Pre-clinical validation and risk management of autonomous tumor prosthesis using FMEA approach

Since prostheses are biomedical devices implanted directly on the patient's body, they carry a higher risk compared to other engineering products. In the development process, it is a critical issue to identify potential errors and malfunctions that may arise during the clinical use of prostheses and to take precautions against them. Autonomous tumor prostheses have a higher risk than any other prosthesis due to its extension capacity of approximately 100 mm, having a large battery in its structure and performing non-clinical extension without physician control. In this study, the risk analysis of the autonomous tumor prosthesis previously developed by the authors was performed using the failure mode and effects analysis (FMEA) method. In order to determine potential failure risks, a literature review was performed on clinical errors of tumor prostheses. In addition, malfunctions caused by each component of the prosthesis have been identified. Risk Priority Number (RPN) values are calculated for each risk determined. The design of the prosthesis was changed by taking the necessary precautions for the risks with high RPN values. After taking the necessary precautions, the RPN values of the risks that the prosthesis still carries have been recalculated and discussed. As a result of the measures taken, the RPN values of all risks were reduced to below the threshold value that was generally accepted

Comparison of machine learning algorithms for EMG signal classification

The use of muscle activation signals in the control loop in biomechatronics systems is extremely important for effective and stable control. One of the methods used for this purpose is motion classification using electromyography (EMG) signals that reflect muscle activation. Classifying these signals with variable amplitude and frequency is a difficult process. On the other hand, EMG signal characteristics change over time depending on the person, task and duration. Various artificial intelligence-based methods are used for movement classification. One of these methods is machine learning. In this study, a total of 24 different models of 6 main machine learning algorithms were used for motion classification. With these models, 7 different wrist movements (rest, grip, flexion, extension, radial deviation, ulnar deviation, expanded palm) are classified. Test studies were carried out with 8 channels of EMG data taken from 4 subjects. Classification performances were compared in terms of classification accuracy and training time parameters. According to the simulation results, the Ensemble algorithm Bagged Trees model has been shown to have the highest classification performance with an average classification accuracy of 98.55%

Design of a 4-DOF grounded exoskeletal robot for shoulder and elbow rehabilitation

The number of cerebrovascular and neuromuscular diseases is increasing in parallel with the rising average age of the world’s population. Since the shoulder anatomy is complex, the number of rehabilitation robots for shoulder movements is limited. This paper presents the mechanical design, control, and testing of 4 degrees of freedom (DOF) grounded upper limb exoskeletal robot. It is capable of four different therapeutic exercises (passive, active assistive, isotonic, and isometric). During the mechanical design, the forces to be exposed to the robot were determined and after the design, the system was tested with strength analysis. Also, a low-cost electromyograph device was developed and integrated into the system to measure muscular activation for feedback and instantaneously muscle activation control for the physiotherapist during the therapy. The system can be used for rehabilitation on the shoulder and elbow.  A PID controller for position-controlled exercises was developed. The test results were presented in terms of simulation and the real system for passive exercise. According to the test results, the developed system can perform the passive exercise and can be used for other therapeutic exercises as well

Realize the Industrial Distribution Adjustment by means of Captial Operation——Case studies on Industry Integration of Xiamen Light Industry Group

香港中文大学教授郎咸平掀起的关于国有企业改革的大论战，像一场强烈的冲击波，席卷整个学术界和企业界，国有企业改革的出路，成了社会各界讨论的焦点。为实现未来经济持续健康发展，加快转变经济发展方式、完善社会主义市场经济体制，要继续毫不动摇地巩固和发展公有制经济，大力推进国有经济布局调整和国有企业战略性改组，深化国有企业改革，健全现代企业制度，优化国有经济布局和结构调整，增强国有经济的活力、控制力、影响力，提高经济整体素质和国际竞争力。 国有企业改革不是要搞活现有的全部国有企业，而是根据有所进有所退的原则，以市场和产业政策为导向，抓大放小，优化国有资产布局，择优扶强，优胜劣汰，采取兼并、重组、引入战...Professor Lang Xianping from the Chinese University of Hong Kong roused a big debate over the state-owned enterprises’ system reform, which turned out to be a strong shock wave that rapidly swept the whole academic circle and business world. The outlet of state-owned enterprises’ reform became a hot issue among all social walks. In order to realize the sustainable and healthy development of econom...学位：会计硕士院系专业：厦门大学与厦门国家会计学院会计硕士专业学位联合教育中心（MPACC）_会计硕士(MPACC)学号：X200515701

Design and control of a diagnosis and treatment aimed robotic platform for wrist and forearm rehabilitation: DIAGNOBOT

Therapeutic exercises play an important role in physical therapy and rehabilitation. The use of robots has been increasing day by day in the practice of therapeutic exercises. This study aims to design and control a novel robotic platform named DIAGNOBOT for diagnosis and treatment (therapeutic exercise). It has three 1-degree-of-freedom robotic manipulators and a single grasping force measurement unit. It is able to perform flexion-extension and ulnar-radial deviation movements for the wrist and pronation-supination movement for the forearm. The platform has a modular and compact structure and is capable of treating two patients concurrently. In order to control the DIAGNOBOT, an impedance control-based controller was developed for force control, which was required for the exercises, as well as a proportional-integral-derivative controller for position control. To model the resistive exercise, an angle-dependent impedance control method different from traditional methods has been proposed. Experiments were made on five healthy subjects and it has been demonstrated that the proposed robotic platform and its controller can perform therapeutic exercises