Gong
Seniors expect to age in place, which means living in their own homes as long as possible with familiar facilities and environments. Due to the capability of continuous and unobtrusive monitoring, the sensor-enhanced in-ho monitoring is regarded as a promising solution to support aging in place. In this paper, by reviewing three influential projects in this field of in-home monitoring for aging in place, we present our opinions and suggestions on the development of informatics-supported aging in place for its practical application in healthcare such as diagnosis and nursing in the era of data science...
2018: Studies in Health Technology and Informatics
Nida Saddaf Khan, Sayeed Ghani, Sajjad Haider
IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it...
May 25, 2018: Sensors
Suman Rao, Prashanth Thankachan, Bharadwaj Amrutur, Maryann Washington, Prem K Mony
Background: Remote biomonitoring of vital parameters in hospitals and homes has the potential to improve coverage and quality of maternal and neonatal health. Wearable sensors coupled with modern information and communication technology now offer an opportunity to monitor temperatures and kangaroo mother care (KMC) adherence in a continuous and real-time manner remotely for several days' duration in hospital and home settings. Using an innovative remote biomonitoring device to measure both temperature and baby position, we undertook a techno-feasibility study in preparation for a clinical trial...
2018: Pilot and Feasibility Studies
Sunghoon I Lee, Catherine P Adans-Dester, Matteo Grimaldi, Ariel V Dowling, Peter C Horak, Randie M Black-Schaffer, Paolo Bonato, Joseph T Gwin
High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using, or attempting to use, the stroke-affected upper limb during Activities of Daily Living (ADL) are effective ways to achieve high-dosage motor practice in stroke survivors. This paper presents a novel technological approach that enables 1) detecting goal-directed upper limb movements during the performance of ADL, so that timely feedback can be provided to encourage the use of the affected limb, and 2) assessing the quality of motor performance during in-home rehabilitation exercises so that appropriate feedback can be generated to promote high-quality exercise...
2018: IEEE Journal of Translational Engineering in Health and Medicine
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