A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294)

3D Visualisation of Wearable Inertial/Magnetic Sensors

Inertial and inertial-magnetic sensors are miniaturised and lightweight which allows the technology to be used to assess human movements in sports. These sensors can produce up to nine channels of time series data which can often be difficult to read and interpret. This paper describes the development of a 3D visualisation tool for inertial/magnetic sensors. A sensor fusion technique known as an attitude heading reference system (AHRS) is used to calculate 3D orientation of the sensor. With AHRS data from a gyroscope and magnetometer can be expressed in animation as orientation of the sensor. However acceleration does not appear in the animation. Therefore the function to express the magnitude and direction of the acceleration using a shake of the sensor animation is added to the tool. It was confirmed that the tool produce an animation which clearly portrayed athlete posture and accelerations synchronised with the gait cycle

Stealthy progression of type 2 diabetes mellitus due to impaired ketone production in an adult patient with multiple acyl-CoA dehydrogenase deficiency

Background: Multiple acyl-CoA dehydrogenase deficiency (MADD) is an inherited metabolic disorder caused by biallelic pathogenic variants in genes related to the flavoprotein complex. Dysfunction of the complex leads to impaired fatty acid oxidation and ketone body production which can cause hypoketotic hypoglycemia with prolonged fasting. Patients with fatty acid oxidation disorders (FAODs) such as MADD are treated primarily with a dietary regimen consisting of high-carbohydrate foods and avoidance of prolonged fasting. However, information on the long-term sequelae associated with this diet have not been accumulated. In general, high-carbohydrate diets can induce diseases such as type 2 diabetes mellitus (T2DM), although few patients with both MADD and T2DM have been reported. Case: We present the case of a 32-year-old man with MADD who was on a high-carbohydrate diet for >30 years and exhibited symptoms resembling diabetic ketoacidosis. He presented with polydipsia, polyuria, and weight loss with a decrease in body mass index from 31 to 25 kg/m2 over 2 months. Laboratory tests revealed a HbA1c level of 13.9%; however, the patient did not show metabolic acidosis but only mild ketosis. Discussion/conclusion: This report emphasizes the potential association between long-term adherence to high-carbohydrate dietary therapy and T2DM development. Moreover, this case underscores the difficulty of detecting diabetic ketosis in patients with FAODs such as MADD due to their inability to produce ketone bodies. These findings warrant further research of the long-term complications associated with this diet as well as warning of the potential progression of diabetes in patients with FAODs such as MADD

An Architectural Based Framework for the Distributed Collection, Analysis and Query from Inhomogeneous Time Series Data Sets and Wearables for Biofeedback Applications

The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming

Novel Method for Estimating Propulsive Force Generated by Swimmers’ Hands Using Inertial Measurement Units and Pressure Sensors

Propulsive force is a determinant of swimming performance. Several methods have been proposed to estimate the propulsive force in human swimming; however, their practical use in coaching is limited. Herein, we propose a novel method for estimating the propulsive force generated by swimmers’ hands using an inertial measurement unit (IMU) and pressure sensors. In Experiment 1, we use a hand model to examine the effect of a hand-mounted IMU on pressure around the hand model at several flow velocities and water flow directions. In Experiment 2, we compare the propulsive force estimated using the IMU and pressure sensors (FIMU) via an underwater motion-capture system and pressure sensors (FMocap). Five swimmers had markers, pressure sensors, and IMUs attached to their hands and performed front crawl swimming for 25 m twice at each of nine different swimming speeds. The results show that the hand-mounted IMU affects the resultant force; however, the effect of the hand-mounted IMU varies with the flow direction. The mean values of FMocap and FIMU are similar (19.59 ± 7.66 N and 19.36 ± 7.86 N, respectively; intraclass correlation coefficient(2,1) = 0.966), and their waveforms are similar (coefficient of multiple correlation = 0.99). These results indicate that the IMU can estimate the same level of propulsive force as an underwater motion-capture system

Predicting Ground Reaction Forces in Sprint Running Using a Shank Mounted Inertial Measurement Unit

