Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Kinematic Analysis of the Postural Demands in Professional Soccer Match Play Using Inertial Measurement Units

The development of wearable sensors has allowed the analysis of trunk kinematics in match play, which is necessary for a better understanding of the postural demands of the players. The aims of this study were to analyze the postural demands of professional soccer players by playing position. A longitudinal study for 13 consecutive microcycles, which included one match per microcycle, was conducted. Wearable sensors with inertial measurement units were used to collect the percentage (%) of playing time spent and G-forces experienced in different trunk inclinations and the inclination required for different speeds thresholds. The inclination zone had a significant effect on the time percentage spent on each zone (p < 0.001, partial eta-squared (ηp2 = 0.85) and the G-forces experienced by the players (p < 0.001, ηp2 = 0.24). Additionally, a significant effect of the speed variable on the trunk inclination zones was found, since trunk flexion increased with greater speeds (p < 0.001; ηp2 = 0.73), except for midfielders. The players spent most of the time in trunk flexion between 20° and 40°; the greatest G-forces were observed in trunk extension zones between 0° and 30°, and a linear relationship between trunk inclination and speed was found. This study presents a new approach for the analysis of players’ performance. Given the large volumes of trunk flexion and the interaction of playing position, coaches are recommended to incorporate position-specific training drills aimed to properly prepare the players for the perception-action demands (i.e., visual exploration and decision-making) of the match, as well as trunk strength exercises and other compensatory strategies before and after the match

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

Electrical and Computer Engineering Annual Report 2016

Faculty Directory Faculty Highlights Faculty Fellow Program Multidisciplinary Research Fills Critical Needs Better, Faster Technology Metamaterials: Searching for the Perfect Lens The Nontraditional Power of Demand Dispatch Space, Solar Power\u27s Next Frontier Kit Cischke, Award-Winning Senior Lecturer Faculty Publications ECE Academy Class of 2016 Staff Profile: Michele Kamppinen For the Love of Teaching: Jenn Winikus Graduate Student Highlights Undergraduate Student Highlights External Advisory Committee Contracts and Grants Department Statistics AAES National Engineering Awardhttps://digitalcommons.mtu.edu/ece-annualreports/1002/thumbnail.jp

ME-EM 2018-19 Annual Report

Table of Contents Faculty Research Enrollment & Degrees Department News Graduates Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1000/thumbnail.jp

Three-Dimensional Measurement of Spinal Kinematics and Whole-Body Activity Recognition

Back pain is one of the leading causes of disability, being the second largest contributor to work days missed, and sixth largest disability when expressed in terms of an overall burden measured in disability-adjusted life years. Back pain is a large economic burden, where indirect costs from work days missed far outweigh the direct costs due to treatment. As such, it is economically better to prevent back pain from occurring, rather than treating it after the onset of pain. Some risk factors of back pain which can be monitored to help in the prevention of pain include poor posture and prolonged sedentary behaviour. Inactivity, being similar to prolonged sedentary behaviour, is also a risk factor for some of the major non-communicable diseases responsible for death including heart diseases, stroke, breast and colon cancer, and diabetes. The aims of the thesis were to: 1) compare a number of commonly used measurement systems, including a low-cost wearable sensor, in their ability to measure motion typically seen in the human spine; 2) develop an activity classification model capable of predicting everyday activities including standing, sitting, lying, and walking; 3) create a new, inexpensive device that can simultaneously track user spine posture/kinematics and activity; and 4) validate the device to have accuracy within ±5° for spine posture, and an average positive activity classification rate of 90% or above. This research demonstrates the accuracy of a low-cost wearable sensor in its ability to track motion similar to that of the human spine under typical conditions and compare this to more expensive systems. Using two accelerometers and machine learning, a new activity recognition model was created with the ability to track 13 distinct activities commonly used in daily living, being: standing, sitting, prone, supine, right-side, and left-side lying, walking, jogging, jumping, stair ascending, stair descending, walking on an incline, and transitions. From this new knowledge, a new concept inertial-sensor-based device was created with the capabilities of measuring spinal kinematics and whole-body activity tracking. The device has been developed to measure spinal motions with mean errors of ±2.5°, and therefore meeting the aim to have an accuracy within ±5°, while also showing that the more superior the position on the spine an inertial sensor is placed, the higher the errors in measurement. The device can also predict standing, sitting, lying, and walking with an average accuracy of 95.6%, and therefore above the desired accuracy of 90%. When including all activities, the classifier has an average accuracy of 90.3%. To reduce the global effect of back pain, the developed device has the capabilities to aid in the prevention, management, and rehabilitation of back pain by focussing on two risk factors: poor posture and inactivity. For use in this research, the definition of a good posture is one that compromises between minimising spinal load and minimise muscle activity, therefore a poor posture is one that doesn’t adhere to this requirement which could significantly increase the risk of the onset of back pain. For widespread use, the device created in this research has been developed to be as inexpensive as possible. To meet these goals, the future work of the device has been outlined, including size and cost reduction, as well as increasing the aesthetic appeal, thus making it a more appealing product to the general population.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

A modelling approach for evaluating impacts of hydropeaking in a sub-arctic river

Abstract. The release of pulses of water to increase hydroelectric power production at hydropower dams to meet daily peaks in electricity demands is called hydropeaking. Due to energy supply and demand fluctuations, the energy markets direct hydropower companies to balance load fluctuations through variations in power generation which result in flow regulation. More recently, this regulation is being carried out at shorter time intervals i.e., intra-daily and intra-hourly levels. The hydropeaking phenomenon increases drastically at shorter time intervals, severely impacting the riverine and riparian ecosystem. Social, economic, and ecological impacts arise from short-term hydropeaking. Furthermore, recreational services offered by the river are also impacted. This research develops a novel methodology for assessing these impacts in a strongly regulated sub-arctic river in Finland, i.e., Kemijoki River, Ossauskoski-Tervola reach. The methodology combines assessment of seasonal variations in sub-daily hydropeaking, two-dimensional hydrodynamic modelling, and a high-resolution land cover map developed through supervised land use classification via a machine learning algorithm. The results obtained include; the identification of a zone of influence of hydropeaking at sub-daily levels during each season, the total and class-wise area affected during each peaking event, and vulnerability zonation for water-based recreation in the river reach. The overall area of reach affected by peaking in Winter was (1.05 km2), Spring (0.96 km2), Summer (1.39 km2), and Autumn (0.66 km2). A vulnerability mapping was also carried out for the suitability of water-based recreation in the study reach. The novel methodology developed in this research which defines the vulnerable zone of hydropeaking can be used as the first step in detailed impacts assessment studies such as those for impacts on fish habitat and sediment transport processes in the river. The hydropeaking-influenced zone can be used to set thresholds for ecological flows and ramping rates downstream of power stations and opens avenues for future research, development, and policy endeavors for riparian ecosystem impact assessment and mitigation

Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running

Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products