It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications

Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smart-phones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications. In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no prior-configuration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of $15^\circ$ averaged over a wide variety of phone positions. This is $558\%$ better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.Comment: Accepted for publication in the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2014

Evaluating the Effects of Powered Prostheses on Walking Biomechanics In and Out of the Lab

Powered ankle prostheses aim to replicate the biological ankle function for individuals with transtibial amputation. The effects of powered prostheses on gait have been experimentally quantified in several studies. The findings are non-universal and potentially dependent on user characteristics. Disparate responses to the powered prosthesis among users point to unanswered questions regarding the fundamental biomechanical effects of the device. Additionally, it is unknown how powered prostheses impact daily function. To address these gaps in knowledge, I investigated various biomechanical and clinical outcomes of the powered prosthesis in the laboratory and in the users’ everyday lives. My research can be broken down into two approaches: experimental analyses of functional capacity and examinations of functional performance in everyday life. To assess functional capacity in the lab, I first explored users’ neuromuscular adaptations to the powered prosthesis (Chapter 2). Specifically, I quantified changes in lower-limb muscle activations and their relationships with changes in metabolic cost. This aim revealed the potential importance of effective residual limb stabilization as a contributor to metabolic reductions. Second, I sought to quantify fatigue-related compensations in walking with the powered prosthesis (Chapter 3). While the powered prosthesis did not improve the user’s endurance, there were differences in hip joint compensation strategies when wearing unpowered and powered prostheses. I then developed methods to bring gait analysis out of the lab, to assess functional performance in daily life. I first explored the use of portable GPS and IMU sensors to quantify functional mobility in everyday walking (Chapter 4). Through this work I demonstrated the clinical viability of estimating cadence and walking speeds in different real-world environments. I then applied these techniques to assess changes to the volume and characteristics of walking with a powered prosthesis in daily life (Chapter 5). Further, I examined the relationships between capacity in the lab, performance in daily life, and the users’ perceptions of mobility. Lastly, I examined potential implications of the powered prosthesis on trips and falls in daily life (Chapter 6). In this study, I applied a novel method for using IMU signals to estimate minimum toe clearance in daily life. Findings from Chapters 5 and 6 suggest there were no universal powered prosthesis-related changes to gait in daily life. Together, these works add to the existing body of literature and reinforces the notion that the benefits of the powered prosthesis are non-universal and subject-specific. Chapters 4 through 6 specifically represent a meaningful first step toward an evidence-based approach in the prescription, design, and assessment of powered prostheses.PHDKines&MechEng PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168019/1/jaywoo_1.pd

Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform

Navigation and location technologies are continually advancing, allowing ever higher accuracies and operation under ever more challenging conditions. The development of such technologies requires the rapid evaluation of a large number of sensors and related utilization strategies. The integration of Global Navigation Satellite Systems (GNSSs) such as the Global Positioning System (GPS) with accelerometers, gyros, barometers, magnetometers and other sensors is allowing for novel applications, but is hindered by the difficulties to test and compare integrated solutions using multiple sensor sets. In order to achieve compatibility and flexibility in terms of multiple sensors, an advanced adaptable platform is required. This paper describes the design and testing of the NavCube, a multi-sensor navigation, location and timing platform. The system provides a research tool for pedestrian navigation, location and body motion analysis in an unobtrusive form factor that enables in situ data collections with minimal gait and posture impact. Testing and examples of applications of the NavCube are provided

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Visual-Inertial first responder localisation in large-scale indoor training environments.

