Arm Prosthetics Research Articles

Development of Customized Passive Arm Prosthetics by Integrating 3D Printing and Scanning Technology

Severe hand injuries resulting from accidents can lead to the traumatic loss of an upper limb. The provision of an assistive device, such as an arm prosthesis, can help patients regain their courage and motivation to engage with the public. This project aimed to integrate 3D scanning and printing technology to develop a customized passive arm prosthesis for individuals who have experienced shoulder disarticulation due to a traumatic accident. The fabrication of the arm prosthesis was specifically designed and tailored to meet each patient’s unique needs and preferences. The prosthesis design was created based on 3D scan files obtained from the patients. Finite element analysis was employed to analyze the design and determine the optimal materials to use, as well as to optimize the overall design. The finalized model was then converted into STL file format and G-code for the 3D printing process. To ensure ease of use, the entire arm prosthesis and a harness system were assembled, and multiple tests and fittings were conducted on the patient. Several prototypes of the arm prosthesis were fabricated to achieve the best fit for each individual patient.

Categorization and Evaluation Methods for Control Strategies of Bilateral Tasks in Arm Prosthetics

Categorization and Evaluation Methods for Control Strategies of Bilateral Tasks in Arm Prosthetics

Comparative evaluation of EMG signal features for myoelectric controlled human arm prosthetics

Myoelectric controlled human arm prosthetics have shown a promising performance with regards to the supplementation of the basic manipulation requirements for amputated people over recent years. However these assistive devices still have important restrictions in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyography (EMG) is used as the control signal to command such prosthetic devices to ensure the amputated people to compensate their fundamental movement patterns. The ability of extraction of clear and certain neural information from EMG signals is a critical issue in fine control of hand prosthesis movements. Various signal processing methods have been employed for feature extraction from EMG signals. In this study, it was aimed to comparatively evaluate the widely used time domain EMG signal features, i.e., integrated EMG (IEMG), root mean square (RMS), and waveform length (WL) in estimation of externally applied forces to human hands. Once the signal features were extracted, classification process was applied to predict the external forces using artificial neural networks (ANN). EMG signals were recorded during two types of muscle contraction: (i) isometric and isotonic, and (ii) anisotonic and anisometric contractions. Experiments were implemented by six healthy subjects from the muscles that are proximal to the upper body, i.e., biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically evaluated and, merits and shortcomings of the features were discussed. Findings of the study are expected to provide better insight regarding control structure of the EMG-based motion assistive devices.

웨어러블 로봇 인터페이스를 위한 회귀 기법 기반 손목 움직임 추정

Recently, the development of practical wearable robot interfaces has resulted in the emergence of wearable robots such as arm prosthetics or lower-limb exoskeletons. In this paper, we propose a novel method of wrist movement intention estimation based on a regression technique using electromyography of human bio-signals. In daily life, changes in user arm position changes cause decreases in performance by modulating EMG signals. Therefore, we propose an estimation method for robust wrist movement intention for arm position changes, combining several movement intention models based on the regression technique trained by different arm positions. In our experimental results, our method estimates wrist movement intention more accurately than previous methods.

Novel Method for Predicting Dexterous Individual Finger Movements by Imaging Muscle Activity Using a Wearable Ultrasonic System

Recently there have been major advances in the electro-mechanical design of upper extremity prosthetics. However, the development of control strategies for such prosthetics has lagged significantly behind. Conventional noninvasive myoelectric control strategies rely on the amplitude of electromyography (EMG) signals from flexor and extensor muscles in the forearm. Surface EMG has limited specificity for deep contiguous muscles because of cross talk and cannot reliably differentiate between individual digit and joint motions. We present a novel ultrasound imaging based control strategy for upper arm prosthetics that can overcome many of the limitations of myoelectric control. Real time ultrasound images of the forearm muscles were obtained using a wearable mechanically scanned single element ultrasound system, and analyzed to create maps of muscle activity based on changes in the ultrasound echogenicity of the muscle during contraction. Individual digit movements were associated with unique maps of activity. These maps were correlated with previously acquired training data to classify individual digit movements. Preliminary results using ten healthy volunteers demonstrated this approach could provide robust classification of individual finger movements with 98% accuracy (precision 96%-100% and recall 97%-100% for individual finger flexions). The change in ultrasound echogenicity was found to be proportional to the digit flexion speed (R(2)=0.9), and thus our proposed strategy provided a proportional signal that can be used for fine control. We anticipate that ultrasound imaging based control strategies could be a significant improvement over conventional myoelectric control of prosthetics.