Analysis of Raw GNSS Measurements Derived Navigation Solutions from Mobile Devices with Inertial Sensors

2019 ◽  
Author(s):  
Dong-Kyeong Lee ◽  
Matthias Petit ◽  
Damian Miralles ◽  
Sherman Lo ◽  
Dennis Akos
Keyword(s):  
Mobile Devices ◽  
Inertial Sensors ◽  
Gnss Measurements

scholarly journals User-independent accelerometer-based gesture recognition for mobile devices

2013 ◽  
Vol 1 (3) ◽  
pp. 11-25 ◽  
Author(s):  
Xian Wang ◽  
Paula Tarrío ◽  
Ana María Bernardos ◽  
Eduardo Metola ◽  
José Ramón Casar
Keyword(s):  
Computational Complexity ◽  
Mobile Devices ◽  
Gesture Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Smart Phone ◽  
Recognition System ◽  
Independent Manner ◽  
Wheeled Robot

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user-independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human-robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

scholarly journals Mobile Applications in Evaluations of Knee Joint Kinematics: A Pilot Study

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3675 ◽  
Author(s):  
Lisiński ◽  
Wareńczak ◽  
Hejdysz ◽  
Sip ◽  
Gośliński ◽  
...  
Keyword(s):  
Knee Joint ◽  
Mobile Devices ◽  
Home Environment ◽  
Mobile Applications ◽  
Inertial Sensors ◽  
Qualitative And Quantitative ◽  
Study Results ◽  
Left Lower Limb ◽  
Knee Joint Kinematics ◽  
The Right

Because medical professionals lack the means to monitor exercises performed by patients in their home environment directly, there is a strong case for introducing technological solutions into this domain. They include methods that use wireless inertial sensors, which emit signals recorded and processed by special applications that work with mobile devices. This paper’s aim is (a) to evaluate whether such sensors are suitable for qualitative and quantitative motion analysis, and (b) to determine the repeatability of results over a few recordings. Knee joint activity was analysed using a system of inertial sensors connected through a Wi-Fi network to mobile devices. The tested individuals did eight different activities, all of which engaged the knee joint. Each excercise was repeated three times. Study results did not reveal any statistically significant differences between the three measurements for most of the studied parameters. Furthermore, in almost every case, there were no statistically significant differences between the results of the right and left lower limb (p > 0.05). This study shows that easy use and repeatability of results combined with the feature of quantitative and qualitative analysis make the examined method useful for functional evaluations of the knee joint.

scholarly journals Can We Rely on Mobile Devices and Other Gadgets to Assess the Postural Balance of Healthy Individuals? A Systematic Review

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2972 ◽  
Author(s):  
Alexandre S. Pinho ◽  
Ana P. Salazar ◽  
Ewald M. Hennig ◽  
Barbara C. Spessato ◽  
Antoinette Domingo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Postural Balance ◽  
Inertial Sensors ◽  
Cost Benefit ◽  
Healthy Adults ◽  
Discriminatory Power ◽  
Healthy Individuals

The consequences of falls, costs, and complexity of conventional evaluation protocols have motivated researchers to develop more effective balance assessments tools. Healthcare practitioners are incorporating the use of mobile phones and other gadgets (smartphones and tablets) to enhance accessibility in balance evaluations with reasonable sensitivity and good cost–benefit. The prospects are evident, as well as the need to identify weakness and highlight the strengths of the different approaches. In order to verify if mobile devices and other gadgets are able to assess balance, four electronic databases were searched from their inception to February 2019. Studies reporting the use of inertial sensors on mobile and other gadgets to assess balance in healthy adults, compared to other evaluation methods were included. The quality of the nine studies selected was assessed and the current protocols often used were summarized. Most studies did not provide enough information about their assessment protocols, limiting the reproducibility and the reliability of the results. Data gathered from the studies did not allow us to conclude if mobile devices and other gadgets have discriminatory power (accuracy) to assess postural balance. Although the approach is promising, the overall quality of the available studies is low to moderate.