Accurately and reliably determining the position and heading of first responders undertaking training exercises can provide valuable insights into their situational awareness and give a larger context to the decisions made. Measuring first responder movement, however, requires an accurate and portable localisation system. Training exercises of- ten take place in large-scale indoor environments with limited power infrastructure to support localisation. Indoor positioning technologies that use radio or sound waves for localisation require an extensive network of transmitters or receivers to be installed within the environment to ensure reliable coverage. These technologies also need power sources to operate, making their use impractical for this application. Inertial sensors are infrastructure independent, low cost, and low power positioning devices which are attached to the person or object being tracked, but their localisation accuracy deteriorates over long-term tracking due to intrinsic biases and sensor noise. This thesis investigates how inertial sensor tracking can be improved by providing correction from a visual sensor that uses passive infrastructure (fiducial markers) to calculate accurate position and heading values. Even though using a visual sensor increase the accuracy of the localisation system, combining them with inertial sensors is not trivial, especially when mounted on different parts of the human body and going through different motion dynamics. Additionally, visual sensors have higher energy consumption, requiring more batteries to be carried by the first responder. This thesis presents a novel sensor fusion approach by loosely coupling visual and inertial sensors to create a positioning system that accurately localises walking humans in largescale indoor environments. Experimental evaluation of the devised localisation system indicates sub-metre accuracy for a 250m long indoor trajectory. The thesis also proposes two methods to improve the energy efficiency of the localisation system. The first is a distance-based error correction approach which uses distance estimation from the foot-mounted inertial sensor to reduce the number of corrections required from the visual sensor. Results indicate a 70% decrease in energy consumption while maintaining submetre localisation accuracy. The second method is a motion type adaptive error correction approach, which uses the human walking motion type (forward, backward, or sideways) as an input to further optimise the energy efficiency of the localisation system by modulating the operation of the visual sensor. Results of this approach indicate a 25% reduction in the number of corrections required to keep submetre localisation accuracy. Overall, this thesis advances the state of the art by providing a sensor fusion solution for long-term submetre accurate localisation and methods to reduce the energy consumption, making it more practical for use in first responder training exercises

An automatic wearable multi-sensor based gait analysis system for older adults.

Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities

Evaluation of Pedometer Performance Across Multiple Gait Types Using Video for Ground Truth

This dissertation is motivated by improving healthcare through the development of wearable sensors. This work seeks improvement in the evaluation and development of pedometer algorithms, and is composed of two chapters describing the collection of the dataset and describing the im-plementation and evaluation of three previously developed pedometer algorithms on the dataset collected. Our goal is to analyze pedometer algorithms under more natural conditions that occur during daily living where gaits are frequently changing or remain regular for only brief periods of time. We video recorded 30 participants performing 3 activities: walking around a track, walking through a building, and moving around a room. The ground truth time of each step was manu-ally marked in the accelerometer signals through video observation. Collectively 60,853 steps were recorded and annotated. A subclass of steps called shifts were identified as those occurring at the beginning and end of regular strides, during gait changes, and during pivots changing the direction of motion. While shifts comprised only .03% of steps in the regular stride activity, they comprised 10-25% of steps in the semi-regular and unstructured activities. We believe these motions should be identified separately, as they provide different accelerometer signals, and likely result in different amounts of energy expenditure. This dataset will be the first to specifically allow for pedometer algorithms to be evaluated on unstructured gaits that more closely model natural activities. In order to provide pilot evaluation data, a commercial pedometer, the Fitbit Charge 2, and three prior step detection algorithms were analyzed. The Fitbit consistently underestimated the total number of steps taken across each gait type. Because the Fitbit algorithm is proprietary, it could not be reimplemented and examined beyond a raw step count comparison. Three previously published step detection algorithms, however, were implemented and examined in detail on the dataset. The three algorithms are based on three different methods of step detection; peak detection, zero crossing (threshold based), and autocorrelation. The evaluation of these algorithms was performed across 5 dimensions, including algorithm, parameter set, gait type, sensor position, and evaluation metric, which yielded 108 individual measures of accuracy. Accuracy across each of the 5 dimensions were examined individually in order to determine trends. In general, training parameters to this dataset caused a significant accuracy improvement. The most accurate algorithm was dependent on gait type, sensor position, and evaluation metric, indicating no clear “best approach” to step detection. In general, algorithms were most accurate for regular gait and least accurate for unstructured gait. In general, accuracy was higher for hip and ankle worn sensors than it was for wrist worn sensors. Finally, evaluation across running count accuracy (RCA) and step detection accuracy (SDA) revealed similar trends across gait type and sensor position, but each metric indicated a different algorithm with the highest overall accuracy. A classifier was developed to identify gait type in an effort to use this information to improve pedometer accuracy. The classifier’s features are based on the Fast Fourier Transform (FFT) applied to the accelerometer data gathered from each sensor throughout each activity. A peak detector was developed to identify the maximum value of the FFT, the width of the peak yielding the maximum value, and the number of peaks in each FFT. These features were then applied to a Naive Bayes classifier, which correctly identified the gait (regular, semi-regular, or unstructured) with 84% accuracy. A varying algorithm pedometer was then developed which switched between the peak detection, threshold crossing, and autocorrelation based algorithms depending on which algorithm performed best for the sensor location and detected gait type. This process yielded a step detection accuracy of 84%. This was a 3% improvement when compared to the greatest accuracy achieved by the best performing algorithm, the peak detection algorithm. It was also identified that in order to provide quicker real-time transitions between algorithms, the data should be examined in smaller windows. Window sizes of 3, 5, 8, 10, 15, 20, and 30 seconds were tested, and the highest overall accuracy was found for a window size of 5 seconds. These smaller windows of time included behaviors which do not correspond directly with the regular, semi-regular, and unstructured gait activities. Instead, three stride types were identified: steady stride, irregular stride, and idle. These stride types were identified with 82% accuracy. This experiment showed that at an activity level, gait detection can improve pedometer accuracy and indicated that applying the same principles to a smaller window size could allow for more responsive real-time algorithm selection

