Human gait modelling with step estimation and phase classification utilising a single thigh mounted IMU for vision impaired indoor navigation

This research is focused on human gait modelling for infrastructure free inertial navigation for vision impaired. A pedometer based on a single thigh mounted gyroscope, an efficient algorithm to estimate thigh flexion and extension, gait models for level walking, a model to estimate step length and a technique to detect gait phases based on a single thigh mounted Inertial Measurement Unit (IMU) were developed and confirmed higher accuracies

Smartphone Based Personalized Balance Training Platform

ME450 Capstone Design and Manufacturing Experience: Winter 2021Older adults are at high risk of falls, mainly due to the loss of balance control. It is important for them to regain balance control through balance training exercises for quality living. These exercises are conventionally done in a clinic-based setting under the supervision of a physical therapist (PT). However, this method comes with limitations such as cost, insurance reimbursement policies, and travel. Thus, there is a need for a portable balance training platform that can be used by older adults at home. Our team is developing a platform as such that can not only provide balance training to our users but can also measure kinematic data from multiple body parts and capture self-performance ratings after exercises are performed - these data are uploaded to a secure cloud account. The platform can also support a machine learning framework that generates a list of recommended exercises and simulated PT ratings for the users based on their performance during the balance training exercise sessions.Jamie Ferris, Safa Jabri, Christopher DiCesare, Xun Huan: Sienko Research Grouphttp://deepblue.lib.umich.edu/bitstream/2027.42/167652/1/Team_8-Smartphone_Based_Personalized_Balance_Training_Platform.pd

UAV-BASED GEOTECHNICAL MODELING AND MAPPING OF AN INACCESSIBLE UNDERGROUND SITE

Digital photogrammetry is becoming a more common method used for mapping geological and structural rock mass features in underground mining. The issue of capturing geological and structural data in inaccessible, unsupported areas of mines remains even when utilizing terrestrial photogrammetric methods; thus, geotechnical models of mines are left with incomplete datasets. Large unsupported underground voids, like stopes, have the potential to cause major failures, but by filling in the geotechnical data gaps in inaccessible areas, potential failures can be predicted through kinematic analysis of the area’s mapped discontinuities. Implementation of Unmanned Aerial Vehicles (UAVs) in underground mines and recent advances in obstacle detection systems have allowed for greater experimentation with photogrammetry conducted from a UAV platform in mines. For this study, a UAV-based underground photogrammetry system was developed to manually capture imagery in an inaccessible stope at Barrick Gold Corporation’s Golden Sunlight Mine (GSM) in Whitehall, Montana, to see whether or not the approach is a viable remote sensing technique for gathering georeferenced geotechnical data. Development of the system involved selecting an appropriate UAV platform, identifying a lighting system capable of providing adequate illumination, acquiring a sensor system that consistently avoids obstacles, and choosing the appropriate UAV camera (and its respective settings) for underground UAV-based imaging. In order to georeference the data collected in the inaccessible stope, paintballs were shot into the stope to create ground control points that were then surveyed in laser range detection. These paintball marks had to be in visual line-of-sight and visible in the imagery captured via UAV camera in order to georeferenced them. Using the imagery collected in the stope at GSM, models were constructed and structural features were mapped on those models. Bentley ContextCapture software was able to successfully construct a stope model from the video frame imagery collected via UAV in the stope, while ADAM Technology was not. Split-Engineering’s Split-FX and ADAM Technology were used separately to map the discontinuity planes present within the model. A comparison of underground discontinuity mapping was performed using the UAV-based photogrammetry captured in the stope and hand mapping data collected around the entrance to the stope. It was found that northeasterly striking discontinuity planes were identified using the digital mapping, but not in hand mapping. Using UAV-based photogrammetry for geotechnical data collection creates a quick and thorough mapping process with time-stamped imagery that can potentially create a safer mine. The lessons learned during this study may help guide future efforts using UAVs to capture geologic data and to help monitor stability in areas that are inaccessible

Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions

This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering. A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components. An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system. The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions. An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation. The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system