scholarly journals LOCATE-US: Indoor Positioning for Mobile Devices Using Encoded Ultrasonic Signals, Inertial Sensors and Graph-Matching

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1950
Author(s):  
David Gualda ◽  
María Carmen Pérez-Rubio ◽  
Jesús Ureña ◽  
Sergio Pérez-Bachiller ◽  
José Manuel Villadangos ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Acoustic Waves ◽  
Graph Matching ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Positioning Systems

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others.

scholarly journals Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 528 ◽  
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
Gonçalo Marques ◽  
Nuno M. Garcia ◽  
...  
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Mobile Devices ◽  
Inertial Sensors ◽  
Dynamic Balance ◽  
Well Being ◽  
State Transitions ◽  
Motion Sensors ◽  
Timed Up And Go ◽  
Test Execution

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

scholarly journals Measurement of Results of Functional Reach Test with Sensors: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1078
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Eftim Zdravevski
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Mobile Devices ◽  
Inertial Sensors ◽  
Dynamic Balance ◽  
Medical Specialties ◽  
Stroke Treatment ◽  
Functional Reach ◽  
Wide Range ◽  
Functional Reach Test

The test of physical conditions is important to treat and presents several diseases related to the movement. These diseases are mainly related to the physiotherapy and orthopedy, but it can be applied in a wide range of medical specialties. The Functional Reach Test is one of the most common physical tests used to measure the limit of stability that is highly important for older adults because their stability is reduced with aging. Thus, older adults are part of the population more exposed to stroke. This test may help in the measurement of the conditions related to post-stroke and stroke treatment. The movements related to this test may be recorded and recognized with the inertial sensors available in off-the-shelf mobile devices. This systematic review aims to determine how to determine the conditions related to this test, which can be detected, and which of the sensors are used for this purpose. The main contribution of this paper is to present the research on the state-of-the-art use of sensors available on off-the-shelf mobile devices to measure Functional Reach Test results. This research shows that the sensors that are used in the literature studies are inertial sensors and force sensors. The features extracted from the different studies are categorized as dynamic balance, quantitative, and raw statistics. These features are mainly used to recognize the different parameters of the test, and several accidents, including falling. The execution of this test may allow the early detection of different diseases.

scholarly journals Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3803 ◽  
Author(s):  
Abayomi Otebolaku ◽  
Timibloudi Enamamu ◽  
Ali Alfoudi ◽  
Augustine Ikpehai ◽  
Jims Marchang ◽  
...  
Keyword(s):  
Time Series ◽  
Mobile Devices ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Deep Convolutional Neural Networks ◽  
Class Imbalance Problem ◽  
Context Recognition ◽  
Embedded Sensing ◽  
Sensor Signals ◽  
Ambient Sensing

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy.

scholarly journals Identification of Warning Situations in Road Using Cloud Computing Technologies and Sensors Available in Mobile Devices: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 416
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia
Keyword(s):  
Systematic Review ◽  
Mobile Devices ◽  
Major Part ◽  
Inertial Sensors ◽  
Magnetic Sensors ◽  
Systematic Research ◽  
Acoustic Sensors ◽  
Small Areas ◽  
Imaging Sensors ◽  
Future Work

The use of mobile devices connected continuously to the cloud is increasing, and the development of a cloud-based solution may power the function of these devices in mobility. Several types of sensors available in the mobile devices may allow the acquisition of different kinds of data, including inertial sensors, magnetic sensors, location sensors, acoustic sensors, and imaging sensors. The primary purpose of this study is to review the methods, features, and studies related to the identification of road conditions and warning situations. We performed systematic research to discover relevant studies written in English for the identification of different situations using the sensors available in the mobile devices, published between 2011 and 2019. After that, we analyzed the remaining studies to verify its reproducibility. The major part of the studies does not report the accuracy in the detection of warning situations. As future work, we intend to develop a system based on the Centre of Portugal for the detection of warning situations, road problems, and other issues verified during driving activities. As future work, we intend to develop a system using only a mobile device for the acquisition of sensors data in the centre of Portugal. We verified that the majority of the studies were performed in big lands, but in small areas, the number of accidents and road abnormalities is also high.

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