A virtual reality input device for sports-related rehabilitation

Abstract. This work entails the hardware design, manufacturing and implementation of a VR controller device tailored for people with specific sports-related injuries. The target case of this thesis is the tennis elbow injury, where the designed controller helps them interface easily to the VR environment that is designed for their therapy. The sensors used are carefully selected in order to adequately capture the therapy exercise movements related to this kind of injury. For example, the use FSRs (Force Sensitive Resistors) that are put on the surface of a test object helps to detect a grasp during the exercise. The hardware design and manufacturing was done for a VR controller device that would give the desired performance, using Arduino IDE for its software development. In addition to this, the design of the VR environment allowed for an immersive VR experience for the rehabilitation. An experiment was carried out with eight participants, where they were asked to perform two exercises that involve grasping the test object. A series of questions were asked to them as part of the experimental evaluation. The results showed positive indications about the participants’ experience

Deep Learning Based Abnormal Gait Classification System Study with Heterogeneous Sensor Network

Gait is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion site and has been demonstrated to play a guiding role in clinical research such as medical diagnosis and disease prevention. In order to promote the research of automatic gait pattern recognition, this paper introduces the research status of abnormal gait recognition and systems analysis of the common gait recognition technologies. Based on this, two gait information extraction methods, sensor-based and vision-based, are studied, including wearable system design and deep neural network-based algorithm design. In the sensor-based study, we proposed a lower limb data acquisition system. The experiment was designed to collect acceleration signals and sEMG signals under normal and pathological gaits. Specifically, wearable hardware-based on MSP430 and upper computer software based on Labview is designed. The hardware system consists of EMG foot ring, high-precision IMU and pressure-sensitive intelligent insole. Data of 15 healthy persons and 15 hemiplegic patients during walking were collected. The classification of gait was carried out based on sEMG and the average accuracy rate can reach 92.8% for CNN. For IMU signals five kinds of abnormal gait are trained based on three models: BPNN, LSTM, and CNN. The experimental results show that the system combined with the neural network can classify different pathological gaits well, and the average accuracy rate of the six-classifications task can reach 93%. In vision-based research, by using human keypoint detection technology, we obtain the precise location of the key points through the fusion of thermal mapping and offset, thus extracts the space-time information of the key points. However, the results show that even the state-of-the-art is not good enough for replacing IMU in gait analysis and classification. The good news is the rhythm wave can be observed within 2 m, which proves that the temporal and spatial information of the key points extracted is highly correlated with the acceleration information collected by IMU, which paved the way for the visual-based abnormal gait classification algorithm.步态指人走路时表现出来的姿态，是人体重要生物特征之一。异常步态多与病变部位有关，作为反映人体健康状况和行为能力的重要特征，其被论证在医疗诊断、疾病预防等临床研究中具有指导作用。为了促进步态模式自动识别的研究，本文介绍了异常步态识别的研究现状，系统地分析了常见步态识别技术以及算法，以此为基础研究了基于传感器与基于视觉两种步态信息提取方法，内容包括可穿戴系统设计与基于深度神经网络的算法设计。 在基于传感器的研究中，本工作开发了下肢步态信息采集系统，并利用该信息采集系统设计实验，采集正常与不同病理步态下的加速度信号与肌电信号，搭建深度神经网络完成分类任务。具体的，在系统搭建部分设计了基于MSP430的可穿戴硬件设备以及基于Labview的上位机软件，该硬件系统由肌电脚环，高精度IMU以及压感智能鞋垫组成，该上位机软件接收、解包蓝牙数据并计算出步频步长等常用步态参数。 在基于运动信号与基于表面肌电的研究中，采集了15名健康人与15名偏瘫病人的步态数据，并针对表面肌电信号训练卷积神经网络进行帕金森步态的识别与分类，平均准确率可达92.8%。针对运动信号训练了反向传播神经网络，LSTM以及卷积神经网络三种模型进行五种异常步态的分类任务。实验结果表明，本工作中步态信息采集系统结合神经网络模型，可以很好地对不同病理步态进行分类，六分类平均正确率可达93%。 在基于视觉的研究中，本文利用人体关键点检测技术，首先检测出图片中的一个或多个人，接着对边界框做图像分割，接着采用全卷积resnet对每一个边界框中的人物的主要关节点做热力图并分析偏移量，最后通过热力图与偏移的融合得到关键点的精确定位。通过该算法提取了不同步态下姿态关键点时空信息，为基于视觉的步态分析系统提供了基础条件。但实验结果表明目前最高准确率的人体关键点检测算法不足以替代IMU实现步态分析与分类。但在2m之内可以观察到节律信息，证明了所提取的关键点时空信息与IMU采集的加速度信息呈现较高相关度，为基于视觉的异常步态分类算法铺平了道路

