Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

An Evaluation of the Suitability of Commercially Available Sensors for Use in a Virtual Reality Prosthetic Arm Motion Tracking Device

The loss of a hand or arm is a devastating life event that results in many months of healing and challenging rehabilitation. Technology has allowed the development of an electronic replacement for a lost limb but similar advancements in therapy have not occurred. The situation is made more challenging because people with amputations often do not live near specialized rehabilitation centres. As a result, delays in therapy can worsen common complications like nerve pain and joint stiffness. For children born without a limb, poor compliance with the use of their prosthesis leads to delays in therapy and may affect their development. In many parts of the world, amputation rehabilitation does not exist. Fortunately, we live in an age where advances in technology and engineering can help solve these problems. Virtual reality creates a simulated world or environment through computer animation much like what is seen in modern video games. An experienced team of rehabilitation doctors, therapists, engineers and computer scientists are required to realize a system such as this. A person with an amputation will be taught to control objects in the virtual world by wearing a modified electronic prosthesis. Using computers, it will be possible to analyze his or her movements within the virtual world and improve the wearer's skills. The goals of this system include making the system portable and internet compatible so that people living in remote areas can also receive therapy. The novel approach of using virtual reality to rehabilitate people with upper limb amputations will help them return to normal activities by providing modern and appropriate rehabilitation, reducing medical complications, improving motivation (via gaming modules), advancing health care technology and reducing health care costs. The use of virtual reality technology in the field of amputee rehabilitation is in its earliest stages of development world wide. A virtual environment (VE) will facilitate the early rehabilitation of a patient before they are clinically ready to be fitted with an actual prosthesis. In order to create a successful virtual reality rehabilitation system such as this, an accurate method of tracking the arm in real-time is necessary. A linear displacement sensor and a microelectromechanical system (MEMS) inertial measurement unit (IMU) were used to create a device for capturing the motion of a user's movement with the intent that the data provided by the device be used along with a VE as a virtual rehabilitation tool for new upper extremity amputation patients. This thesis focuses on the design and testing of this motion capture device in order to determine the suitability of current commercially available sensing components as used in this system. Success will be defined by the delivery of accurate position and orientation data from the device so that that data can be used in a virtual environment. Test results show that with current MEMS sensors, the error introduced by double integrating acceleration data is too significant to make an IMU an acceptable choice for position tracking. However, the device designed here has proven to be an excellent cable emulator, and would be well suited if used as an orientation tracker

Associations Between the PlayerLoad™ Metric and Common IMU-based Physical Activity-Related Measures of Human Movement

Over the last 60 years, supplemental strength and conditioning for improving sport performance has gone from being nearly non-existent to a cornerstone feature of elite collegiate, amateur, and professional athletics. Concurrent with this profession-related evolution, technologies for monitoring and tracking physical performance have become more prevalent and useful. As an example, the Optimeye S5 from Catapult Sports is currently the most widely used sport performance-related inertial measurement unit (IMU) available for monitoring sport-related movement and is used by more than 2,500 athletic teams globally. The primary metric of interest quantified by this device is known as PlayerLoad™. PlayerLoad™ represents a cumulative summarization of changes in acceleration across three axes of measurement. Although somewhat similar in its formulation to commonly used physical activity-related acceleration metrics, such as band pass filtered Euclidean norm (BFEN) and Euclidean norm minus one (ENMO), no published reports have evaluated the associations between PlayerLoad™ and any of the aforementioned measures. We obtained two publicly available data sets (REAListic DISPlacement [REALDISP] and MHEALTH) containing high resolution IMU data. The REALDISP data set contained data from 17 participants (10 males, ages 22 to 37 years-old) completing 33 unique activities while wearing 9 IMUs across the body (upper-back; right and left side: upper-arm, lower-arm, thigh, and calf). The MHEALTH data set contained data from 10 participants completing 11 unique activities while wearing 3 IMUs (right wrist, left pectoral, left ankle). Raw acceleration data collected from the IMUs was used to compute per second summaries of PlayerLoad™, BFEN, and ENMO. Association analyses revealed that PlayerLoad™ was highly correlated with BFEN and ENMO at all evaluated IMU locations (r = 0.742 to 0.983); however, the strength of these associations did significantly vary across many locations. Notably, lower-body IMU measurement locations (r = 0.951 to 0.983) generally produced higher correlations for PlayerLoad™ with BFEN and ENMO than did upper-body locations (r = 0.742 to 0.960). Models were also developed for predicting epoch- and summary-level PlayerLoad™ from BFEN and ENMO. Unbiased pseudo R2 values exceeded 0.879 in all instances indicating high predictive ability for all models. More research is needed to better understand PlayerLoad™ and its potential value for quantifying time-based dosages of human movement

Constructing a reference standard for sports science and clinical movement sets using IMU-based motion capture technology

