Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Generalisable FPCA-based Models for Predicting Peak Power in Vertical Jumping using Accelerometer Data

Peak power in the countermovement jump is correlated with various measures of sports performance and can be used to monitor athlete training. The gold standard method for determining peak power uses force platforms, but they are unsuitable for field-based testing favoured by practitioners. Alternatives include predicting peak power from jump flight times, or using Newtonian methods based on body-worn inertial sensor data, but so far neither has yielded sufficiently accurate estimates. This thesis aims to develop a generalisable model for predicting peak power based on Functional Principal Component Analysis applied to body-worn accelerometer data. Data was collected from 69 male and female adults, engaged in sports at recreational, club or national levels. They performed up to 16 countermovement jumps each, with and without arm swing, 696 jumps in total. Peak power criterion measures were obtained from force platforms, and characteristic features from accelerometer data were extracted from four sensors attached to the lower back, upper back and both shanks. The best machine learning algorithm, jump type and sensor anatomical location were determined in this context. The investigation considered signal representation (resultant, triaxial or a suitable transform), preprocessing (smoothing, time window and curve registration), feature selection and data augmentation (signal rotations and SMOTER). A novel procedure optimised the model parameters based on Particle Swarm applied to a surrogate Gaussian Process model. Model selection and evaluation were based on nested cross validation (Monte Carlo design). The final optimal model had an RMSE of 2.5 W·kg-1, which compares favourably to earlier research (4.9 ± 1.7 W·kg-1 for flight-time formulae and 10.7 ± 6.3 W·kg-1 for Newtonian sensor-based methods). Whilst this is not yet sufficiently accurate for applied practice, this thesis has developed and comprehensively evaluated new techniques, which will be valuable to future biomechanical applications

Proceedings, MSVSCC 2019

Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019. The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference. Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference. Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere

Deep Learning Based Malware Classification Using Deep Residual Network

The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB

Enhancing Biomechanical Function through Development and Testing of Assistive Devices for Shoulder Impairment and Total Limb Amputation

Assistive devices serve as a potential for restoring sensorimotor function to impaired individuals. My research focuses on two assistive devices: a passive shoulder exoskeleton and a muscle-driven endoprosthesis (MDE). Previous passive shoulder exoskeletons have focused on testing during static loading conditions in the shoulder. However, activities of daily living are based on dynamic tasks. My research for passive shoulder exoskeletons analyzes the effect that a continuous passive assistance has on shoulder biomechanics. In my research I showed that passive assistance decreases the muscular activation in muscles responsible for positive shoulder exoskeleton. An MDE has the potential to have accurate and precise control of movement as well as restore a sense of proprioception to the user. Such a transformative and invasive device has never previously been tested. Therefore, my research focused on analyzing fundamental principles of the MDE in an in-vivo rabbit model. The two concepts I tested in my research were the feasibility of implanting an orthopedic device underneath the skin at the distal end of a limb following amputation and the locomotor restorative capabilities of an artificial tendon used for muscle-device connection. In my work I proved the feasibility of implanting fully-footed rigid endoprostheses underneath the skin and isolated the primary factors for a successful surgery and recovery. In addition, my research showed that although artificial tendons have the potential to restore locomotor function, proper in-situ tendon lengths must be achieved for optimal movement. This research informed the design and testing of a fully jointed muscle-driven endoprosthesis prototype

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications

Determining jumping performance from a single body-worn accelerometer using machine learning

External peak power in the countermovement jump is frequently used to monitor athlete training. The gold standard method uses force platforms, but they are unsuitable for field-based testing. However, alternatives based on jump flight time or Newtonian methods applied to inertial sensor data have not been sufficiently accurate for athlete monitoring. Instead, we developed a machine learning model based on characteristic features (functional principal components) extracted from a single body-worn accelerometer. Data were collected from 69 male and female athletes at recreational, club or national levels, who performed 696 jumps in total. We considered vertical countermovement jumps (with and without arm swing),sensor anatomical locations, machine learning models and whether to use resultant or triaxial signals. Using a novel surrogate model optimisation procedure, we obtained the lowest errors with a support vector machine when using the resultant signal from a lower back sensor in jumps without arm swing. This model had a peak power RMSE of 2.3 W·kg-1 (5.1% of the mean), estimated using nested cross validation and supported by an independent holdout test (2.0 W·kg-1). This error is lower than in previous studies, although it is not yet sufficiently accurate for a field-based method. Our results demonstrate that functional data representations work well in machine learning by reducing model complexity in applications where signals are aligned in time. Our optimisation procedure also was shown to be robust can be used in wider applications with low-cost, noisy objective functions

A Biomechanical Analysis of Total and Unicompartmental Knee Arthroplasty Patients during Stair Negotiation Compared to Healthy Controls.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Computational Tools and Experimental Methods for the Development of Passive Prosthetic Feet

Modern prosthetic foot designs are incredibly diverse in comparison to what was o↵ered to amputees at the turn of the millennium. Powered ankles can supply natural levels of joint torque, whilst passive feet continue to optimise for kinematic goals. However, most passive feet still do not solve the issue of unhealthy loads, and an argument can be made that optimisation methods have neglected the less active and elderly amputee. This thesis creates a framework for a novel approach to prosthetic foot optimisation by focusing on the transitionary motor tasks of gait initiation and termination.An advanced FEA model has been created in ANSYS® using boundary con-ditions derived from an ISO testing standard that replicates stance phase loading. This model can output standard results found in the literature and goes beyond by parameterising the roll-over shape within the software using custom APDL code. Extensive contact exploration and an experimental study have ensured the robustness of the model. Subject force and kinematic data can be used for specific boundary conditions, which would allow for easy adaptation to the transitionary motor tasks.This FEA model has been used in the development of prosthetic experiment tool, which can exchange helical springs to assess e↵ects of small changes in sti↵-ness on gait metrics. A rigorous design methodology was employed for all compo-nents, including parametric design studies, response surface optimisation, and ISO level calculations. The design has been manufactured into a working prototype and is ready for clinical trials to determine its efficacy.The conclusion of this framework is in the development of an experimental method to collect subject data for use in the models. A pilot study uncovered reliable protocols, which were then verified with ANOVA statistics. Proportional ratios were defined as additions to metric peak analyses already found in the liter-ature. These tools are ready for deployment in full clinical trials with amputees, so that a new prosthetic optimisation pathway can be discovered for the benefit of less active or elderly amputees