A Biomechanical and Physiological Signal Monitoring System for Four Degrees of Upper Limb Movement

A lack of adherence to prescribed physical therapy regimens in improper healing results in poor outcomes for those affected by musculoskeletal disorders (MSDs) of the upper limb. Societal and psychological barriers to proper adherence can be addressed through the system presented in this work consisting of the following components: an ambulatory biosignal acquisition sleeve, an electromyography (EMG) based motion repetition detection algorithm, and the design of a compatible capacitive EMG acquisition module. The biosignal acquisition sleeve was untethered, unobtrusive to motion, contained only modular components, and collected biomechanical and physiological sensor data to form full motion profiles of the following four degrees of freedom: elbow flexion—extension, forearm pronation—supination, wrist flexion—extension, and ulnar--radial deviation. The piloted sleeve simultaneously collected data from four inertial sensors, two electromyography (EMG) sensors and a flex-bend sensor. A visualization application was developed to present the information in a manner meaningful to the user. As well, an EMG based motion repetition detector was developed for use within the system. It was validated using an existing database of 23 subjects with varying musculoskeletal health, achieving a success rate of 95.43%. This algorithm was modified for use with the sleeve, resulting in a 95% success rate. An electrode and analog front end module was proposed, relying on unique material structures and low-noise, precision sensing techniques. The system prototype presented a resource-conscious tool for multi-modality tracking of elbow, forearm, and wrist motion, which could eventually be integrated into upper limb MSD rehabilitation

Non-linear actuators and simulation tools for rehabilitation devices

Mención Internacional en el título de doctorRehabilitation robotics is a field of research that investigates the applications of robotics in motor function therapy for recovering the motor control and motor capability. In general, this type of rehabilitation has been found effective in therapy for persons suffering motor disorders, especially due to stroke or spinal cord injuries. This type of devices generally are well tolerated by the patients also being a motivation in rehabilitation therapy. In the last years the rehabilitation robotics has become more popular, capturing the attention at various research centers. They focused on the development more effective devices in rehabilitation therapy, with a higher acceptance factor of patients tacking into account: the financial cost, weight and comfort of the device. Among the rehabilitation devices, an important category is represented by the rehabilitation exoskeletons, which in addition to the human skeletons help to protect and support the external human body. This became more popular between the rehabilitation devices due to the easily adapting with the dynamics of human body, possibility to use them such as wearable devices and low weight and dimensions which permit easy transportation. Nowadays, in the development of any robotic device the simulation tools play an important role due to their capacity to analyse the expected performance of the system designed prior to manufacture. In the development of the rehabilitation devices, the biomechanical software which is capable to simulate the behaviour interaction between the human body and the robotics devices, play an important role. This helps to choose suitable actuators for the rehabilitation device, to evaluate possible mechanical designs, and to analyse the necessary controls algorithms before being tested in real systems. This thesis presents a research proposing an alternative solution for the current systems of actuation on the exoskeletons for robotic rehabilitation. The proposed solution, has a direct impact, improving issues like device weight, noise, fabrication costs, size an patient comfort. In order to reach the desired results, a biomechanical software based on Biomechanics of Bodies (BoB) simulator where the behaviour of the human body and the rehabilitation device with his actuators can be analysed, was developed. In the context of the main objective of this research, a series of actuators have been analysed, including solutions between the non-linear actuation systems. Between these systems, two solutions have been analysed in detail: ultrasonic motors and Shape Memory Alloy material. Due to the force - weight characteristics of each device (in simulation with the human body), the Shape Memory Alloy material was chosen as principal actuator candidate for rehabilitation devices. The proposed control algorithm for the actuators based on Shape Memory Alloy, was tested over various configurations of actuators design and analysed in terms of energy eficiency, cooling deformation and movement. For the bioinspirated movements, such as the muscular group's biceps-triceps, a control algorithm capable to control two Shape Memory Alloy based actuators in antagonistic movement, has been developed. A segmented exoskeleton based on Shape Memory Alloy actuators for the upper limb evaluation and rehabilitation therapy was proposed to demosntrate the eligibility of the actuation system. This is divided in individual rehabilitation devices for the shoulder, elbow and wrist. The results of this research was tested and validated in the real elbow exoskeleton with two degrees of freedom developed during this thesis.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Eduardo Rocón de Lima.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Martin Stoele