The transition from a stationary crouch on running-blocks to an erect running position is critical to success in sprint running. Three elite sprinters repeated five sprint starts on a 50 m-long instrumented running track each wearing three inertial measurement units (IMU) on both shanks. The IMU profiles and force plate data was highly consistent between runs. The increasing maximum ground force was correlated with the IMU data using a linear fit and gyroscope triggered acceleration component. Both techniques show promise (r2 > 0.5). This is of significant interest to athletes and coaches using IMUs rather than a long, instrumented running track

STEM educational engagement through coompetition, sport and wearable technology

Student engagement in Science, Technology, Engineering and Mathematics (STEM) activities are often perceived as solitary activities, resulting in a limited range of students that are intrinsically interested in it [1]. Historically STEM activities are often about “things rather than people” [2]. For those whom STEM is not perceived as intrinsically interesting, its lack of relevance to everyday life and social engagement means potential STEM students’ interests are focussed elsewhere. A previously reported education program [3] utilised a range of sports technologies and sport-based activities to engage students in sport and play activities that were more likely to be of interest and have a significant social context through working as a team and competition. These were then linked to classroom activities involving numeracy and engineering based. The vehicle for this were principally inertial sensors, which have emerged in recent decades as a viable alternative for the quantification of human movement at the elite level [4] as well as emerging as popular consumer electronics [5] through wearable technologies (an important hook for children). Recent studies of the program (STEMfit) [6] measured its efficacy for educational engagement and improving education outcomes [3]. These investigations garnered international interest for the potential to undertake cross cultural activities and exchange (even in a pandemic). Typically, in this program, physical activities are combined with classroom-based analysis using time series data developed from the STEMfit program and collected using a single body worn inertial sensor (Fig. 1). Here we introduce the ISEA STEMfit International cup, an ISEA Education co-sponsored program that was supported by global expertise in inertial sensors from the wider ISEA community. Furthermore, it supports interest in translational outcomes to foster the education of children as a pathway into STEM careers, in particular in Sports Engineering. Figure 1 shows a sample activity, jumping together with associated time series data collection and visual representation of the vertical axis. The analysis can be varied and scaled, depending on student capabilities e.g. early primary school students may count how many jumps they did in 10 seconds. In the developed competition and through partner schools of the co-authors, student teams from around the globe competed in a series of physical Olympic style athletic events and by using a range of sports technologies, collected data for a STEM analysis project. Student teams were judged by an international panel comprising of a senior sports engineer, an inertial sensor manufacturer, and an elite sports athlete/administrator. Students made a video presentation of their STEM analysis (in their own language and English) to share with other teams. References 1. Holmegaard, H. T., Madsen, L. M., & Ulriksen, L. (2014). To choose or not to choose science: Constructions of desirable identities among young people considering a STEM higher education programme. International Journal of Science Education, 36(2), 186-215. 2. Su, R., & Rounds, J. (2015). All STEM fields are not created equal: People and things interests explain gender disparities across STEM fields. Frontiers in psychology, 6, 189. https://www.frontiersin.org/articles/10.3389/fpsyg.2015.00189/full 3. Lee, J., Willis, C., Parker, J., Wheeler, K., & James, D. (2020). Engaging the disengaged: A literature driven, retrospective reflection, of a successful student centric STEM intervention. Australasian Association for Engineering Education Annual Conference 2020 4. Ohgi, Y. (2002, June). Microcomputer-based acceleration sensor device for sports biomechanics-stroke evaluation by using swimmer's wrist acceleration. In SENSORS, 2002 IEEE (Vol. 1, pp. 699-704). IEEE. 5. James, D. A., & Petrone, N. (2016). Sensors and wearable technologies in Sport: Technologies, trends and approaches for implementation (pp. 1-49). Berlin, Germany: Springer. 6. James, D. A., Parker, J., Willis, C., & Lee, J. (2020). STEMfit: Student Centric Innovation to Improve STEM Educational Engagement Using Physical Activity, Wearable Technologies and Lean Methodologies. In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 49, No. 1, p. 33)