Motion analysis has improved greatly over the years through the development of low-cost inertia sensors. Such sensors have shown promising accuracy for both sport and medical applications, facilitating the possibility of a new reference standard to be constructed. Current gold standards within motion capture, such as high-speed camera-based systems and image processing, are not suitable for many movement-sets within both sports science and clinical movement analysis due to restrictions introduced by the movement sets. These restrictions include cost, portability, local environment constraints (such as light level) and poor line of sight accessibility. This thesis focusses on developing a magnetometer-less IMU-based motion capturing system to detect and classify two challenging movement sets: Basic stances during a Shaolin Kung Fu dynamic form, and severity levels from the modified UPDRS (Unified Parkinson’s Disease Rating Scale) analysis tapping exercise. This project has contributed three datasets. The Shaolin Kung Fu dataset is comprised of 5 dynamic movements repeated over 350 times by 8 experienced practitioners. The dataset was labelled by a professional Shaolin Kung Fu master. Two modified UPDRS datasets were constructed, one for each of the two locations measured. The modified UPDRS datasets comprised of 5 severity levels each with 100 self-emulated movement samples. The modified UPDRS dataset was labelled by a researcher in neuropsychological assessment. The errors associated with IMU systems has been reduced significantly through a combination of a Complementary filter and applying the constraints imposed by the range of movements available in human joints. Novel features have been extracted from each dataset. A piecewise feature set based on a moving window approach has been applied to the Shaolin Kung Fu dataset. While a combination of standard statistical features and a Durbin Watson analysis has been extracted from the modified UPDRS measurements. The project has also contributed a comparison of 24 models has been done on all 3 datasets and the optimal model for each dataset has been determined. The resulting models were commensurate with current gold standards. The Shaolin Kung Fu dataset was classified with the computational costly fine decision tree algorithm using 400 splits, resulting in: an accuracy of 98.9%, a precision of 96.9%, a recall value of 99.1%, and a F1-score of 98.0%. A novel approach of using sequential forward feature analysis was used to determine the minimum number of IMU devices required as well as the optimal number of IMU devices. The modified UPDRS datasets were then classified using a support vector machine algorithm requiring various kernels to achieve their highest accuracies. The measurements were repeated with a sensor located on the wrist and finger, with the wrist requiring a linear kernel and the finger a quadratic kernel. Both locations achieved an accuracy, precision, recall, and F1-score of 99.2%. Additionally, the project contributed an evaluation to the effect sensor location has on the proposed models. It was concluded that the IMU-based system has the potential to construct a reference standard both in sports science and clinical movement analysis. Data protection security and communication speeds were limitations in the system constructed due to the measured data being transferred from the devices via Bluetooth Low Energy communication. These limitations were considered and evaluated in the future works of this project

Aeronautical Engineering: A Continuing Bibliography with indexes

This bibliography lists 426 reports, articles and other documents introduced into the NASA scientific and technical information system in August 1984. Reports are cited in the area of Aeronautical Engineering. The coverage includes documents on the engineering and theoretical aspects of design, construction, evaluation, testing operation and performance of aircraft (including aircraft engines) and associated components, equipment and systems

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy

An inertial sensor system for analysing human movement

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Studies on Sensor Aided Positioning and Context Awareness

This thesis studies Global Navigation Satellite Systems (GNSS) in combination with sensor systems that can be used for positioning and obtaining richer context information. When a GNSS is integrated with sensors, such as accelerometers, gyroscopes and barometric altimeters, valuable information can be produced for several applications; for example availability or/and performance of the navigation system can be increased. In addition to position technologies, GNSS devices are integrated more often with different types of technologies to fulfil several needs, e.g., different types of context recognition. The most common integrated devices are accelerometer, gyroscope, and magnetometer but also other sensors could be used.More specifically, this thesis presents sensor aided positioning with two satellite signals with altitude assistance. The method uses both pseudorange and Doppler measurements. The system is required to be stationary during the process and a source of altitude information, e.g., a MEMS barometer, is needed in addition to a basic GNSS receiver. Authentic pseudorange and Doppler measurements with simulated altitude were used used to test the algorithm. Results showed that normally the accuracy of couple of kilometers is acquired. Thesis also studies on what kind of errors barometric altimeter might encounter especially in personal positioning. The results show that barometers in differential mode provide highly accurate altitude solution (within tens of centimeters), but local disturbances in pressure need to be acknowledged in the application design. For example, heating, ventilating, and air conditioning in a car can have effect of few meters. Thus this could cause problems if the barometer is used as a altimeter for under meter-level positioning or navigation.We also explore methods for sensor aided GNSS systems for context recognition. First, the activity and environment recognition from mobile phone sensor and radio receiver data is investigated. The aim is in activity (e.g., walking, running, or driving a vehicle) and environment (e.g., street, home, or restaurant) detection. The thesis introduces an algorithm for user specific adaptation of the context model parameters using the feedback from the user, which can provide a confidence measure about the correctness of a classification. A real-life data collection campaign validate the proposed method. In addition, the thesis presents a concept for automated crash detection to motorcycles. In this concept, three different inertial measurement units are attached to the motorist’s helmet, torso of the motorist, and to the rear of the motor cycle. A maximum a posteriori classifier is trained to classify the crash and normal driving. Crash dummy tests were done by throwing the dummy from different altitudes to simulate the effect of crash to the motorist and real data is collected by driving the motorcycle. Preliminary results proved the potential of the proposed method could be applicable in real situations. In all the proposed systems in this thesis, knowledge of the context can help the positioning system, but also positioning system can help in determining the context