Incorporating Modular Arrangement of Predetermined Time Standard with a Wearable Sensing Glove

“Performance” – a common watchword in the present age, and that which is optimized through the most functional methodology of investigating the work procedure. This encompassed the auditing, updating of the tasks, while at the same time, applied automation and mechanization. The Modular Arrangement of Predetermined Time Standard (MODAPTS) is a useful application of a work measurement technique that allow a greater variety of work for manufacturing, engineering, and administrative service activities to be measured quickly with ease and accuracy. The MODAPTS, however, made it extremely difficult for engineers to use because it required an ample amount of time to analyze and code the raw data. A new design was proposed to help resolve the conventional system\u27s inadequacy because in MODAPTS, each task cycle of a minute required about 2 hours to calculate and document, and also, the judgment of the analysts varied for the same task. This study aimed to reduce the time taken for the traditional MODAPTS documentation usually took and produced unified results by integrating MODAPTS with a Sensing Wearable Glove while maintaining the same performance. The objective was to introduce an easy, cost-effective solution, and to compare the accuracy of coding between manual and automated calculated MODAPTS while maintaining consistent performance. This study discusses the glove and accompanying software design that detected movements using flex sensors, gyroscopes, microcontrollers, and pressure sensors. These movements were translated into analog data used to create MODAPTS codes as an output, which then sent the data wirelessly using the Bluetooth module. The device designed in this study is capable of sensing gestures for various operations, and the traditional method was compared to the proposed method. This was in turn, validated using the two-way ANOVA analysis. It was observed that the sensor-based glove provided efficient and reliable results, just like the traditional method results while maintaining the same performance

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Wearable pressure sensing for intelligent gesture recognition

The development of wearable sensors has become a major area of interest due to their wide range of promising applications, including health monitoring, human motion detection, human-machine interfaces, electronic skin and soft robotics. Particularly, pressure sensors have attracted considerable attention in wearable applications. However, traditional pressure sensing systems are using rigid sensors to detect the human motions. Lightweight and flexible pressure sensors are required to improve the comfortability of devices. Furthermore, in comparison with conventional sensing techniques without smart algorithm, machine learning-assisted wearable systems are capable of intelligently analysing data for classification or prediction purposes, making the system ‘smarter’ for more demanding tasks. Therefore, combining flexible pressure sensors and machine learning is a promising method to deal with human motion recognition. This thesis focuses on fabricating flexible pressure sensors and developing wearable applications to recognize human gestures. Firstly, a comprehensive literature review was conducted, including current state-of-the-art on pressure sensing techniques and machine learning algorithms. Secondly, a piezoelectric smart wristband was developed to distinguish finger typing movements. Three machine learning algorithms, K Nearest Neighbour (KNN), Decision Tree (DT) and Support Vector Machine (SVM), were used to classify the movement of different fingers. The SVM algorithm outperformed other classifiers with an overall accuracy of 98.67% and 100% when processing raw data and extracted features. Thirdly, a piezoresistive wristband was fabricated based on a flake-sphere composite configuration in which reduced graphene oxide fragments are doped with polystyrene spheres to achieve both high sensitivity and flexibility. The flexible wristband measured the pressure distribution around the wrist for accurate and comfortable hand gesture classification. The intelligent wristband was able to classify 12 hand gestures with 96.33% accuracy for five participants using a machine learning algorithm. Moreover, for demonstrating the practical applications of the proposed method, a realtime system was developed to control a robotic hand according to the classification results. Finally, this thesis also demonstrates an intelligent piezoresistive sensor to recognize different throat movements during pronunciation. The piezoresistive sensor was fabricated using two PolyDimethylsiloxane (PDMS) layers that were coated with silver nanowires and reduced graphene oxide films, where the microstructures were fabricated by the polystyrene spheres between the layers. The highly sensitive sensor was able to distinguish throat vibrations from five different spoken words with an accuracy of 96% using the artificial neural network algorithm