Online Learning of Gait Models

Gait event identification is the identification of gait events (e.g., foot impact) which occur cyclically, at the same location in each gait cycle. Gait event identification plays an important role in many applications, such as health monitoring, diagnosis, and rehabilitation. The majority of gait event identification algorithms are based on heuristics, many of which are threshold-based, making them sensitive to threshold parameters and causing poor generalization to new data (e.g., different gait type, ground surface, sensor placement, footwear, etc.). While a number of machine learning techniques have been proposed, they use offline training and do not generalize well to data that is different from the training set. This thesis proposes a novel approach for online, individualized gait analysis, based on an adaptive periodic model of any gait signal. The proposed method learns a model of the gait cycle during online measurement, using a continuous representation that can adapt to inter and intra-personal variability by creating an individualized model. The model of gait is learned online during observation, using incremental updates to the model parameters based on the error between the model-predicted and measured signal. The gait data is modeled as a periodic signal with a continuous phase variable, allowing data to be automatically labeled with a phase value corresponding to a particular event that re-occurs each gait cycle. Once the algorithm has converged to the input signal, key gait events can be identified based on the estimated gait phase. Two methods of gait event identification were implemented: analytical event identification and initial event identification. Since we learn a gait model that has an analytical representation, if we know the properties of the gait events of interest, we can use the model to directly compute the corresponding phase. For example, to identify the peak event in each gait cycle, we can solve for the phase which generates the maximum value and assign this as the peak phase value. In the initial event identification method, we provide a manual or heuristic identification of a gait event in the first converged gait cycle and use the corresponding event phase to identify all future events. Once gait events are identified relative to the estimated gait phase, we can automatically identify any future events since we assume they occur at the same phase, as is common in gait analysis. Our approach is implemented and tested on two datasets: one measuring medio lateral angular velocity of the ankles from a healthy young group of adults and the other measuring sagittal linear acceleration of the ankles from a group of retirement home residents who each have a variety of medical conditions. For the former dataset, the proposed approach converges within approximately five gait cycles and heel impact and toe takeoff events are extracted with an average error of 0.04 gait cycles, using the manual initial event identification method. For the latter dataset, the proposed approach converges within approximately eight gait cycles and initial swing events are extracted with an average error of 0.03 gait cycles, using the analytical event identification method. When using learning rates optimized on a set of training trials (opposed to a default set of learning rates), the proposed approach converges within approximately four gait cycles and maintains an average error of 0.03 gait cycles, on the corresponding set of test trials. Further, when including ground truth events occurring prior to the model having met convergence criteria, the average error is only slightly increased to 0.04 gait cycles